Take advantage of AI and use it to make your business better

Businesses across industries have moved beyond asking “Should we adopt AI?” to “How can we integrate AI technology strategically?” The answer lies in understanding both the challenges and the immense benefits AI can bring to modern enterprises.

MIT’s State of AI in Business 2025 report concludes that 95% of AI pilots are failing to deliver measurable business results. Why? It’s not necessarily the AI itself, it’s the poor integration with existing workflows, the unclear objectives from leaders rushing to “use AI” and the immense organizational readiness gaps. To overcome these challenges requires a shift process, business models and mindset.

The challenges can feel so great and the unknown so scary that the benefits of using AI tools are sometimes hard to see. Let’s delve into the transformative benefits of AI in business and how to use them to reach real-world business value.

AI uses internal and external data to optimize processes, eliminate repetitive tasks and enable smarter decisions. These benefits work together—cutting costs, driving innovation and strengthening outcomes through predictive analytics—all supported by AI-driven risk management. Here are seven key benefits with real-world success stories.

AI enhances decision-making by quickly processing large volumes of structured and unstructured data, identifying patterns and delivering data-driven insights. AI systems can analyze historical trends, market conditions and predictive indicators to recommend optimal actions. This support helps business leaders make more accurate, informed decisions.

Generative AI use cases

• Natural language business intelligence queries: Ask questions in plain language and get instant insights
• Automated reports: Summarize complex data into executive-ready reports
• Scenario modeling: Use generative AI to create multiple business scenarios based on different variables

Agentic AI use cases

• Autonomous market analysts: Monitor markets, competitors and economic trends to recommend strategic actions
• Dynamic pricing agents: Adjust pricing automatically through real-time supply, demand and competitor data
• Decision-support agents: Gather information, evaluate options and provide ranked recommendations with evidence

Real-world story: Bradesco Bank (Brazil)

Bradesco, one of Brazil’s largest banks, implemented an AI-based credit decision system that evaluates thousands of data points to assess risk. It automated 95% of credit analyses, reduced decision time from days to minutes, improved accuracy in identifying creditworthy customers and lowered default rates. The system scales credit operations, speeds customer decisions and lets analysts focus on complex cases requiring expert judgment.¹

AI automates repetitive, time-consuming tasks—often prone to human error—which allows employees to focus on higher-value strategic initiatives. It operates all day without fatigue, processes information at scale and can handle multiple tasks simultaneously.

Generative AI use cases

• Content creation: Generate marketing materials, product descriptions and documentation
• Code generation and debugging: Accelerate software development
• Email automation: Draft and respond to emails while maintaining a brand voice
• Document summarization: Condense lengthy reports into key takeaways

Agentic AI use cases

• Autonomous workflow orchestration: Coordinate complex processes across departments
• Self-optimizing supply chains: Adjust inventory, routing and procurement automatically
• AI recruiting agents: Source candidates, screen resumes, schedule interviews and manage communications

Real-world story: Franchise Brokers Association (FBA)

The FBA, a leader in connecting aspiring business owners with franchise opportunities, had to manually review and transcribe franchise disclosure documents (FDDs), often at 200–400 pages each. This time-consuming, error-prone process limited how many listings they can review each week.

By adopting IBM’s AI-powered automation platform, watsonx Orchestrate®, FBA can automatically extract key data from each FDD, convert it into structured data and push it into their CRM. This dropped listing creation time by 75% (from ~4 hours to ~1 hour) and reduced calculation errors to zero.²

AI personalizes customer interactions at scale, provides instant support, anticipates needs and creates seamless omnichannel experiences. By using real-time insights, companies can deliver hyper-personalized customer journeys, leading to increased customer engagement and higher customer satisfaction.

Generative AI use cases

• AI-powered chatbots and virtual assistants: Provide human-like customer service by understanding context, sentiment and intent
• Personalized recommendations: Suggest product recommendations based on individual behavior and preferences
• Dynamic content creation: Deliver tailored website content, emails and offers to each customer
• Voice-of-customer analysis: Process feedback to identify improvement opportunities

Agentic AI use cases

• Proactive customer service agents: Detect issues early and initiate resolutions automatically
• Personal shopping assistants: Learn preferences and autonomously curate products
• Customer journey orchestrators: Adjust touchpoints across channels to optimize engagement
• Autonomous problem-solving agents: Troubleshoot technical issues end-to-end without human intervention

Real-world story: Lowe’s (home improvement retailer)

Lowe’s used IBM® watsonx Assistant® to support customer service across web, mobile and in-store kiosks. Customers can describe projects in natural language, and the AI recommends products, offers guidance and checks inventory.

