Mature AI adopters report a 43% reduction in uncollectable balances.

The case for AI in finance

Mature AI adopters report a 25% increase in finished goods inventory turns.

The case for AI in supply chain

Organizations that have implemented AI have a 30% lower talent acquisition cost per employee.

Talent acquisition

50% of organizations used generative AI for information governance at the end of 2023.

Data governance

70% of organizations will use GenAI to predict customer needs for ad content and placement by end of 2024.

Digital commerce

83% of field services management systems are wholly or partially hosted on the cloud.

Field services

Featured findings

Benchmarking

Our patented custom survey method and toolset enable us to provide meaningful and accurate comparisons of performance tailored to each client’s priorities.

Performance data and benchmarking

Our patented custom survey method and toolset enable us to provide meaningful and accurate comparisons of performance tailored to each client’s priorities

42,000

Data from more than 42,000 business entities in more than 60 countries

6,000

More than 5,000 performance metrics, supported by business practices

Coverage for all geographics and 18 major industries

How does your client's performance measure up?

IBM participates in an internationally recognized open standards benchmarking partnership with APQC, a not-for-profit research organization.

Performance metrics

Data around the globe

Coverage

Peers

Benchmark Wizard empowers you to:

What is Benchmark Wizard?

Organizations that have used AI in a business-as-usual environment for 2 or more years have 22% lower total HR cost per employee compared to non-adopters.

The case for AI in HR

Organizations that have adopted AI in finance are 22% more likely to be top performers in finance function efficiency and quality metrics.

The case for AI in Finance

Leading HR organizations deliver 20% more learning days at a 10% lower cost per employee.

Talent development

Employing agile practices in finance is the first step in the journey to becoming a truly agile finance organization.

Agile in finance

Compared to other respondents, over two times more HR leaders invest in AI across the talent acquisition domain.

Organizations that have fully implemented cloud technology in the finance function lowered system cost by 32%.

Finance and Accounting

Organizations that have fully implemented cognitive capabilities for finance operations cost optimization have cut average invoice cost to $11.03.

Order to cash

Top performers are 75% more likely to review forecast to actual in real time.

Financial planning and analysis

The ability of AI users to respond rapidly to changing business conditions allows better scalability.

AI-Powered Manufacturing

Top performers are twice as likely to use AI-based cognitive automation.

IT innovators achieve better business outcomes.

Information Technology

Organizations that fully implemented cognitive capabilities for learning have higher learning budget per employee.

Talent

HR leaders are 59% more likely to modify staffing levels ahead of market demand.

HR organization

How does your performance measure up?

Data from 42,000 organizations across 60 countries

More than 1,000 performance metrics, supported by business practices

Coverage for all geographies and 18 major industries

Numerous peer group types for comparisons

Our open standards benchmarking database

Customer service

<h3>Customer service is the proving ground for generative AI</h3>Customer service is the tip of the spear for generative AI—the function that pierces through the unknown to deliver unprecedented business value. In fact, as this new technology disrupts how work is done across the enterprise, customer service has become the C-suite’s top priority for adoption. That’s no surprise, as it’s the next logical step for companies that have already been using traditional artificial intelligence in customer service for years.From chatting with customers to creating targeted content to optimizing call center and contact center performance, generative AI is taking the transformation of customer service to the next level. By enabling dynamic and personalized experiences for both customers and human agents, it has the potential to supercharge traditional AI, spurring a seismic shift in productivity and effectiveness. Making the right bets can pay off exponentially—but where companies should invest depends on where they’re starting from.<blockquote>Every leader we surveyed says their organization plans to use generative AI in customer service—and 67% say they’ve already begun.<o:p></o:p></blockquote> So, where do business leaders at different stages in the AI journey see the most promise? To answer this question, the IBM Institute for Business Value (IBM IBV) surveyed nearly 1,500 customer service managers, directors, and executives from organizations that have used conversational AI for at least 12 months across 34 countries and all major industries. We asked how their organizations are using generative AI in customer service today, which use cases show the greatest potential, and where this technology is already delivering the most business value through automation and augmentation.Overall, customer service leaders agree that adopting generative AI is essential for their business. In fact, every single respondent says their organization plans to use generative AI in customer service—and 67% say they’ve already begun. More than half (54%) of these organizations have deployed generative AI in one to four customer service use cases.But not every organization plans to use generative AI the same way. Our data suggests the number of years an organization has used conversational AI, which is designed to understand and respond to customer queries in natural language, is a telling predictor of whether it will be an aggressive early adopter of generative AI. We see that organizations with the most experience using conversational AI in customer interactions have the confidence to be bold, implementing more sophisticated use cases. For instance, 89% of organizations that have used conversational AI in customer service for at least three years are already using generative AI to answer customer queries directly.<blockquote>65% of customer service leaders expect using generative AI with conversational AI to increase customer satisfaction.</blockquote> However, our research also reveals that using generative AI in conjunction with conversational AI can improve customer experience, support agents, and deliver significant business benefits, regardless of how much AI experience an organization has. Veterans do see best-in-class performance—but novices could gain the biggest edge over their peers. This means organizations at every maturity level have opportunities to provide better customer support, outpace their competitors, increase customer engagement—and deliver game-changing performance improvements. They just need to know the best next step to take.&nbsp;<h3>AI gets a nitro boost</h3>When conversational AI came on the scene, it helped companies improve on early chatbot experiences, which were driven by rule-based systems that delivered pre-defined responses. These early AI systems were used primarily to address common, easy-to-answer customer questions and had limited capabilities. Conversational AI improved on the chatbot experience by leveraging natural language processing (NLP) and machine learning algorithms to understand and respond to customer questions. When trained well, these AI models sound more like humans and less like machines. However, while these enhanced AI assistants can successfully execute much more complex interactions, their capabilities eventually hit a wall. Generative AI offers the next evolution of AI technology. Using natural language generation and customer data, it answers customer questions with more fluent, contextually relevant responses. It can also tap into a customer’s interaction history to tailor responses and deliver more personalized responses. These capabilities let customers chat with generative AI assistants in the same way they would engage a human agent.<blockquote>More than 40% of organizations are using generative AI to create test cases for training conversational AI.</blockquote> What’s more, the applications of generative AI go far beyond direct interactions with customers. This technology can enhance the customer service function more generally by supporting customer service agent training, increasing personalization, translating content, and predicting future customer behavior to increase agent productivity. It can also support customer-facing conversational AI by generating test cases and dialogue, as well as reviewing interactions to identify opportunities for improvement.<h3>Experience matters—but generative AI is a tide that raises all boats</h3>Our research shows that, on average, all organizations using generative AI in customer service report higher levels of customer satisfaction than those that don’t. However, organizations that have been using conversational AI longer report the best business outcomes overall.A bold step forward: Veterans are experimenting with more customer-facing use cases than novices.<o:p></o:p><o:p></o:p><figure class="image"><img src="/thought-leadership/institute-business-value/uploads/en/9eoine_V1_F5q554h5_S_3xt_Rn_O_d83d3aa149.jpg" alt="Chart illustrating that veterans are experimenting with more customer-facing use cases than novices"></figure> Download the report to learn how organizations are leveraging generative AI in customer service today, how it can impact key performance metrics—including cost per contact and ROI—and which approaches work best for novices and veterans. Then explore an action guide that outlines how organizations in each group can optimize gen AI to get the most value.<o:p></o:p>&nbsp;

Power up conversational AI to gain a competitive edge.

Customer service and the generative AI advantage

The COVID-19 pandemic has rocketed the adoption of <a href="https://www.ibm.com/cloud/watson-assistant" target="_blank">virtual agent technology (VAT)</a> into hyperdrive. For emergency situations, when speed and simplicity are mission critical, out-of-the-box implementations can go live in a matter of hours. However, companies that thoughtfully design, develop, and deploy VAT have the potential to unlock even more value for their organizations and their users, be they customers, employees, or human agents.

Until now, actual data about VAT performance has been limited to individual case studies, making it difficult for organizations to derive meaningful comparisons. So the IBM Institute for Business Value (IBV), in cooperation with Oxford Economics, surveyed 1,005 respondents across 12 industries and 33 countries to gather performance data from organizations employing VAT daily, in live situations, for periods from six months to just over four years.

<blockquote>
99% of respondents report an increase in customer satisfaction as a result of using virtual agent technology.
</blockquote>

As we embarked on this research, we knew organizations needed answers to four key questions:

<ul>
	<li>How efficient is AI-enabled VAT?</li>
	<li>What are the key attributes and practices that drive value from VAT?</li>
	<li>What is the impact of AI-enabled VAT on customers, end users, and human agents?</li>
	<li>What impact does VAT have on organizations&rsquo; financial performance?&nbsp;</li>
</ul>

Our analysis revealed that VAT is having a positive impact on nearly every measure, including customer satisfaction, employee satisfaction, and revenue, and an exceedingly high proportion of respondents (96 percent) have achieved, or expect to achieve, their anticipated ROI.

<blockquote>
96% of respondents have exceeded, achieved, or expect to achieve their anticipated return on investment for their virtual agent technology implementation.
</blockquote>

Lessons from the leaders

Our analysis identified a small group of virtual agent technology pioneers we have identified as leaders. Each leader demonstrates three critical attributes that decision makers directly influence:

<ul>
	<li>Early adopter of VAT</li>
	<li>Large proportion of inbound contacts are within the scope of their VAT</li>
	<li>High degree of integration exists between their VAT and backend systems.</li>
</ul>

Comprising 10 percent of our sample, leaders span all industries and regions surveyed, as well as all three types of VAT implementation, in almost equal proportions. Leaders also report outperforming their competition on revenue and profitability for the last three years. And they outperform other respondents on core metrics used to measure the efficiency of VAT.

Virtual agent technology leaders outperform their peers in three key areas 

<img alt="Virtual agent technology leaders outperform their peers in three key areas " height="423" src="https://s3.us-south.cloud-object-storage.appdomain.cloud/ibv/01aef4cf7bc244e7bfc859b85930332b.png" width="720" />

Learn what sets these leaders apart&mdash;and how virtual agent technology can help your organization realize customer, agent, and financial benefits.

Improve customer service and boost financial results with AI-enabled systems.

The value of virtual agent technology

The COVID&ndash;19 pandemic has intensified industry trends and external pressures that were already impacting electronics service delivery operations. Seemingly overnight, organizations across the industry needed to not just respond, but respond with alacrity. Despite the need for electronics companies to reinvent the entire service delivery function virtually, consumers still had high expectations for quality.

Executives had to act quickly to offer plenty of contact options, rapid response times, and new modes of hardware repair or replacement.

<blockquote>
In changing the premise of service delivery, everything around it needed to evolve, as well.
</blockquote>

As a result, the service function has dramatically shifted, not only because service delivery has become more dynamic and virtual. In changing the premise on which service is delivered, everything around it needed to evolve, as well. Electronics organizations began by prioritizing cash flow and optimizing operating expense. But going forward, they need to consider how to create competitive advantage and resiliency.&nbsp;&nbsp;

It&rsquo;s true that a rapid response to a force majeure, such as COVID-19, combined with an amplified need for collaboration and increased cost pressures, can negatively impact an organization. But it doesn&rsquo;t have to. What if the force majeure is a catalyst to transitioning from old ways of working to a new, intelligent service approach?

To better understand the current and future after-sales support capabilities of electronics companies, the IBM Institute for Business Value (IBV) joined forces with Oxford Economics to survey 600 executives across 6 industry segments and 19 countries.

The Empowered: Driving service delivery success

In our survey, executives revealed plans for applying exponential technologies and shared how their current implementations enhance aspects of after-sales support performance. The research also studied how developing and applying insights from after-sales support data drives improvements throughout the organization. Three categories of after-sales support emerged: the Enabled, the Enacted, and the Empowered.

<blockquote>
94% of Empowered organizations drive their service delivery functions from cloud-enabled architectures.&nbsp; 
</blockquote>

While all three models deliver benefits, Empowered organizations&mdash;those fully embracing NextGen Service capabilities&mdash;are significantly more efficient, using far fewer resources to execute on far more support requests. On average, they earn higher customer retention, which often translates to higher profitability. As well, these advanced service organizations showed the highest percentage of cloud computing across locations and applications. In fact, 94% of Empowered organizations drive their service delivery functions from cloud-enabled architectures versus less than 80% of Enacted and Enabled companies.&nbsp;

Each service delivery model depicts a set of capabilities across four aspects of the after-sales support function:

<ul>
	<li>Primary objectives (customer experience, cost takeout, or growth)&nbsp;</li>
	<li>Governance and execution model (internal, external, or a combination)</li>
	<li>Extent to which collaboration is enabled across an ecosystem</li>
	<li>Organizational satisfaction with after-sales capabilities and performance.</li>
</ul>

The path to Empowered

Our research says electronics service organizations can &ldquo;have it all&rdquo;&mdash;or at least most of what they need to deliver effective, efficient, technically savvy service. The question, is how to get there from here?&nbsp;

A logical, four-step path starts with a guided re-evaluation of the enterprise&rsquo;s strategic principles. Second, executives should examine potential interaction improvements. The third step develops a new business architecture that embeds intelligent workflows&mdash;supported by exponential technologies&mdash;for service delivery and other business functions.