The system handles inquiries autonomously, cuts handling time, boosts customer satisfaction and scales volume during peak seasons. Employees handle more complex questions, reducing stress and turnover while increasing job satisfaction. Beyond these benefits, Lowe’s realized a 10% reduction in legacy applications and a 25% reduction in cloud costs.³

AI deployment typically reduces operational costs through automation, optimization and prevention of costly errors. Cost savings come from reduced labor for routine tasks, decreased error rates, optimized resource allocation and predictive maintenance that prevents expensive failures.

Generative AI use cases

• Automated customer support: Cut call center staffing needs
• Legal document analysis: Complete in hours what once took weeks
• Automated financial reporting: Eliminate manual data-entry and reconciliation
• Training material generation: Reduce content development costs

Agentic AI use cases

• Autonomous IT operations (AIOps): Self-healing systems to prevent downtime
• Energy optimization agents: Continuously adjust building systems to reduce utility costs
• Procurement agents: Negotiate with suppliers and identify cost-saving opportunities
• Fraud detection agents: Block suspicious transactions to prevent financial losses

Real-world story: IBM as Client Zero

IBM launched “IBM as Client Zero” in 2023, a company-wide transformation designed to radically boost productivity by embedding AI, hybrid cloud and automation across the enterprise.

Simplifying, automating and accelerating workflows had a major impact on HR operations. Instead of employees waiting for hours on hold or days for email replies, an AI-powered tool called AskHR now handles 94% of common employee inquiries. It delivers answers in minutes at any time of day. And managers are now able to complete HR-related tasks such as employee promotions an estimated 75% faster.⁴

A common phrase about AI is that it’s only as good as the data that shapes it. Therefore, effective business AI requires strong data governance and risk management.

AI enhances risk management by spotting patterns humans might miss. It monitors compliance, detects fraud, identifies security threats and predicts operational failures. AI-powered systems can detect threats faster than traditional methods and reduce false positives.

Generative AI use cases

• Compliance monitoring: Scan communications and transactions for potential violations
• Risk scenario generation: Create risk scenarios with mitigation strategies
• Policy analysis: Ensure that contracts comply with regulations
• Threat intelligence summaries: Convert security feeds into actionable insights

Agentic AI use cases

• Autonomous cybersecurity agents: Detect, investigate and respond to threats in real time
• Fraud prevention agents: Learn new fraud patterns and automatically block suspicious activity
• Compliance agents: Autonomously audit processes and proactively flag violations
• Predictive risk agents: Assess operational risks and recommend preventive actions

Real-world story: Wimbledon (The Championships)

For Wimbledon, IBM implemented AI-driven risk management and security systems that monitor millions of daily security events and respond in real time. In 2023, the AI autonomously detected and blocked a sophisticated ransomware attack targeting livestreams millions of viewers. It identified the threat pattern, avoiding millions of British pounds in damages and reputational harm.⁵

AI accelerates innovation by augmenting human creativity, rapidly prototyping ideas, discovering novel patterns in data and automating the experimentation process. AI enables companies to test more hypotheses faster, reduces time-to-market for new products and uncovers insights that lead to breakthrough innovations.

Generative AI use cases

Moving far beyond simple text generation, generative AI acts as a creative partner that amplifies human capabilities, accelerates production cycles and opens entirely new possibilities for innovation.