It also requires orchestrating change based on real-time visibility of the enterprise&mdash;its people, technologies, and overall performance. The fourth and final step involves building a business case for transitioning to NextGen Service, as well as outlining a strategic workforce development plan.

Stepping up service delivery

4 steps to help electronics organizations transition to Next-Gen Service<img alt="Stepping up service delivery" src="https://s3.us-south.cloud-object-storage.appdomain.cloud/ibv/c7570114a9844f4587e81ecd5d53268a.svg" width="720" />

Read the full report to see how NextGen Service delivery can help electronics organizations boost operational efficiency, increase customer retention, and deliver a competitive advantage in a shifting business landscape.

NextGen Service delivery boosts operational efficiency and customer retention, which often translates to higher profitability.

Empowered electronics service and support

Frameworks

Information technology

In mergers and acquisitions (M&amp;A), value realization is typically top of mind. But cyber-risk is real. Considering data privacy regulations and mandatory breach disclosure laws, cyber risk exposure has the potential to significantly impact post-merger valuations. When assessing the value of a potential acquisition, acquiring organizations must factor in the cost of cyber risk as part of their deal strategy.

In 2016, a telecom provider based in the UK was heavily fined when a customer database it acquired earlier was hacked. In 2017, the price of Verizon&rsquo;s acquisition of Yahoo&rsquo;s internet business plunged USD 350 million after <a href="https://www.bankinfosecurity.com/%20yahoo-takes-350-million-hit-in-verizon-deal-a-9736" target="_blank">Yahoo disclosed three massive data breaches</a> compromising more than 1 billion customer accounts.

Short-term and long-term security risks

Highly sophisticated threat actors target M&amp;A activities because they offer the potential for short-term and long-term reward. With operations in transition, high-value data is often vulnerable. When publicly held companies are involved, the resulting media coverage can exacerbate the risk that threat actors will seize the opportunity to attack.

<blockquote>
More than one in three executives surveyed said they have experienced data breaches that can be attributed to M&amp;A activity during integration.
</blockquote>

Furthermore, the data of the company being acquired/ divested may not be the ultimate target. Instead, it may serve as an expedient way to break into the acquiring company. This is an entirely different category of advanced persistent threat: one that is carried over into the newly merged company by mistake. This tactic underscores the importance of cybersecurity oversight during M&amp;A activities.

Chief Information Security Officers (CISOs) and their teams are key to protecting the assets and brand reputation of acquirers. They should play a significant advisory role in all activities of the M&amp;A lifecycle. All too often, CISOs learn about, or are asked to engage in, acquisitions late in the deal lifecycle. This exposes organizations to significant risk, for example, when a breach occurs immediately post-acquisition.

<blockquote>
More than half of companies wait until due diligence is completed to perform cybersecurity assessments. &nbsp;
</blockquote>

In Q4 2019, the IBM Institute for Business Value (IBV) surveyed 720 executives responsible for the M&amp;A functions at acquirer organizations. More than one in three said they have experienced data breaches that can be attributed to M&amp;A activity during integration. Almost one in five experienced such breaches post-integration.

Get security experts involved early

There are several reasons why companies delay or disregard engaging security experts during M&amp;A. In some cases, it&rsquo;s attributable to inexperience with the complex M&amp;A lifecycle. In others, there may be a desire to limit the number of people with knowledge of an impending merger. Restricting &ldquo;line of sight&rdquo; to a potential merger is understandable during the pre-acquisition phase; however, excluding risk and security domain experts is problematic, as security and compliance issues represent potential liabilities.

The solution isn&rsquo;t cut and dry&mdash;but organizations have options. Learn about the proactive measures security teams can take to reduce cybersecurity risk throughout the M&amp;A lifecycle.&nbsp;

Taking proactive measures early on can help organizations avoid the costs that come with a cybersecurity breach.

Assessing cyber risk in M&A 

Faced with an onslaught of cyberthreats, today&rsquo;s security teams must contend with a new operational reality.

Pandemic-accelerated digital transformation has led to more remote workers. More cloud users. More cloud providers. Essential systems integration across an ecosystem of partners. An astounding number of edge devices passing Internet of Things (IoT) data to the cloud. All interconnected and interdependent, delivering the speed, scale, and connectivity expected in our daily digital lives. And all radically expanding an organization&rsquo;s attack surface for cyber criminals to exploit.

More threat vectors have emerged&mdash;from an unwitting supplier to a disgruntled employee. Hackers disrupt business and consumer services with phishing, exfiltration of data, denial of service, malware, and ransomware attacks. Some threat actors are even applying adversarial AI to unleash more efficient strikes. <a href="https://www.ibm.com/security/data-breach/threat-intelligence/" target="_blank">Cyberattacks are increasingly sophisticated</a>&mdash;and they are costly. For example, the <a href="https://www.ibm.com/security/data-breach/" target="_blank">average cost of a data breach</a> reached an all-time high of $4.24 million in 2021.

<blockquote>
Adopting AI-powered automation can help cybersecurity teams drive improved insights, productivity, and economies of scale.
</blockquote>

The net result is a stark realization for many executives: modern digital operations are driving value but also creating new vulnerabilities. Cybersecurity professionals must adopt a more preventative, proactive posture for protecting core business operations.

To position their teams for success, they need to bring together disparate data sets and security tools while addressing the skills gaps in their limited cybersecurity resources. Our research suggests that leading organizations are pursuing a forward-looking approach to threat management, adopting AI-powered automation to drive improved insights, productivity, and economies of scale.

To understand how AI is being used to support security operations and to quantify its impact on cybersecurity performance, the IBM Institute for Business Value (IBV) partnered with APQC (American Productivity and Quality Center) to survey 1,000 executives with overall responsibility for their organization&rsquo;s IT and operational technology (OT) cybersecurity systems. Respondents described their initiatives to use AI technology to support security operations and manage protection, prevention, detection, and response processes.

AI for cybersecurity is gaining traction

Overall, the majority of executives&mdash;globally and across industries&mdash;are adopting or are considering adoption of AI as a security tool. 64% of respondents have implemented AI for security capabilities and 29% are evaluating implementation.

<blockquote>
Only 7% of respondents are not considering the use of AI for cybersecurity.
</blockquote>

We refer to the 64% who are currently piloting, implementing, operating, or optimizing security AI solutions as the &ldquo;AI Adopters.&rdquo; They report AI applications have delivered significant positive impacts on their security outcomes. These include the ability to triage Tier 1 threats more effectively, detect zero-day attacks and threats, and reduce false positives and noise that require human analyst inspection.

AI advantage: AI Adopters improve performance by using AI for critical capabilities

<img alt="AI Adopters improve performance by using AI for critical capabilities" height="496" src="https://s3.us-south.cloud-object-storage.appdomain.cloud/ibv/5_H3_EO_5uaq8o_Oe_E9fj2vz_T_5m_873f90f03d" width="1000" />

The AI Adopters are successfully pairing AI systems with human intelligence to extend their visibility across a rapidly expanding digital landscape of apps and endpoints. In fact, 35% rank endpoint discovery and asset management as one of their top AI use cases now, with plans to increase usage to almost 50% in 3 years.

Facing a talent shortage, organizations are also turning to artificial intelligence to boost productivity of their overstretched resources. AI plus automation can help teams better manage the sheer volume and speed of security threats. 34% of AI Adopters say threat detection is one of their top AI use cases today, helping them gain efficiency from real-time detection of abnormalities. They also rank automated detection and response and threat intelligence as important applications, with plans to increase their use of AI for these capabilities in the next 3 years.

AI plus automation offers opportunities

Top-performing AI Adopters illustrate the potential for AI to transform cyber defense operations. Their use of AI has helped reinforce network security by monitoring 95% of network communications and 90% of endpoint devices for malicious activity and vulnerabilities. They estimate that AI is helping them detect threats 30% faster. They are also significantly improving response times to incidents and the time to investigate. And their return on security investment (ROSI) has jumped 40% as well.

Read the report to explore more positive impacts AI-powered cybersecurity solutions are delivering inside the security operations centers of leading Adopters. And review the action guide for steps you can take to drive performance and relieve pressure on overextended security teams using AI for cybersecurity.

Leading AI Adopters are uniting technology and talent to boost visibility and productivity across security operations.

AI and automation for cybersecurity

While global travel and travel workforces have been reduced as a result of the COVID-19 crisis, threat activity against the aviation sector has not. A case in point is a March 2020 data breach disclosed by the San Francisco International Airport. Reportedly, <a href="https://www.infosecurity-magazine.com/news/san-francisco-airport-attack/">the attack</a> was perpetrated by Russia&rsquo;s state-sponsored hacking group Dragonfly. This group typically targets organizations in critical infrastructure sectors with the objectives of reconnaissance, lateral movement, and cyber espionage.

Sustaining and securing critical infrastructures &ndash; such as those shared by travel and transportation companies &ndash; have always been challenges. The addition of COVID-19-related concerns has strained companies&rsquo; security, resiliency, and continuity plans to their limits. While the industry will recover from COVID-19, it may never be immune to cyber attacks. Overcoming this global challenge requires adaptability and innovative security and risk management practices.

<img alt="" height="542" src="https://s3.us-south.cloud-object-storage.appdomain.cloud/ibv/c3807ae9fe544f2fa2cbf1149b36c74e.png" style="float:left" width="280" />The travel industry is an attractive target for malicious actors. The reliance on information technology (IT) to facilitate operations, the ubiquitous need for integration of third-party vendors, and the global scope and integration of the travel supply chain represent a large, diversified attack surface.

IIoT benefits&hellip; and risks 

Many travel providers rely on Industrial Internet of Things (IIoT) solutions to manage complex operations. Use of these IIoT platforms and services increases the potential for unauthorized access to proprietary data and critical systems that can disrupt physical assets.

Numerous travel companies also are dependent on legacy IT systems, some with critical software vulnerabilities. Because they are difficult to update, these systems are inherently vulnerable, yet firms connect IIoT devices to them for operational applications, including some used by travelers.

Whether executed by financially motivated cybercriminals or politically motivated nation-states, a successful attack on a segment of the travel industry can result in severe cascading effects that can influence aggregate travel demand and, thus, the entire global economy. As attack vectors multiply, and critical vulnerabilities are exploited in short order, risks grow exponentially &mdash; often rapidly and without precedent.

In addition, as ecosystems grow, companies become even more vulnerable. And continued innovation across the industry makes it likely the travel ecosystem will continue to expand and evolve. To prepare for the future, travel organizations should focus on improving their cyber resilience today.

The travel industry security landscape

To better understand what makes some organizations more secure and cyber resilient than others, the IBM Institute for Business Value, in cooperation with Oxford Economics, surveyed IT and operational technology leaders from travel organizations across the globe. Our findings confirm the rapid adoption of IIoT technologies in a wide variety of functional areas.

Many companies are applying these technologies in their supply chain and logistics processes &ndash; for fleet management, predictive maintenance, and location management. However, executives are apprehensive about the security of information flowing between their operational, corporate IT, and IIoT networks. Gateways and gateway-related connectivity represent almost half of the most vulnerable IIoT components reported by travel companies.

<img alt="Many travel companies continue to deploy IIoT technologies faster than they can secure them." height="61" src="https://s3.us-south.cloud-object-storage.appdomain.cloud/ibv/a72dac7475374fb49a764d28579da296.png" width="540" />

While travel providers might be aware of the risks, many continue to deploy IIoT technologies faster than they can secure them. We found that almost two thirds of surveyed executives say they have, at a minimum, the capabilities to provide new IIoT-enabled offerings and services, yet only half say they can do so in a secure manner.

Learning from security leaders

Using our survey data, we identified a group of companies we deem &ldquo;security leaders&rdquo; based on their IIoT cybersecurity budgets, known vulnerabilities addressed by security controls, and response and recovery times. What truly differentiates security leaders from the other companies from the survey is their cyber resilience: they are able to detect, respond to, and recover from IIoT-related incidents at least twice as fast as other companies.

<img alt="We recommend a two-phase approach to help improve IIoT cybersecurity postures and resilience." height="62" src="https://s3.us-south.cloud-object-storage.appdomain.cloud/ibv/6c4aed64a01341688129f883d2096a88.png" width="650" />

Using our study data and analysis of the security leaders, we identified ten security controls and AI-driven practices that can positively impact IIoT cybersecurity performance. Read the report for recommendations on&nbsp;how travel companies can implement them as part of a two-phase approach to improve their IIoT cybersecurity postures and resilience.

We found one third of cybersecurity incidents at travel companies are IIoT related. Read why some companies are more resilient than others.

IIoT cybersecurity for travel companies

Organizations have responded to the COVID-19 pandemic by accelerating digital business transformation, expanding cloud footprints, increasing their remote workforces, and integrating their supply chains. As a result, our research indicates the percentage of remote workers serviced by the security function increased by 41% between the end of 2019 and through 2020.

But moving communication, business, and personal interactions online has also significantly increased potential attack surfaces, resulting in a <a href="https://www.ibm.com/security/data-breach/threat-intelligence" target="_blank">dramatic surge in cybersecurity incidents</a> and exposed records. As workloads move to the cloud, threats move with them. Our research indicates that in 2020, upwards of 90% of cyber-related incidents originated in cloud environments.

Valuable yet vulnerable: Securing critical infrastructure

The very nature of critical infrastructure implies a dynamic relationship between trust and risk. As operations move online, both IT and operational technology (OT) networks are subject to compromise. The July 2021 <a href="https://www.theguardian.com/technology/2021/jul/06/kaseya-ransomware-attack-explained-russia-hackers" target="_blank">Kaseya ransomware attack</a>, for example, affected up to 2,000 organizations and carried ransom demands in excess of $70 million. Our reliance on IT and OT environments means mission-critical infrastructure is increasingly vulnerable to new threats.

Interconnected risk: IT and OT risks are complex and interdependent

<img alt="Interconnected risk: IT and OT risks are complex and interdependent" height="327" src="https://s3.us-south.cloud-object-storage.appdomain.cloud/ibv/1z_Xy2c_H0ysixnfu_Ia_Nv_Px_I9_B_2da3d2a49c" width="750" />Trust is the basis for collaboration and partnership. As these capabilities become essential to delivering value, how we think about trust is rapidly changing. While traditional approaches to cybersecurity relied on permissions and discrete network boundaries, today&rsquo;s networks are defined by dynamic services and diffuse boundaries. Today&rsquo;s digital platforms generate value by virtue of being interconnected, by sharing information across multiple parties.

<blockquote>
70% of organizations are unable to secure data that moves across multiple cloud and on-premises environments.
</blockquote>

Tensions may be inevitable. Many OT systems have traditionally relied on system isolation, yet the demand for insights from connected devices and smart systems makes such practices difficult to sustain. If anything, a lack of connectivity can render existing vulnerabilities more difficult to remediate.

Making matters worse, risks often cascade: a failure in one system often results in the failure of others. <a href="https://www.washingtonpost.com/politics/2021/07/08/cybersecurity-202-kaseya-attack-is-revolution-sophistication-ransomware-hackers/" target="_blank">Threat actors are becoming more sophisticated</a> in their ability to capitalize on shortcomings in IT and OT security controls. &nbsp;While the potential impacts are significant, such risks can be difficult to anticipate.

Setting the pace in zero trust security

To better understand how organizations are implementing zero trust security, the IBM Institute for Business Value (IBV) partnered with Oxford Economics to survey more than 1,000 operations and security executives from organizations in 15 industries across the globe. Our analysis reveals 23% of organizations&mdash;a group we refer to as &ldquo;zero trust pacesetters&rdquo;&mdash;are ahead of their peers in deploying zero trust capabilities across their IT and OT environments and in their interactions with ecosystem partners.

These organizations have fashioned their IT and security operations as a single estate. They are proficient in partnering internally and externally to manage cybersecurity risk. They have modernized their security operations related to interdependent governance, risk, and compliance frameworks. They apply cloud, AI-driven analytics, and automation extensively. And they recruit, develop, and retain skilled cybersecurity resources to enable zero trust capabilities across their digital estates.

<blockquote>
92% of organizations lack the ability to securely enable and extend new cloud-native capabilities to their internal and external partners.
</blockquote>

Most importantly, their security operations can adapt to the complexity of the current business environment&mdash;whether it&rsquo;s enabling a remote workforce; monitoring endpoints, applications, data, and network traffic; or analyzing the behaviors of employees, customers, and partners to identify emergent threats.

Read the full report to learn what sets zero trust pacesetters apart&mdash;and how your organization can create a zero trust roadmap that leads to greater cyber resilience.

Organizations with the most mature zero trust capabilities have reduced security expenditures, increased cyber resilience, and boosted talent retention rates.

Getting started with zero trust security

Climate change is the existential threat of our time, and businesses are under increasing pressure to minimize their environmental impact.&nbsp;Information technology (IT) is a double-edged sword in this fight. While IT empowers businesses to operate more efficiently and sustainably, IT energy consumption and related carbon footprint mean it is not impact-free.&nbsp;&nbsp;<figure class="image"><img src="/thought-leadership/institute-business-value/uploads/en/z_FNNF_Lfpz_B0048_DA_Dg3wg0fx_38905043f2.jpg"></figure>&nbsp;Generative AI is a game changer for more sustainable IT. How it changes the game, though, is up to every organization that uses it. &nbsp;<h3>Holistic approach to more sustainable IT fares best</h3>The good news: generative AI can help solve the very problem it may contribute to over time. It is creating new opportunities to transform IT and make it more sustainable.Organizations are at different maturity levels not only in terms of making their IT greener overall, but also using AI to make their technology operations more sustainable and environmentally friendly.Gen AI for greener IT operations: Organizations vary widely in their adoption<figure class="image"><img style="aspect-ratio:2909/3439;" src="/thought-leadership/institute-business-value/uploads/en/Fk_SC_6_Qex5ln_TR_Jts_Akw_Uh_J8c_3e14bc1336.png" width="2909" height="3439"></figure>&nbsp;<h3>Beyond gen AI: Where do more mature organizations stand out in sustainable IT?&nbsp;</h3><h4>Making IT more sustainable by design&nbsp;</h4>Mature organizations differentiate themselves by integrating sustainability assessments during the design and planning stages of IT projects. This proactive approach, adopted by only 23% of organizations, ensures that environmental considerations are embedded into the very fabric of IT systems. More than half (52%) of those who do report taking this approach had a significant positive impact on the overall sustainability of their IT operations, making it the most effective sustainability-related practice applied in IT application development and maintenance operations.&nbsp;<h4>Optimizing cloud and automation&nbsp;</h4><blockquote>A quarter of businesses are already leveraging hybrid cloud solutions to significantly boost the sustainability and energy efficiency of their IT operations. Nearly half (46%) report a substantial positive impact on their overall IT sustainability.&nbsp;</blockquote>Mature organizations, however, also are harnessing automation to dynamically adjust IT environments based on demand. Many organizations overprovision how much compute power is at the ready. They can reduce excess standby processing power through greater optimization and demand management, which should lower energy usage significantly without compromising performance (while also freeing up high-demand processing power for other purposes).&nbsp;<h3>A greener future for IT operations&nbsp;</h3>By adopting a holistic approach, businesses can navigate the more sustainable IT crossroads and emerge as leaders in a sustainable future. The need will only become more pressing as enterprises seek to transform themselves with the power of generative AI.&nbsp;To learn more about the different ways organizations are heading toward more environmentally responsible technology, and to learn how different industries and leaders are handling the decision points, download the report. We detail how a bank and a fast-moving consumer goods company—two very different organizations—are moving to more sustainable IT by design.&nbsp;<o:p></o:p>

Choosing a future of responsible computing: Today’s technology decisions are shaping tomorrow’s environmental impacts. What does getting it right look like?

IT sustainability at a crossroads

Generative AI offers significant potential in digital product engineering, but new research shows that only a few organizations are realizing its value.Based on our survey of hundreds of product and engineering leaders worldwide, organizations are struggling to scale generative AI in product development—whether it’s early idea generation, building, testing, or support. One in five haven’t even started experimenting with gen AI.And for those that have made progress, the technology hasn’t delivered as predicted.&nbsp;Just 27% report having achieved the functional value they initially anticipated, and only 23% say they have realized the financial returns they expected.&nbsp;Why the challenge? Our analysis reveals a compelling truth: the habits teams cultivate matter significantly more than the AI tools they use. The most successful digital product teams aren't just experimenting with generative AI—they're fundamentally transforming how they work to harness its power.&nbsp; &nbsp;<blockquote>Product team work habits—not just AI tools—determine gen AI success.</blockquote>&nbsp;The teams seeing strong results—55% return&nbsp;on their investment:<ul><li style="padding-top:15px;">Deliver in small steps. They release improvements bit by bit, learning and adjusting along the way—not waiting for one big launch.</li><li style="padding-top:10px;">Work as one multidisciplinary team. Designers, engineers, and analysts collaborate closely, reducing delays and handoffs.</li><li style="padding-top:10px;">Turn user data into action. They don’t just collect data—they use it to make real product decisions.</li><li style="padding-top:10px;">Keep standards flexible. They adjust their standards and ways of working as needed based on feedback and experiential learning.</li></ul>These practices help teams turn gen AI from an experiment into a regular part of making better products. The real breakthrough happens when teams shift their focus from merely using AI to focusing on measurable results and continuous learning—making gen AI part of how they work.Download the presentation of benchmark data to delve deeper into where organizations are today in their use of gen AI in digital product engineering, where they are seeing value, and how leaders work differently. Recommendations on best practices for optimizing gen AI ROI through high-performing teams are included.&nbsp;&nbsp;

Iterative, outcome-driven teams see greater ROI on generative AI in their digital product initiatives.

Generative AI in digital product engineering

The entire IT estate—not just the artificial intelligence portfolio—plays a role in building a more profitable and productive AI-powered enterprise.

Cut the cost of complexity with intelligent IT automation

Intelligent IT automation

Marketing and sales

Procurement

Product development

Program overview

Strategy

Globally, organizations spent <a href="https://www.jpmorgan.com/jpmpdf/1320746694177.pdf">USD 4.1 trillion</a> on mergers and acquisitions (M&amp;A) in 2018. A significant chunk of capital changed hands: 44 deals were each worth over <a href="https://www.jpmorgan.com/jpmpdf/1320746694177.pdf">USD 10 billion</a>.

The electronics industry is no stranger to M&amp;A. Recent examples include <a href="https://www.powermag.com/hitachi-acquires-abb-power-grids-business-in-11-billion-deal/">Hitachi&rsquo;s acquisition of ABB&rsquo;s Power Grids business</a> to create economies of scale. <a href="https://www.engadget.com/2019/12/02/apple-owns-intel-modem-business/">Apple purchased Intel&rsquo;s mobile modem business</a> to increase control of its supply chain. And <a href="https://techcrunch.com/2018/08/01/siemens-acquires-low-code-platform-mendix-for-700m/">Siemens purchased Mendix</a> to accelerate research and development for low code environments. Large acquisitions reshape the marketplace, and powerhouse deals indicate the industry&rsquo;s fundamental shifts and transformations.

M&amp;A deals are complex, time-consuming, and inherently risky. It takes electronics companies an average of 52 weeks to execute the M&amp;A process from strategy to integration. On average, they achieve about 58 percent of projected synergies, taking an additional 56 weeks to do so. Our respondents report taking 100 weeks post integration to realize a profit.

Using analytics and AI to advance the M&amp;A process

It&rsquo;s mission critical for buyers and sellers to know if their organizations can blend successfully. Until two organizations move beyond &ldquo;dating&rdquo; to merging, &ldquo;mismatches&rdquo; are a risk. How can organizations quantify and avoid them?

As we embarked on the 2019 Cross-Industry M&amp;A Benchmark Study, we knew that organizations were examining how advanced analytics and AI could answer these questions&mdash;and improve overall M&amp;A performance.

Less clear were:

<ul>
	<li>What dimensions of the M&amp;A functions (either on their own or as part of the larger corporate development structure) differentiate organizations and performance?</li>
	<li>To what extent are companies applying advanced analytics and AI throughout the M&amp;A lifecycle, and how are they applying them?</li>
	<li>To what extent have analytics and AI improved M&amp;A performance?</li>
</ul>

Time kills deals, says the old adage. But what makes some deals&mdash;and more importantly some organizations&mdash;successful?

Electronics companies buy for the same reasons as organizations in other industries: to expand market presence or geographic reach (see figure). But beyond those basics, most companies have a more nuanced and layered set of buying criteria. In fact, responses yield more than 70 unique combinations.

<img alt="" height="550" src="https://s3.us-south.cloud-object-storage.appdomain.cloud/ibv/e57bafaf1c954503bdeb3007f0f21ebe.png" width="500" />

Ten tenets for modern M&amp;A 

This report shows how the ten M&amp;A tenets can help you integrate automation, analytics, and AI into your M&amp;A processes. (Hint: Begin with the end in mind.) The desired outcome is a set of M&amp;A capabilities that are repeatable and scalable.

This approach to M&amp;A is not simply about better strategy or better integration approaches. Organizations need both. We found:

1. M&amp;A governance and execution approaches, in conjunction with workflow and organizational processes, drive corporate development models for modern M&amp;A.

2. Automation, analytics, and AI are critical to scoping and scaling the process. They help to repeat prior successes, reduce friction, and elevate a company&rsquo;s portfolio above any one transaction.

3. Not all AI-enabled processes are equal. If you&rsquo;re in the early stages of infusing intelligence into your processes, some areas contribute more to overall success and synergy than others.

&nbsp;

Our analysis discovered ten tenets where embedding analytics and automation into the M&A process can deliver results for electronics companies.

Stronger M&A strategies through AI-driven processes

Electronics AI M&A strategies

Not all innovation is created equal. In a world of increased volatility and continuous change, there&rsquo;s a real risk of &ldquo;innovation fatigue.&rdquo; When everyone claims to be innovating, it can begin to feel like no one is innovating. Innovation can become commoditized. And with new ideas and products flooding the marketplace on a daily basis, it&rsquo;s no longer enough to innovate more. To differentiate itself, your organization needs to innovate better.

The impetus for change is especially urgent in the wake of generative AI. Executives expect generative AI to have significant impact across the entire innovation lifecycle, from ideation, discovery, and evaluation to execution and commercialization&mdash;not to mention collaboration with partners and measurement of outcomes (see Figure 1). Business leaders don&rsquo;t just view generative AI as another tool in the innovation toolbox; they see it as fundamentally altering the nature of innovation in the modern enterprise.

Executives expect improved performance from generative AI at every step of the innovation lifecycle (percentage of executives expecting improvement from generative AI) 
<img alt="Improved performance from generative AI: ideation 80%; discovery 81%; evaluation 63%; execution 74%; commercialization 61%; collaboration with partners 77% and 70% measuring outcomes" height="1023" src="/thought-leadership/institute-business-value/uploads/en/yz_RBXIL_Qr888_PH_036ya_HM_7_Gw_58b67bb7d2" width="1500" />

With generative AI already poised to upset the innovation landscape, now is an ideal time for organizations to re-evaluate their approach to innovation.

Innovation has traditionally been closed&mdash;an internal process that uses organizational resources to maintain secrecy, surprise, and competitive advantage. But traditional &ldquo;closed&rdquo; innovation is no longer adequate. In the current ecosystem economy that runs on partnerships, the clear choice for growing one&rsquo;s business is open innovation.

<h2 class="ck-h2 ibv-ckeditor">Collaboration is key</h2>

Based on collaboration and co-creation, open innovation has become a prevailing aspiration, with 84% of executives saying open innovation is important for the growth of their business. Their instincts are right: the rate of revenue growth for open innovation leaders is 59% higher than that for other companies, according to research by the IBM Institute for Business Value (IBM IBV).

<blockquote>
The rate of revenue growth for open innovation leaders is 59% higher than that for other companies.
</blockquote>

Our most recent research reinforces this. In a typical large organization, as much as 10% of revenue stems from open innovation. If effectively adopted across all Fortune 500 companies in the US, that would translate into $1.8 trillion of revenue per year.

But a large share of this potential value remains untapped. Many companies haven&rsquo;t yet converted the opportunity into reality.

<blockquote>
Organizations that are more mature with regard to open innovation are 3.3 times more likely to outperform less mature organizations on revenue growth, and 2.7 times more likely to outperform them on profitability.
</blockquote>

<h2 class="ck-h2 ibv-ckeditor">Treat innovation as a team sport&mdash;or lose to those who do</h2>

To move from aspiration to realization in an era of exponential technology, organizations must determine what business value they can derive from innovation with ecosystem partners and what&rsquo;s required to facilitate it.

To this end, the IBM IBV and APQC jointly developed a maturity model that we used to analyze whether maturity on key open innovation capabilities results in a more efficient innovation process and improved business outcomes. We call it the Ecosystem Enabled Innovation Maturity Model (EEIMM), and we have tested it with more than 1,000 companies.

Our results are compelling: organizations that are more mature with regard to open innovation significantly outperform less mature organizations. For example, they are 3.3 times more likely to outperform less mature organizations on revenue growth, and 2.7 times more likely to outperform them on profitability.

Download the report to learn what open innovation maturity looks like, how it can drive business performance, and&mdash;most importantly&mdash;what it takes to get there.

<div class="btgrid">
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Click <a href="https://www.apqc.org/what-we-do/benchmarking/assessment-survey/open-innovation-maturity-assessment" rel="noopener noreferrer" target="_blank">here</a> to assess your organization&rsquo;s open innovation capabilities. You will be taken to APQC&rsquo;s website where you can complete the survey and receive a benchmark report that will include detailed views of your assessment results and comparisons to other organization&rsquo;s maturity. 
&nbsp;
</div>
</div>
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&nbsp;

In the current ecosystem economy that runs on partnerships, the clear choice for growing one’s business is open innovation.

Ecosystems and open innovation: Co-create or stagnate

Ecosystems and open innovation

The chemicals and petroleum industry has long used M&amp;A as a critical strategy. When examining the history of M&amp;A deals across industries, chemicals and petroleum represents 20 percent of the 50 largest deals of all time (see figure below). In 2019, chemicals and petroleum deals were valued at over <a href="https://www.prnewswire.com/news-releases/oil-and-gas-ma-deal-value-decreased-in-2019-driven-by-energy-transition-considerations-and-global-uncertainty-301003074.html">USD 550 billion.</a>

Pre-pandemic M&amp;A transactions reflect how chemicals and petroleum companies were responding to the disruptive global environment prior to COVID-19. Using AI-enabled strategies to address today&#39;s disruptions of global political and economic uncertainty&mdash;decreased demand for oil, lower levels of investment, and accelerating energy transition&mdash;can help these organizations re-establish progress.

Chemicals and petroleum industries represent a fifth of the largest 50 M&amp;A deals in history 

<img alt="" height="380" src="https://s3.us-south.cloud-object-storage.appdomain.cloud/ibv/8cc2f6c5caba415da131b2f41cd2c86a.png" width="700" />

To better understand strategies behind M&amp;A success, the IBM Institute for Business Value (IBV), in cooperation with Oxford Economics, surveyed leaders from 720 organizations across the chemicals and petroleum, electronics, healthcare, and life sciences industries. Respondents represented 18 countries and included 220 chemicals and petroleum executives. Each respondent holds overall responsibility for the M&amp;A process, from the definition of M&amp;A strategy to post-purchase integration.

Companies buy for the same reasons as organizations in other industries: to expand scope or scale. The Bayer/Monsanto deal is an example of scope expansion around crop sciences, while Saudi Aramco&rsquo;s acquisition of SABIC was scale driven to expand downstream operations in refining and petrochemicals. But beyond these basic reasons, most companies have a more nuanced and layered set of buying criteria.

In fact, survey responses yield more than 40 unique combinations of reasons behind their M&amp;A pursuits. Recent M&amp;A transactions reflect how chemicals and petroleum companies are responding to the current global environment (excluding the disruption caused by the COVID-19 pandemic). Each segment within the industry pursues different strategies in its M&amp;A:

In chemicals, the merger of <a href="https://www.rockymountainair.com/blog/linde-praxair-create-worlds-largest-industrial-gas-company/">Praxair/Linde AG</a> created the world&rsquo;s largest industrial gas company. Synergies were driven by scale benefits, cost savings, and efficiency improvements.

In upstream oil and gas, efficiency and scope have driven recent transactions. For example, <a href="https://www.houstonchronicle.com/business/energy/article/Oxy-completes-38B-acquisition-of-Anadarko-to-14291751.php">Occidental Petroleum&rsquo;s acquisition of Anadarko</a> created a combined company that is the largest player in the Permian Basin and one of the top producers in the Gulf of Mexico. The deal is expected to generate USD 3.5 billion per year in cost savings and capital spending cuts and allows Occidental to apply its Permian Basin expertise to Anadarko&rsquo;s Texas and Colorado oilfields. <a href="https://www.cnbc.com/2018/07/27/bp-buys-bhp-shale-assets-to-beef-up-us-business.html">&nbsp;BP bought BHP Billiton&rsquo;s US shale oil and gas assets</a> for USD 10.5 billion to increase its US onshore oil and gas resources by 57 percent.&nbsp; Investment in deep-water assets, such <a href="https://www.ey.com/en_gl/oil-gas/in-this-transformation-age--do-you-compete-or-collaborate">as ExxonMobil in the Guyana-Suriname Basin in South America and Shell in Brazil</a>, suggests these companies are positioning their portfolios to protect themselves from price volatility.

In midstream oil and gas, scale is the rationale behind <a href="https://www.ey.com/en_gl/oil-gas/in-this-transformation-age--do-you-compete-or-collaborate">the Energy Transfer Partners USD 60 billion merger with Energy Transfer Equity</a>. The combined entity created a more simplified ownership structure, improved its overall cost of capital, and allowed Newco to continue pursuing growth capital projects and strategic merger and acquisition transactions.

In downstream oil and gas, scale through better integration across the value chain drove <a href="https://www.fool.com/investing/2018/08/10/3-things-energy-transfer-equity-management-wants-y.aspx">Marathon Petroleum&rsquo;s acquisition of Andeavor for USD 35.6 billion</a>. The merger linked Andeavor&rsquo;s gathering and transportation infrastructure in West Texas to Marathon&rsquo;s United States Gulf Coast refinery to provide access to low-cost crude oil supply.

Each of these M&amp;A deals is complex, time-consuming, and inherently risky. Our survey respondents indicated that it takes 50 weeks (median) to execute the M&amp;A process from strategy to integration. After integration, the merged companies take an additional 80 weeks (median) to achieve about 60 percent of projected synergies. And it took 85 weeks post-integration for respondents to realize a profit.

It&rsquo;s mission-critical for buyers and sellers to know if their organizations can blend successfully. Until two companies move beyond &ldquo;dating&rdquo; to merging, &ldquo;mismatches&rdquo; are a risk.

Corporate development, and M&amp;A in particular, will always be a human-plus-machine partnership. The complexity of products/services, markets, and revenue models necessitates an executive focus on technology-enabled M&amp;A approaches and strong governance and execution. This report explores those details and concludes with a four-step Action Guide to help you build a modern M&amp;A capability that can drive effective, inorganic portfolio growth.

Chemicals and petroleum execs describe their M&A processes, use of analytics and artificial intelligence, and the financial performance of their M&A deals.

AI-assisted acquisitions 

Finance

For chief financial officers (CFOs) and finance professionals, the expectation to be guardians of stability and agents of transformation has elevated both what&rsquo;s at stake and opportunity. To tackle this paradox of responsibilities and drive value through the organization, finance executives need to leverage new approaches, new tools, new perspectives, new organizational constructs, and new skills&mdash;especially related to data and decision-making.

<blockquote>
Finance leaders need new process approaches that generate quantifiable results&mdash;and AI can fit the bill.
</blockquote>

Yet, finance leaders are ambiguous about how well their own finance departments fulfill their duties (see figure). In the IBV 2022 C-suite CFO Study, leaders state they are most effective at executing traditional finance tasks (transaction processing); still, two in five are not effective. Only 47% think they excel at measuring and managing performance&mdash;a task that has not seen improvement since 2013. And only 38% of respondents say they are effective in planning and executing strategy, a dramatic 25% drop over the same period. Control and risk management effectiveness has declined even more: 31% since 2013.

Finance effectiveness shifting 
A decline in strategy and control effeciveness over time

<img alt="Finance effectiveness" height="637" src="https://s3.us-south.cloud-object-storage.appdomain.cloud/ibv/Q_Raj6_ZGXVKJ_2t_Lq_Hj_Lmog_Y_Mh_bb3f731067" width="620" />

<blockquote>
Finance leaders report much room for improvement in their processes, and many are turning to AI for measurable results.
</blockquote>

Artificial intelligence as a key enabler to the finance function

Artificial intelligence can change and improve finance processes, driving quantifiable business outcomes and optimizing operational costs. It can be applied to improve transactional activities and decision support. And replacing manual work with AI automation and machine learning can help streamline financial processes while also enhancing business partnerships through better-informed decision-making.

In the record-to-report (R2R) general accounting and reporting area, AI-powered workflow and data models could include a reconciliation module that aggregates subledger transactions and performs risk-based reconciliations and cognitive forecasting. Automating procure to pay (P2P) with AI has been shown to increase productivity and permit finance to detect more fraudulent invoices. AI-driven innovations in order to cash (O2C) help with credit scoring, pricing decisions, and the prevention of payment frauds. AI technology and advanced analytics rank as key components of the financial planning and analysis (FP&amp;A) process, galvanizing and orchestrating planning and performance management.

The future of finance transformation

Given this immense opportunity for finance, the IBM Institute for Business Value (IBV), in partnership with the American Productivity &amp; Quality Center (APQC), surveyed 1,000 senior finance department personnel. The research covered their AI adoption in four key finance areas (R2R, P2P, O2C, FP&amp;A) and finance organization performance. This survey was conducted in 2021 with the goal of quantifying the impact of AI on operational level metrics. AI adopters, defined as those piloting, implementing, operating, or optimizing AI in any of the four finance areas, provided estimations of how AI influenced their scores on common finance metrics.

Across respondents, roughly half report adopting AI in each area, with only one-fifth operating or optimizing AI (see figure). For more than four in five of AI adopters, AI has been incorporated into business-as-usual environments for less than two years.

AI adoption by finance area 
Roughly half of organizations report adopting AI in each function 
 
<img alt="AI adoption" height="516" src="https://s3.us-south.cloud-object-storage.appdomain.cloud/ibv/0j_H_Ep34_Rd_F9_Nzxwm_JJ_Imx_E8v_0cd2b199ae" width="720" />Finance executives who have incorporated AI into processes demonstrated measurable results. Read this report to learn how these senior finance leaders use AI as a key enabler to the finance function, changing the way work gets done and driving quantifiable improvements.

Learn how senior finance leaders use AI for concrete improvements in process quality, cost, and efficiency.

AI's quantified impact on the finance function

Positioned at the intersection of business strategy and value, finance can spearhead the discovery of insights that facilitate faster, better decision-making for its organizations.

Yet <a href="https://ibm.co/c-suite-study-cfo" target="_blank">nearly half of the finance department&rsquo;s time</a> is still spent on transactional activities. In fact, <a href="https://ibm.co/c-suite-study-cfo" target="_blank">fewer than 10% of activities</a> are dedicated to analysis and action&mdash;areas crucial to supporting improved decisions. Finance teams need to free time so they can drive greater value for their organizations.

Exponential technologies are the key. They can deliver efficiencies and cost savings within core processes, such as purchase-to-pay (P2P), also known as the procure-to-pay process.

<blockquote>
Less than 10% of finance department activities are dedicated to analysis and action.
</blockquote>

The P2P process, a complicated system of purchasing and paying for the goods/services required to deliver a solution to customers, faces multiple challenges. These range from fragmented, siloed processes to manual inefficiencies and human errors to limited transaction visibility. The results are higher procurement and invoice processing costs as well as suboptimal user experiences for buyers and suppliers.

A recent benchmark survey by the IBM Institute for Business Value reveals how exponential technologies can help P2P and procurement teams overcome these obstacles and other bottlenecks. The advantages of automating processes and data collection, gaining intelligence, and eliminating error-prone manual tasks can indeed free finance teams to focus on higher value decision-making and strategic goals.

But adoption of these technologies is lagging. For example, only 33% of 500 purchase-to-pay managers surveyed report they are implementing, operating, or optimizing a procurement platform solution&mdash;an integrated system that fully or partially automates significant parts of procurement processes.

Those organizations that have optimized such a platform are seeing significant cost and quality improvements from P2P automation compared to those that have not implemented automation software. They have reduced the total process cost to order material and services per purchase order, and they can procure 22% more of their total purchase value from certified vendors.

<blockquote>
Organizations that have optimized a procurement automation platform can procure 22% more of their total purchase value from certified vendors.
</blockquote>

Automation solutions for accounts payable (AP)&mdash;while also not broadly adopted&mdash;are delivering similar advantages compared to time-consuming manual processes such as invoice matching. Case in point: organizations that have optimized an accounts payable platform report 33% fewer erroneous or duplicate payments.

Accounts payable automation platforms yield efficiencies

<img alt="Accounts payable automation platforms yield efficiencies" height="444" src="https://s3.us-south.cloud-object-storage.appdomain.cloud/ibv/bd_YS_Kx7_Egx_MDY_2_R_Ul_X6_JG_64u_c0599da2e3" width="700" />

Advanced analytics reveal opportunities

Respondents report that advanced analytics and predictive algorithms are helping them reduce costs and risks. For instance, by connecting data from suppliers and stakeholders, analytics can identify opportunities where contractual discounts have not been taken and where they could be introduced to suppliers that do not currently have them. Or they can point to potential fraud and abuse activities. In making relevant procurement data accessible in real time throughout the enterprise, analytics tools help strengthen decision-making too.

<blockquote>
52% of top-performing organizations based on key P2P measures use advanced analytics and algorithms.
</blockquote>

Artificial intelligence simplifies workflows

Survey results also indicate that initiatives in artificial intelligence (AI) are helping streamline workflows for greater efficiencies in procurement and accounts payable. <a href="https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/artificial-intelligence-finance" target="_blank">An AI-powered workflow can optimize touchless processing</a> and provide a unified interface for buyers, suppliers, procurement, and finance teams. The resulting spend and pricing intelligence enables better insights during sourcing.

<blockquote>
Organizations that have optimized AI in P2P processes detect 50% more fraudulent invoices.
</blockquote>

Getting started

Exponential technologies can be powerful performance boosters for finance. But organizations cannot jump into these investments without first building a foundation focused on the required people skills, processes, infrastructure, and culture. From there, technology adoption can accelerate P2P processes.

Download the report to see the impact of advanced technologies on purchase-to-pay. An action guide follows to help you lay the groundwork for adopting P2P solutions powered by automation, analytics, and AI.

Purchase-to-pay managers say adopting automation, advanced analytics, and artificial intelligence boosts performance.

Modernize purchase to pay

Boost purchase-to-pay performance with automation, analytics, and AI

Succeeding in economic uncertainty doesn&rsquo;t happen on its own. It requires purposeful agility, rapid innovation, and the right kinds of platforms and ecosystems. How to capitalize on these opportunities and lead decision-making? Look to finance functions as your key.

Yet, nearly half of finance function time is still spent on transactional activities. In fact, fewer than <a href="https://ibm.co/c-suite-study-cfo" target="_blank">10% of activities</a> are dedicated to analysis and action&mdash;areas crucial to supporting improved business decisions. To provide <a href="https://www.gartner.com/en/finance/glossary/record-to-report-r2r-" target="_blank">strategic, financial, and operational feedback</a> on the performance of the organization, finance needs to transform and modernize <a href="https://www.apqc.org/resource-library/resource-listing/record-report-process-and-internal-controls" target="_blank">record-to-report,</a> the end-to-end process of recording, closing, consolidating, and reporting on financial data at the period end.

Typically, the record-to-report process encounters a number of key challenges. 
&nbsp;

Journal entries: 
&nbsp;

<ul>
	<li>&nbsp;Inefficiency and variability arise with existing activities. Journal entries are created with many repetitive tasks, and manual follow-ups are required for much of the input data.</li>
	<li>Disparate enterprise systems coupled with lack of data access and process digitization often create a lack of transaction visibility. As a result, manual interventions in the process compromise the audit trail.</li>
	<li>The inability to review and verify all in-process journal entries leads to delayed close.</li>
	<li>Decisions cannot be made in real time. Limited business insights are available or delayed, often rendering them useless. 
	&nbsp;</li>
</ul>

Reconciliations: 
&nbsp;

<ul>
	<li>Siloed disparate processes involve manual and repetitive work.</li>
	<li>Few insights are available to facilitate meaningful forecasts or real-time decisions.</li>
	<li>A lack of transparency and traceability exists with no end-to-end visibility of siloed data.</li>
	<li>Finance staff encounter a poor user experience with no unified view of end-to-end operations that would allow them to work seamlessly without hindrances. 
	&nbsp;</li>
</ul>

Often, these challenges pile up, resulting in complexity, time-consuming activities, errors, and inaccurate financial results. An ideal record-to-report process will digitally modernize finance operations with virtual and continuous accounting to better meet compliance requirements, improve efficiency, and enable business leaders to make agile&nbsp; financial decisions. The financial reports produced by the record-to-report process help CxOs with strategic planning in terms of performance goals and actions required.

<blockquote>
An increase in efficiency in record-to-report can directly reduce process costs since on average record-to-report is approximately 20% of all finance function FTEs.
</blockquote>

The IBM Institute for Business Value (IBV) Performance Data and Benchmarking Program performed a statistical analysis of data collected in 2021 and 2022 from more than 500 finance managers globally. The analysis correlates the adoption of exponential technologies with outperformance in a variety of record-to-report performance measures.

<h2 class="ck-h2 ibv-ckeditor">How data management sets the foundation for record-to-report</h2>

We determined that top-performing organizations in the cycle time to perform the monthly financial close have three things in common: maturity of information availability, the use of robotic process automation (RPA), and implementation of artificial intelligence.

When we segmented organizations based on the volume of journal entries processed annually, we saw differences in the relative importance of those three factors (see figure). For organizations that process fewer than 1.5 million journal entries annually, the top predictor of cycle time was the maturity of information availability. For organizations processing more than 1.5 million journal entries annually, the top predictor of cycle time was the implementation of artificial intelligence. 
&nbsp;

Key factors in estimating the speed for the monthly close

<h2 class="ck-h2 ibv-ckeditor"><img alt="Key factors in estimating the speed for the monthly close" height="320" src="https://s3.us-south.cloud-object-storage.appdomain.cloud/ibv/Lr_Z_Dbctnz_F_Ab_D_Fw6k_Cs_ZK_gz_2ab9f3b11d" width="640" /></h2>

Data is the heart of record-to-report. The emphasis on information availability can be explained by struggles with: 
&nbsp;

<ul>
	<li>Gathering and processing structured and unstructured data from different sources</li>
	<li>Collecting and consolidating a large amount of data</li>
	<li>Validating and reconciling data. 
	&nbsp;</li>
</ul>

For those organizations processing fewer than 1.5 million journal entries annually, reduction of this data&rsquo;s structural complexity becomes a precondition. This requires an enterprise-wide data governance framework: data standards and rules to collect, use, and share data simplify the environment.

A comprehensive and consistent enterprise architecture is important to the scaling and compatibility of workflows. Cloud computing can help with the control and sharing of data. Without this cloud environment, finance organizations will struggle to develop and maintain not only financial and operational data, but the integration of the two. In addition, cloud computing allows access to exponential technologies, such as automation and artificial intelligence, and supports the seamless flow of data, enabling finance organizations to use it in new ways.

In fact, 61% more organizations that are leading or optimizing in data availability and analysis capabilities have adopted cloud technology (as opposed to on-prem or hosted solutions) for their general accounting and reporting processes compared to other respondents with lower maturity (see figure). 
&nbsp;

Adoption of cloud: Cloud computing assists with data availability and analysis.

<img alt="Adoption of cloud" height="742" src="https://s3.us-south.cloud-object-storage.appdomain.cloud/ibv/o_Vh_V_Fu2_Leip8bd_NJ_0iw_Sdl99_e8cfdf9697" width="1500" />

<blockquote>
With only 35% of respondents reporting adoption of cloud technology for their general accounting and reporting processes, cloud computing presents an untapped opportunity for many finance organizations.
</blockquote>

<h2 class="ck-h2 ibv-ckeditor">Start your record-to-report modernization</h2>

With proper data management, the combination of automation and artificial intelligence is powerful for record-to-report. These exponential technologies provide tangible business outcomes, with greater than two days&rsquo; close cycle time reduction achievable.

A 100% on-time close calendar, 70% plus transactional efficiency by reducing actions associated with data processing, and 40%-60% cost reduction are all possible. In terms of effectiveness, organizations could generate a more than 50% improvement on financial opportunity and risk insights.

Download the report to learn more about how data management, automation, and artificial intelligence can transform and modernize record-to-report. We conclude the report with an action guide that provides clear next steps on applying these exponential technologies to record-to-report.

How AI and automation are changing the game.

Modernize record-to-report

The case for AI in Finance - Infographic

The case for AI in Finance 

Top performers not only adopt, but optimize, AI and business process automation to help improve cost, efficiency, and quality.

The purchase-to-pay power of exponential tech-Infographic

The purchase-to-pay power of AI and automation

As climate change intensifies, companies are racing to meet crucial sustainability goals. They&rsquo;re setting net-zero targets and accelerating their transition to renewable energy. They&rsquo;re considering climate action in their investments and measuring their carbon footprint across the entire supply chain. They&rsquo;re participating in the circular economy and developing zero-waste products.

But these goals are only meaningful if they are measurable. As sustainability becomes an integral part of an organization&rsquo;s business strategy, the CFO must take a leading role. In fact, the United Nations has convened a <a href="https://www.cfocoalition.org/" target="_blank">CFO Coalition for the Sustainable Development Goals</a>, recognizing that &ldquo;as stewards of trillions of dollars in corporate investments, CFOs are uniquely positioned to reshape the future of corporate finance and investment as a catalyst for growth, value creation, and social&nbsp;impact.&rdquo;

<blockquote>
&ldquo;CFOs are uniquely positioned to reshape the future of corporate finance and investment as a catalyst for growth, value creation, and social&nbsp;impact.&rdquo;
</blockquote>

CFO priorities around sustainability are expected to change drastically as emerging and complex environmental, social, and governance (ESG) reporting requirements call for greater transparency. These, along with regulator and investor pressures and consumer demands for measurable sustainability efforts, have massive implications for the finance function.

To explore the expanding sustainability scope for finance and what actions organizations are taking, the IBM Institute for Business Value (IBV) partnered with the American Productivity and Quality Center (APQC) to survey 1,085 senior finance personnel, including CFOs, finance directors, comptrollers, and finance managers.

As in the <a href="https://www.ibm.com/thought-leadership/institute-business-value/c-suite-study/ceo" target="_blank">IBV 2022 CEO Study</a>, we grouped our respondents into four categories based on how they characterize their organizations&rsquo; investments in sustainable business practices, such as adopting clean energy, improving waste management, and developing a green portfolio: 
&nbsp;

<ul>
	<li>Assessing finance leaders (17%): Have not yet made any enterprise-wide investments in sustainability programs. While some are exploring options, others have no plans at all.</li>
	<li>Complying finance leaders (26%): Use corporate sustainability investments solely to meet compliance and regulatory requirements.</li>
	<li>Operational finance leaders (47%): View sustainability investments as a business optimization opportunity in core and noncore business areas.</li>
	<li>Transformational finance leaders (10%): View sustainability investments as an avenue for enterprise transformation. 
	&nbsp;</li>
</ul>

Substantiating sustainability: Finance leaders help their organizations gauge the performance of sustainability initiatives.

<img alt="Substantiating sustainability" height="975" src="https://s3.us-south.cloud-object-storage.appdomain.cloud/ibv/Y_Hs35r_GN_Kb_Xar_N_T4suls_ZO_2_cc054c51cd" width="1500" />

Our analysis quantifies how Transformational finance leaders are embracing five key responsibilities to deliver real-world sustainability results. 
 
1. Lead on quantification: Involves building business cases, improving decision-making, and assessing effectiveness of sustainability initiatives; also tracking environmental impact in areas such as fossil fuel and water consumption, greenhouse gas emissions, materials, reuse and recycling, compliance, and more.

2. Mobilize capital: Involves obtaining sustainable financing and allocating investments to sustainability initiatives. Also includes providing insights on green bonds and communicating to institutional investors the impact of sustainability on credit and insurance, as well as employee retention, customer retention, and the ability to raise capital.

3. Enable culture change: Involves cultivating a culture focused on sustainability through aligned incentives for both employees and partners to help drive execution of sustainability action items.

4. Operationalize sustainability reporting: Creates transparency around the enterprise&rsquo;s progress toward sustainability goals and commitments in a fluid regulatory landscape. Also includes partnering across the organization to select the right metrics, quantify and manage risks, and keep external and internal stakeholders informed.

5. Embed sustainability in finance operations: Focuses on reducing greenhouse gas emissions within the finance function with tactics such as offering low-carbon travel choices and pursuing net-zero emissions with finance technology solutions.

We found that Transformational finance leaders use three approaches to position their organizations for sustainability success: 
&nbsp;

<ul>
	<li>Agile principles to enhance their organization&rsquo;s flexibility to support business model changes</li>
	<li>Data centricity to facilitate integration and evaluation of nonfinancial data associated with sustainability goals&mdash;enabled through a robust data infrastructure</li>
	<li>A nimbler way of working to focus on speed of innovation and partnerships across business functions.</li>
</ul>

Read the report to explore the data and case studies that demonstrate how Transformational finance leaders are helping their organizations address the climate crisis and build a sustainable future while realizing the business potential of sustainability. A detailed action guide offers specific steps finance functions can take to support sustainability initiatives.

Finance leaders play a critical—and measurable—role in implementing their organizations’ sustainability initiatives.

The sustainability imperative: The integral role of finance—quantified

The integral role of finance in sustainability initiatives

レポート概要： 
&nbsp;

<ul>
	<li>『変革型』経理財務部門リーダーは、サステナビリティー投資が企業に変革をもたらすと捉えています。</li>
	<li>『変革型』経理財務部門リーダーの過半数（53%）は、主導的にサステナビリティー活動にかかるビジネス・ケース*の生成に貢献しています。</li>
	<li>『変革型』経理財務部門リーダーの半数近く（44%）は、サステナビリティー戦略を実行する上で、従業員の足並みがそろうよう動機付け（インセンティブ）を行っています。</li>
	<li>必要なアクションとは？ ー IBVの調査により、自社のサステナビリティー推進において、経理財務部門リーダーが果たすべき5つの責務を特定しました。また、日本語版監訳者考察では、日本企業におけるSX推進の現状を踏まえた上で、取り組むべきアクションの要諦を解説しています。</li>
</ul>

詳しくは、レポートをご覧ください。

*ビジネス・ケース：事業の投資価値や正当性を示す説明文書 
 
レポート詳細：

気候変動が激化する中、企業は重要なサステナビリティー目標を達成すべく日々競争しています。ネット・ゼロ目標の設定により、再生可能エネルギーに移行する取り組みを加速させ、投資の際にも気候変動対策を考慮し、サプライチェーン全体でカーボン・フットプリントを削減します。また、循環型経済に移行することで、廃棄物を出さない製品の開発も進めています。

しかしこれらの目標も、測定可能でなければ意味がありません。実際、国連が<a href="https://www.cfocoalition.org/" target="_blank">「SDGsのためのCFO連合（CFO Coalition for the Sustainable Development Goals）」</a>を発足させたのも、「CFOは数兆ドルに及ぶ企業投資の管理者であり、成長や価値創造、社会的影響を促すカタリスト（触媒）として、企業の経理財務と投資の将来像を決定する特有の立場にある」という認識があるからです。

<blockquote>
「CFOは、成長や価値創造、社会的影響を促すカタリスト（触媒）として、企業の経理財務と投資の将来像を決定する特有の立場にある」
</blockquote>

サステナビリティーにおいて、CFOが担う重要課題は、今後大きく変わっていくと見込まれます。企業に求められるESGレポートの新たな要件は、複雑化し、透明性の向上が求められるためです。こうした動きに加え、測定可能なサステナビリティー施策を求める規制当局と投資家からの圧力や消費者のニーズも強まっており、経理財務部門の役割に大きな影響を及ぼし始めています。

経理財務部門によるサステナビリティーの活動範囲がどのように広がっているのか、また実際にどのような対策が行われているのかを調べるため、IBVは米国生産性品質センター（APQC）と共同で上級経理財務担当者1,085人を対象に調査を実施しました。この中には、CFOをはじめ、ファイナンス・ディレクター、会計監査役、ファイナンス・マネージャーなどが含まれます。

<a href="https://www.ibm.com/thought-leadership/institute-business-value/jp-ja/c-suite-study/ceo">IBVの2022年のCEOスタディ</a>と同様に、各社の持続可能なビジネス施策（クリーン・エネルギーの導入、廃棄物管理の改善、グリーン・ポートフォリオの開発など）への投資の特徴に基づき、回答者を以下の4つのカテゴリーに分類しました。

&nbsp;

<ul>
	<li>『現状維持型』経理財務部門リーダー（17%）：まだ全社的なサステナビリティー投資を行っていない（投資を検討している企業もあるが、全く計画していない企業もある）</li>
	<li>『コンプライアンス重視型』経理財務部門リーダー（26%）：法令・規制要件の順守のみを目的としたサステナビリティー投資を行っている</li>
	<li>『オペレーション重視型』経理財務部門リーダー（47%）：サステナビリティー投資を中核および非中核双方のビジネス最適化の機会として捉えている。</li>
	<li>『変革型』経理財務部門リーダー（10%）：サステナビリティー投資を企業変革の実現手段として捉えている</li>
</ul>

&nbsp;

サステナビリティーの検証：経理財務部門リーダーは、サステナビリティー活動の測定を支援しています。

<img alt="Substantiating sustainability" height="975" src="https://s3.us-south.cloud-object-storage.appdomain.cloud/ibv-jp-ja/B5_Ma0t_g9y_HCRBK_3_SI_8dh8_Z5_9b237dbcd4" width="1500" />

調査結果から自社のサステナビリティー推進において、経理財務部門リーダーに求められる以下5つの責務で、『変革型』経理財務部門リーダーは際立った成果を上げていることが判明しました。 
 
1. 定量化を主導する：ビジネス・ケースを構築し、意思決定を改善して、サステナビリティー活動の効果を評価します。また、化石燃料や水消費、GHGの排出、素材、再利用とリサイクル、コンプライアンスなどの領域で環境への影響を追跡調査します。

2.&nbsp;資本を活かす：持続可能な方法で資金を調達し、サステナビリティー活動へ投資資金を配分できるよう支援します。また、グリーン・ボンド（環境債）の仕組みやサステナビリティーが与信と保険に与える影響について機関投資家に情報を提供するほか、従業員の定着率、顧客リテンション（既存顧客との関係維持・つなぎ留め）、資金調達能力などに関する説明も行います。

3.&nbsp;組織文化を変革する：従業員とパートナー企業双方のインセンティブの方向性を一致させ、サステナビリティー活動を推進させることで、サステナビリティーを重視する組織文化を醸成します。

4.&nbsp;サステナビリティー・レポート作成を業務に組み込む：流動的な規制環境の中で、サステナビリティーの目標や公約の取り組み状況についての透明性を確保します。また、全社的な連携を通じ、適切な評価指標の選定やリスクの定量化と管理、社内外の利害関係者への円滑な情報提供を実施します。

5.&nbsp;サステナビリティーを経理財務部門の業務に組み込む：例えば出張時のサステナブルな移動手段を提供したり、経理財務のテクノロジー・ソリューションにおける排出量のネット・ゼロを目指したりすることによって、経理財務部門内でのGHG排出量を軽減します。

『変革型』経理財務部門リーダーは、以下の3つのアプローチによって自社のサステナビリティー実現を追求しています。 
&nbsp;

<ul>
	<li>アジャイルな手法により、自社の柔軟性を高め、ビジネスモデルの変化に対応</li>
	<li>堅牢なデータ・インフラストラクチャーを基盤としたデータ中心主義により、サステナビリティー目標に関連する非財務データの統合と評価を推進</li>
	<li>より機敏な対処により、イノベーションのスピード向上と全社協力体制を構築</li>
</ul>

『変革型』経理財務部門リーダーは、組織として気候変動危機に対処し、持続可能な未来を築きつつ、サステナビリティーの取り組みを通じて、いかにビジネスの可能性を広げていくかを模索しています。本レポートのデータやケース・スタディーで詳細をご確認ください。なおアクション・ガイドでは、経理財務部門がサステナビリティー活動を支援するために実践できる具体的なステップも紹介しています。

また、日本語版監訳者考察では、日本を含めたグローバル地域別の調査結果を踏まえて、日本企業におけるSX推進の現状を考察した上で、日本の経理財務部門ならびに経理財務部門リーダーが取り組むべきアクションの要諦を解説しています。ぜひご覧ください。

 
経理財務部門リーダーの割合 〜グローバル地域別&times;リーダー類型別〜

<img alt="経理財務部門リーダーの割合 〜グローバル地域別×リーダー類型別〜" height="1650" src="https://s3.us-south.cloud-object-storage.appdomain.cloud/ibv-jp-ja/0n_U_Af_T_Zqrxv_Gus_HU_Ubgs3_Vav_1675077b82" width="2236" />

&nbsp;

<h1 class="ck-h1 ibv-ckeditor">監訳者</h1>

<img alt="" height="200" src="https://s3.us-south.cloud-object-storage.appdomain.cloud/ibv-jp-ja/xq_G7_F8wfui7_F46tr1_Ypfi2_Gp_831641280f" width="200" />

真藤 達也 
経理財務トランスフォーメーション 
パートナー 
&nbsp;

<img alt="" height="200" src="https://s3.us-south.cloud-object-storage.appdomain.cloud/ibv-jp-ja/T8_VX_0h7_Khg3_DWU_Vr_S8_H_Xx_S_3dab561d3d" width="200" />

柴山 龍治 
FSCT Finance Transformation Strategy 
サステナブル経営管理リード 
アソシエイト・パートナー

サステナビリティーがビジネス戦略に不可欠となる中、最高財務責任者（CFO）には、主導的役割が求められています。 

SX推進でビジネスを変える：経理財務部門の役割が不可欠 - 定量化を主導

Artificial intelligence has emerged as a stabilizing force for the finance function. The hype has given way to real-world advancements that let CFOs more easily and accurately plan for future disruption.To learn how CFOs are using AI to make finance more responsive, unlock real-time insights, and streamline operations, the IBM IBV conducted three separate research studies. We assessed what metrics finance organizations are using to gauge the success of their AI initiatives, how AI-powered processes are moving the needle, and how more mature AI adopters perform on KPIs compared to their peers.Our research reveals how the most successful finance organizations are tapping AI systems to transform their operations—and where these efforts are delivering the greatest value. We found that mature AI adopters are improving finance performance by optimizing four key processes: financial planning and analysis, order-to-cash, procure-to-pay, and record-to-report. &nbsp;<h4>Gaining the flexible finance advantage</h4>As AI-driven transformation redefines growth strategies—and the potential of agentic AI dominates the business news cycle—CFOs are looking for responsible ways to leverage AI’s power in finance.<blockquote>69% of CFOs say AI is central to their finance transformation strategy—but fewer than 30% are operating or optimizing traditional AI in key processes.</blockquote>Nearly four in five CFOs plan to maintain or accelerate their finance organization’s pace of transformational change—and 69% say AI is integral to their finance transformation strategy. But successfully implementing AI in finance remains complex. 56% of CFOs cite the execution of AI initiatives as a major obstacle for their finance teams, with 38% saying their finance AI budgets are inadequate to meet their strategic objectives.Looking forward, CFOs are intentionally prioritizing technology investments to enhance their decision-making capabilities. But AI initiatives often require substantial investments in both technology and talent, which can be a barrier for organizations with limited budgets or expertise.Additionally, the need for a robust data infrastructure—and the alignment of new AI solutions with existing systems—can slow adoption. Plus, concerns around regulatory compliance, cybersecurity, and the privacy and security of financial data give CFOs pause. They’re also worried about the accuracy and reliability of AI applications, especially in regard to high-stakes financial decision-making processes.It’s a lot to navigate. As a result, the adoption of both traditional and generative AI in finance remains relatively low. The portion of organizations that are operating or optimizing traditional AI across finance function ranges from 20% to 30%—and that figure is even lower for generative AI. &nbsp;Finance organizations have made limited headway with AI adoption Percentages reflect respondents who say their organization is “operating” or “optimizing” AI.&nbsp;<figure class="image"><img style="aspect-ratio:1500/1699;" src="/thought-leadership/institute-business-value/uploads/en/L1_Vfvc_MN_3cb_FL_Pbg_E7_L_Rzg_O3_95079dcf45.jpg" width="1500" height="1699"></figure> Looking ahead, CFOs are committed to extending the use of AI across finance processes. More than four in five CFOs say it’s important to adopt traditional AI in financial planning and analysis (84%) and procure-to-pay (83%). Similarly, 81% and 78% say it’s important to adopt gen AI in procure-to-pay and order-to-cash, respectively. This widespread intent to broaden AI adoption underscores CFOs’ growing recognition that AI tools can revolutionize finance operations—and that urgent action is needed to stay ahead of the curve. &nbsp;<h4>Unlock operational excellence with AI for finance</h4>As organizations are pushed to derive insights from vast amounts of data faster than ever, CFOs are looking to get more of the benefits of AI.<blockquote>Mature AI adopters complete the annual budget cycle 33% faster and cut&nbsp;annual accounts payable costs per invoice by 25%.</blockquote>"Mature AI adopters," which we define as organizations operating or optimizing AI technology in their finance function, have been able to reduce costs and redeploy resources at a higher rate than their peers.This group of leaders, which makes up only 35% of our respondents, attribute a 16% reduction in their total annual finance function cost as a percentage of revenue to AI implementation. This quantifies the substantial gains that come from effective AI strategy execution and underscores the importance of fully optimizing AI's capabilities in finance. These adopters have also redirected 30% of their resources to high-value activities, compared to just 10% for all others.From forecasting and predictive analytics to improved fraud detection and credit scoring, leading adopters of AI in finance are getting better results from a wide variety of use cases. Read the full report to see how these organizations are using AI to enable the operational efficiency, cost savings, and strategic insights that boost productivity and drive long-term growth.&nbsp;&nbsp;

CFOs can propel precision, prowess, and performance by transforming finance operations with AI.

  Benchmarking the AI advantage in finance

Artificial intelligence is no longer an emerging capability in finance. It is already reshaping how work gets done—cutting costs, accelerating cycles, and improving accuracy across core finance processes. Yet as AI adoption grows, performance gaps between organizations are widening rather than narrowing.The difference is not access to better models or more advanced tools. It is execution.New research from the&nbsp;IBM Institute for Business Value (IBV), conducted in partnership with the&nbsp;American Productivity &amp; Quality Center, shows that finance organizations achieving the strongest and most durable AI returns are not those deploying the most use cases. They are those embedding AI deeply into execution—redesigning workflows, standardizing data, and governing AI as an operational capability rather than a set of pilots.Among experienced adopters, AI is already delivering a&nbsp;median 8% reduction in total annual finance cost, with reductions reaching&nbsp;18%&nbsp;where AI is built directly into end‑to‑end processes rather than siloed use cases. As finance leaders push AI from experimentation into daily operations, execution discipline—not technology choice—has become the decisive factor.&nbsp;<blockquote>Finance organizations achieving the strongest and most durable AI returns are not those deploying the most use cases. They are those embedding AI deeply into execution.</blockquote><h3> From experimentation to execution: why sequencing matters</h3>AI adoption in finance is uneven by design—and that unevenness is a signal, not a flaw.IBV research shows that high‑performing finance organizations sequence AI deployment, scaling first in domains where data is already standardized and workflows are repetitive, while deliberately investing to prepare other areas for future scale. This sequencing approach consistently outperforms broad, simultaneous rollout strategies.The pattern is clear across the four core finance domains: &nbsp;<ul><li data-list-item-id="e33c8861c9d9b2cb6a14959b0ac548e3a">Financial planning and analysis (FP&amp;A)&nbsp;leads early adoption, reflecting structured data, recurring planning cycles, and well‑defined metrics</li><li data-list-item-id="e6eb6945006df80613dbf213a556a010d">Order‑to‑cash (O2C)&nbsp;and&nbsp;record‑to‑report (R2R)&nbsp;follow, where transaction flows and control frameworks support embedded AI</li><li data-list-item-id="ed4ab89b308eb37f6fb22e233abc9ea12">Procure‑to‑pay (P2P)&nbsp;lags, constrained by fragmented supplier data and upstream complexity</li></ul>&nbsp; Among organizations that have reached the highest levels of AI maturity, adoption reaches&nbsp;95% in FP&amp;A, but only&nbsp;58% in P2P. This gap does not reflect lower ambition—it reflects disciplined prioritization. Leaders scale AI where it can deliver measurable value today, while building the data, integration, and governance foundations required elsewhere.The same logic applies to advanced AI. Generative AI adoption concentrates in analytics‑ and narrative‑heavy work such as forecasting and reporting, where outputs can be governed and tied to clear deliverables. Early agentic AI adoption appears in bounded, transaction‑heavy workflows—particularly in O2C—where&nbsp;the autonomy of AI agents can be constrained by rules, monitoring, and escalation paths. &nbsp;<h3>Where AI in finance pays off—and why execution maturity wins</h3>AI is already delivering structural efficiency gains across the finance function. But the magnitude, speed, and reliability of those gains depend on execution maturity.Across the sample, experienced adopters report a&nbsp;median 8% reduction in total finance cost, while organizations with more mature execution models report reductions approaching&nbsp;18%. These gains do not come from isolated automation. They come from embedding AI into redesigned workflows where decision-making, controls, and data flows are simplified first.Execution maturity also reshapes delivery economics: &nbsp;<ul><li data-list-item-id="ec9f0703199feaaa5e6682122f6265ae2">Deployment cost falls by roughly 30%&nbsp;as maturity increases, dropping from about&nbsp;USD59 to USD39 per employee.</li><li data-list-item-id="e927c2bea61dea83058f8063730dd8c36">Payback time improves by around 25%, declining from roughly&nbsp;eight months to six.</li><li data-list-item-id="ed7ebd1b5e08e78e8a6cf6c2e7815027a">Time to deploy shortens, reflecting repeatable delivery models rather than one‑off projects.</li></ul>&nbsp; Process‑level outcomes reinforce the pattern. In FP&amp;A, AI reduces manual effort while improving forecast reliability. In O2C, organizations report fewer uncollectables, faster invoice‑to‑cash cycles, and lower days sales outstanding. In R2R, cost per journal entry declines while close cycles shorten and first‑time accuracy improves. Even in P2P, where scale is harder, experienced adopters report gains in invoice processing cost and approval cycle time.What differentiates these outcomes is not the use case itself, but whether AI is embedded into execution—supported by controls, monitoring, and clear exception management—rather than layered onto fragmented processes. &nbsp;<h3>The operating model gap holding finance back</h3>Despite proven returns, many organizations struggle to scale AI consistently. The obstacles are rarely technical.Finance leaders cite a familiar set of barriers: concerns around AI reliability, compliance, and regulation; limited AI skills capacity; poor data quality and fragmented systems; and integration complexity across legacy platforms. These gaps explain why AI value compounds in some organizations and stalls in others.The research highlights a critical insight: what matters most is not&nbsp;how many&nbsp;AI use cases are deployed, but&nbsp;how&nbsp;they are deployed. Many finance processes were designed for manual execution across disconnected systems, with periodic review and heavy handoffs. Applying AI on top of these workflows accelerates inefficiency rather than eliminating it.High‑performing finance organizations take a different approach. They redesign workflows before embedding AI—clarifying decision points, standardizing inputs, reducing handoffs, and defining how AI outputs trigger action. Trust, auditability, and exception handling are designed into the workflow from the start, protecting both ROI and credibility as AI scales.<blockquote>High‑performing finance organizations redesign workflows before embedding AI—clarifying decision points, standardizing inputs, reducing handoffs, and defining how AI outputs trigger action.</blockquote>&nbsp;Action guide<div class="ck-accordion-wrapper"><details class="ck-accordion" data-id="ckeditor5-accordion-1779197410720-gst"><summary class="ck-accordion-title"><div class="ck-accordion-title-icon">Building durable AI advantage in finance<div contenteditable="false"><svg focusable="false" preserveAspectRatio="xMidYMid meet" fill="currentColor" width="22" height="22" viewBox="0 0 16 16" aria-hidden="true" class="cds--accordion__arrow" xmlns="http://www.w3.org/2000/svg"><path d="M11 8L6 13 5.3 12.3 9.6 8 5.3 3.7 6 3z"></path></svg></div></div></summary><div class="ck-accordion-content">As AI becomes embedded in core finance execution, CFOs and leaders must own architecture and operating‑model decisions. IBV research identifies three moves finance leaders can make now to turn AI into a repeatable source of value. 1. Design for return, not pilots High‑return organizations treat AI as a portfolio of process outcomes—not a collection of tools. They build a multi‑year finance AI blueprint aligned to enterprise AI strategy and deliberately sequence deployment across financial planning and analysis (FP&amp;A), order‑to‑cash (O2C), procure‑to‑pay (P2P), and record‑to‑report (R2R) based on execution readiness.Anchor each use case to a primary value driver—cost, cycle time, or quality—supported by a small set of secondary metrics. This discipline enables teams to compare initiatives, prioritise investment, and scale or stop efforts based on evidence rather than enthusiasm. &nbsp;2. Solidify the foundation for scale High‑return organizations treat data, trust, and lifecycle management as prerequisites—not afterthoughts. They invest in governed, reusable finance data foundations, explicitly define data requirements for priority workflows, and embed explainability, auditability, and controls directly into delivery.Institutionalize model lifecycle management through regular or real-time continuous review, preventing performance drift and sustaining value as data and requirements evolve. Fund integration and platform modernization—particularly in P2P—as part of the AI value case, rather than deferring it as background IT work. &nbsp;3. Industrialize execution Organizations that achieve faster payback and lower delivery costs move beyond project‑based delivery. They build an execution engine that repeatedly converts ideas into production outcomes.Establish formal use‑case intake, assign accountable AI product owners by finance process, conduct regular ROI reviews, and define clear Finance–IT decision rights. Embed adoption mechanisms—training, updated operating procedures, and usage monitoring—into every production release, making AI part of how finance runs day to day.&nbsp;</div></details></div>&nbsp;<h3>AI value in finance is no longer constrained by technology. It is constrained by execution.</h3>Organizations that sequence AI intentionally, redesign workflows before automating them, and anchor AI in a finance‑led operating model turn early gains into sustained advantage—not just for finance, but for the entire enterprise. &nbsp;How AI adoption deepens across finance processes with execution maturity<figure class="image"><img style="aspect-ratio:4501/4244;" src="/thought-leadership/institute-business-value/uploads/en/30_B_Kheiyt_D_Mnle81_T_Dtzd_WWJ_75c5eeeb09.png" width="4501" height="4244" alt="The graphic shows&nbsp;how AI adoption deepens across finance processes with execution maturity"></figure>&nbsp; Download the report&nbsp;to explore how finance leaders are scaling AI with confidence in cost, control, and return.FAQ<div class="ck-accordion-wrapper"><details class="ck-accordion" data-id="ckeditor5-accordion-1779198089568-0iw"><summary class="ck-accordion-title"><div class="ck-accordion-title-icon">Why does execution maturity matter more than technology choice for AI in finance?<div contenteditable="false"><svg focusable="false" preserveAspectRatio="xMidYMid meet" fill="currentColor" width="22" height="22" viewBox="0 0 16 16" aria-hidden="true" class="cds--accordion__arrow" xmlns="http://www.w3.org/2000/svg"><path d="M11 8L6 13 5.3 12.3 9.6 8 5.3 3.7 6 3z"></path></svg></div></div></summary><div class="ck-accordion-content">The research shows that AI outcomes in finance are driven by how deeply AI is embedded into workflows, not by tools alone. Organizations with mature execution models achieve faster payback—around six months versus eight—and significantly lower delivery costs, demonstrating that operating discipline, not technology selection, determines scalable AI value.</div></details></div><div class="ck-accordion-wrapper"><details class="ck-accordion" data-id="ckeditor5-accordion-1779198109348-i1w"><summary class="ck-accordion-title"><div class="ck-accordion-title-icon">What financial impact does AI deliver when scaled across finance operations?<div contenteditable="false"><svg focusable="false" preserveAspectRatio="xMidYMid meet" fill="currentColor" width="22" height="22" viewBox="0 0 16 16" aria-hidden="true" class="cds--accordion__arrow" xmlns="http://www.w3.org/2000/svg"><path d="M11 8L6 13 5.3 12.3 9.6 8 5.3 3.7 6 3z"></path></svg></div></div></summary><div class="ck-accordion-content">Experienced AI adopters report a median 8% reduction in total finance costs, rising to as much as 18% when AI is embedded end to end across finance processes rather than deployed in isolated use cases. This shows that scale—not pilots—unlocks the largest economic gains.</div></details></div><div class="ck-accordion-wrapper"><details class="ck-accordion" data-id="ckeditor5-accordion-1779198135024-1oi"><summary class="ck-accordion-title"><div class="ck-accordion-title-icon">Where should finance leaders prioritize AI deployment first?<div contenteditable="false"><svg focusable="false" preserveAspectRatio="xMidYMid meet" fill="currentColor" width="22" height="22" viewBox="0 0 16 16" aria-hidden="true" class="cds--accordion__arrow" xmlns="http://www.w3.org/2000/svg"><path d="M11 8L6 13 5.3 12.3 9.6 8 5.3 3.7 6 3z"></path></svg></div></div></summary><div class="ck-accordion-content">The research finds that AI adoption is highest where data is standardized and workflows are repeatable. Among optimizing adopters, AI penetration reaches 95% in financial planning and analysis, compared with just 58% in procure-to-pay, signalling where finance teams should scale AI now and where to build prerequisites first.</div></details></div><div class="ck-accordion-wrapper"><details class="ck-accordion" data-id="ckeditor5-accordion-1779198155472-015"><summary class="ck-accordion-title"><div class="ck-accordion-title-icon">How does operating model maturity affect AI economics and speed to value?<div contenteditable="false"><svg focusable="false" preserveAspectRatio="xMidYMid meet" fill="currentColor" width="22" height="22" viewBox="0 0 16 16" aria-hidden="true" class="cds--accordion__arrow" xmlns="http://www.w3.org/2000/svg"><path d="M11 8L6 13 5.3 12.3 9.6 8 5.3 3.7 6 3z"></path></svg></div></div></summary><div class="ck-accordion-content">Organizations with mature AI execution models not only achieve faster payback but also reduce delivery costs by around 30%, with median costs dropping from about USD59 to USD39 per employee. This highlights how industrialized execution improves both speed and economics as AI scales.</div></details></div><div class="ck-accordion-wrapper"><details class="ck-accordion" data-id="ckeditor5-accordion-1779198182119-2ud"><summary class="ck-accordion-title"><div class="ck-accordion-title-icon">&nbsp;Why are governance and data foundations critical to scaling AI in finance?<div contenteditable="false"><svg focusable="false" preserveAspectRatio="xMidYMid meet" fill="currentColor" width="22" height="22" viewBox="0 0 16 16" aria-hidden="true" class="cds--accordion__arrow" xmlns="http://www.w3.org/2000/svg"><path d="M11 8L6 13 5.3 12.3 9.6 8 5.3 3.7 6 3z"></path></svg></div></div></summary><div class="ck-accordion-content">The report shows AI scale often stalls due to gaps in trust, data quality, integration and governance. High‑performing finance teams design controls, auditability and data discipline directly into workflows, enabling AI to deliver repeatable, credible results across regulated, mission‑critical finance processes.</div></details></div>&nbsp;&nbsp;

Finance execution maturity—not technology—unlocks scalable AI value, transforming core finance processes through disciplined operating models.

Finance execution unlocks AI value at scale

Scaling AI in finance 

Human capital

A case for AI in HR - Infographic

A case for AI in HR

Supply chain

Manufacturing 4.0 is about unlocking the value of the Fourth Industrial Revolution, or Industry 4.0, which can help an organization thrive&mdash;even amid disruption.

With the infusion of digital technologies in facilities and operations, manufacturers are redefining possibilities for resource and production process efficiency, asset utilization, labor productivity, quality enhancement, time to market reduction, and service value-add.

&ldquo;Smart manufacturing,&rdquo; which is driven by the analysis of data generated by advanced sensors, software, and robotics, can lead to improved decision making. Heightened visibility and insights are generated from the combination of production data and operational data within an organization and across ecosystem partners.

In addition, the potential value of data sharing in manufacturing process optimization has been estimated at over <a href="https://www.weforum.org/whitepapers/share-to-gain-unlocking-data-value-in-manufacturing" target="_blank">$100 billion</a>.

Data rich, information poor

As part of Industry 4.0, cyber-physical models and digital integration of new technologies across the value chain help increase efficiency and provide mass customization capability. The convergence of information technology (IT) and operational technology (OT) systems creates interconnectivity between autonomous manufacturing equipment and broader computer systems.

However, our recent survey of 2,360 manufacturing industry executives in 32 countries revealed that manufacturers are not tapping the full power of big data, and many face significant technology barriers. Despite the revolutionary potential of data insights in manufacturing, most organizations are not using data from equipment, processes, and systems in any meaningful way&nbsp;to draw insights for continuous process improvement.

<blockquote>
Only 28% of manufacturing organizations are using data from equipment, processes, and systems to draw insights for continuous process improvement.
</blockquote>

For example, predictive maintenance involves the continuous collection and analysis of sensor data to identify faults prior to failures and prompt interventions only when needed. But only a quarter of organizations are optimizing asset or equipment maintenance schedules based on analysis of failure modes and balancing reliability and cost.

Fewer than 1 in 5 have real-time access to important manufacturing data across the enterprise, which could include useful unstructured data from spreadsheets, industrial social media, email, text files, video, or CAD.

Driving business value through Industry 4.0

Executives identify a combination of technologies that are important to advancing their manufacturing objectives. More than three quarters cite AI, which is not surprising given AI-generated insights help improve visibility and predictability of manufacturing operations. Security technologies, cited by 70%, are necessities with the continued risk of cybersecurity attacks in IT and OT environments.

Cited by almost 68%, cloud computing provides connectivity and can run applications and store data. And the Internet of Things (IoT), cited by 67%, connects sensors and devices to networks to take advantage of large amounts of real-time data. For example, if an assembly line is experiencing a disruption, products can be rerouted or delayed to help reduce wasted time and cost.

The combination of edge- and cloud-computing infrastructure provides localized optimization and connected assets for smart manufacturing. However, only 84% of respondents are in the earlier stages of edge computing maturity, with nearly half at the pilot stage and the rest having advanced to implementation in some production lines.

Executives rank the most important technologies driving success in manufacturing

<img alt="Executives rank the most important technologies driving success in manufacturing" height="176" src="https://s3.us-south.cloud-object-storage.appdomain.cloud/ibv/H_Zwi_D_Pc_QGJ_40_Msn_U3_O_o_DO_1c08e013a4" width="760" />

Not surprisingly, manufacturing respondents tell us their top objectives are increasing production yield, improving product quality, addressing sustainability, and reducing machine downtime. Yet, there is a gap between ambition and performance: Despite these objectives, only 36% of respondents are maintaining desired throughput and yield to a significant or very great extent.

However, our research revealed a group of organizations that are ahead of their peers in terms of digital transformation. Having instilled a data-driven culture for their organizations, these Data Transformers offer a model for other organizations to follow.

The Manufacturing 4.0 advantage

In addition to surpassing their peers in digital maturity, Digital Transformers also stand out due to superior business and operational performance. 71% say they outperformed their competitors in revenue growth over the last 3 years. Data Transformers also exhibit both decision-making and mass customization success.

<blockquote>
84% of Data Transformers have significantly integrated artificial intelligence (AI) and machine learning in their data platforms.
</blockquote>

Dependent on a transparent, efficient supply chain, Data Transformers have implemented AI to a greater degree than their peers for procurement and logistics. 50% have fully implemented or implemented at scale raw material/component/subassembly procurement, and 41% have done so for logistics.

By leveraging technologies to align business objectives with improved outcomes, these data mature organizations are distinguishing themselves through their insights, cyber resilience, enterprise architecture, manufacturing excellence, workforce capabilities, and digital integration.

Overall, Data Transformers stand out in 6 key areas:

&mdash; Tap the potential of data.

&mdash; Achieve cyber resilience.

&mdash; Create an enterprise architecture connecting the plant floor to business systems.

&mdash; Increase manufacturing excellence with technology.

&mdash; Embrace the manufacturing worker of the future.

&mdash; Integrate digital with manufacturing operations and management.

Cost-effective digital manufacturing solutions are needed to keep factories and supply chains running smoothly while producing high-quality products.

Download the report to discover how industry leaders are leveraging Manufacturing 4.0 for greater customer value and ongoing operational improvements that help drive productivity and profitability.

Leading manufacturers are leveraging Industry 4.0 technologies, such as AI, IoT, and automation, to uncover new data insights.

Manufacturing 4.0: From data to decisions

At first glance, it appears to be a bad way to woo customers&mdash;a warranty process that&rsquo;s used when things go wrong. Instead, it turns out to be an excellent opportunity for a company, not just to please its customers, but also to radically improve its business processes and find sources for future growth. For the automotive industry, as the traditional ownership model declines, companies are hungry for new revenue streams.

Recent IBM Institute for Business Value research found that on average, automotive original equipment manufacturers (OEMs) spend 2 percent of their annual revenue on paying warranty claims (claims rate). Their total warranty costs, including operations, are closer to 3 percent. For suppliers, these costs are 2.3 percent and 4.5 percent, respectively (see Figure below). The opportunity for improvement is significant.

&nbsp;

<img alt="" height="317" src="https://s3.us-south.cloud-object-storage.appdomain.cloud/ibv/3319bbe81c514c3bb737e3bb056e5698.jpg" width="720" />

But excessive warranty costs are only part of the picture. Money earmarked for warranty reserves to cover those costs can be substantial and difficult to accurately predict. To get a better sense of the issue, consider that, in 2018, total claims for global auto manufacturers were USD 56 billion, while they had USD 115 billion in reserves.

While cost reduction efforts can improve automotive company profitability, there is also value to be gained on the other side of the ledger. For example, one major auto OEM uses warranty data to improve its claims process and forecast parts usage. Time to identify non-compliant claims has decreased from three minutes to less than a minute, helping reduce labor costs by 52 percent.

But perhaps more important, forecasting parts usage allows the manufacturer to make sure dealers have in-demand parts available for repairs, making it more likely customers have an excellent experience and don&rsquo;t experience frustrating delays. Insights from warranty data can thus increase customer satisfaction.

What is data-powered warranty reinvention?

Analysis of warranty data can deliver benefits that extend beyond the warranty function. By integrating Internet of Things (IoT) technologies into manufacturing processes, and applying artificial intelligence (AI) and advanced analytics to the data they generate, automotive companies can gain valuable insights into their operations, products, suppliers, and customers. This can lead to the redesign and improvement of existing warranty processes and operations, customer experiences, actual products, and the workforce methods that produce them.

Analyzing warranty data can drive warranty process redesign, improve quality, make customers happier—even point to new products and services.

Data-powered automotive warranty re-invention

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AI is redefining business by combining the social, creative and leadership skills of the human mind with advanced technology. Our studies address the key strategic questions surrounding AI.

Artificial Intelligence (AI)

AI - Research, reports, and insights

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Across industries, companies are optimizing operations through automation and robotics. Our research illuminates the risks and opportunities.

Automation

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Our studies provide technology experts with actionable insights into optimizing mainframe computing, fostering digital transformation, and staying ahead in an increasingly competitive landscape.

Mainframe / infrastructure

Mainframe/infrastructure - Research, reports, and insights

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Our data-driven research identifies how businesses can locate and seize upon opportunities in the evolving, expanding field of generative AI.

Generative AI

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Organizations leveraging advanced AI deployments in their IT functions are achieving significant benefits.

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The ROI of AI-powered IT automation

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Our research-driven insights show how innovative enterprises are taking steps toward hybrid cloud mastery and reaping the benefits.