• Product ideation: Generate new product concepts based on market trends and customer needs
• Research synthesis: Analyze thousands of papers to uncover new research directions
• Drug discovery: Accelerate healthcare development by generating molecular structures with defined properties
• Content creation: Draft blogs, social media posts, ads, human resources job postings, contracts, proposals, reports, emails and video scripts that reflect the brand voice
• Product design and prototyping: Explore designs, optimize for parameters and visualize concepts before prototyping
• Code generation and modernization: Write code through natural language processing (NLP), translate legacy code, generate tests, find bugs and suggest optimizations
• Data analysis narratives: Turn complex data into clear insights and recommendations
• Training materials: Quickly create educational content, onboarding guides and how-to materials

Agentic AI use cases

• Autonomous research agents: Formulate hypotheses, design experiments, analyze results and iterate independently
• Innovation scouting agents: Monitor patents, startups and research to spot new opportunities
• Product development agents: Prototype, test and refine designs autonomously
• Materials discovery agents: Explore material combinations to identify optimal properties

Real-world story: Coca-Cola

Coca-Cola has explored generative AI in its marketing strategies, employing it to create personalized campaigns at scale. By analyzing consumer preferences, trending topics and brand guidelines, AI generates customized creative assets for different markets, demographics and channels.

This approach enables creativity and innovation around personalization and value creation that might have been impossible to achieve manually. Ultimately, this reportedly improved engagement rates and strengthened brand loyalty across diverse global audiences.⁶

Predictive analytics uses AI to forecast future outcomes based on historical data, enabling proactive rather than reactive business strategies. Organizations that use predictive analytics see improvements in forecasting accuracy, enabling better inventory management, workforce planning and strategic positioning. The technology identifies trends before they become obvious, giving companies competitive advantages.

Generative AI use cases

• Natural language forecasting: Generate predictions with confidence levels and explanations
• Scenario-based forecasting: Create multiple future scenarios with probabilities
• Automated insights: Translate complex models into business language
• Anomaly explanation: Explain why predictions deviate from expectations

Agentic AI use cases

• Autonomous demand planning agents: Continuously forecast demand and adjust production schedules
• Predictive maintenance agents: Forecast equipment failures and automatically schedule maintenance
• Churn prediction agents: Identify at-risk customers and trigger retention actions
• Market forecasting agents: Predict market movements and autonomously adjust business operations and strategies

Real-world story: Dun & Bradstreet (data and analytics)

D&B, known for its trusted business‑risk data, teamed with IBM to build Ask Procurement, an AI assistant. The tool combines D&B’s Data Cloud (hundreds of millions of public and private business records) with IBM’s advanced AI and automation capabilities. By dramatically reducing the need for manual research and data-entry, Ask Procurement reduces the time spent on procurement tasks by an estimated 10–20%. Teams now get a 360° view of potential suppliers with trusted data, real-time analytics and AI-powered summarization.

As Gary Kotovets, Chief Data and Analytics Officer at D&B, said, “Our team was impressed by the depth of experience the IBM team brought to the table and the capabilities available through watsonx Orchestrate.” His words highlight IBM’s expertise and advanced AI capabilities.⁷

According to Ann Funai, CIO and VP of Business Platform Transformation at IBM, “AI on top of crap is just AI-powered crap.” Like all systemic change, realizing the full benefits of AI applications is a step-by-step process. This process helps companies create a clear business strategy and build out AI capabilities in a thoughtful, fully integrated way. These steps should include:

Companies that adopt the use of AI to effectively and ethically improve business operations and drive revenue gain a competitive edge. Critical considerations for building your AI first strategy include:

• How will AI deliver business value? Identify how different AI platforms support your goals—for example, improving customer experiences and operational efficiency by optimizing workflows in areas like customer service, supply chain management and cybersecurity.
  
  
• How will you empower teams to tap into your data? For successful data democratization, treat data as a product and use hybrid cloud to unify data across all environments so teams can access and scale AI effectively. Prioritize the data that’s most critical and provides the strongest competitive advantage.
  
  
• How will you ensure that AI solutions are trustworthy? Establish strong data management and AI governance to help ensure accuracy, fairness, transparency and privacy, avoiding reputational, financial and regulatory risks.

Modernizing your data architecture is foundational to becoming an AI-driven organization. AI workloads need flexibility—spanning public cloud for scale, private environments for sensitive data and edge for low-latency use cases. A hybrid multicloud approach gives you the choice and portability to run AI where it delivers the most value. This strategy also helps to ensure that you can scale compute resources efficiently and balance performance with cost as adoption grows.

A modern data fabric further accelerates AI readiness by unifying access to data across on-premises systems, clouds and SaaS apps. It streamlines discovery and classification, enforces consistent governance and provides real-time, trustworthy data to AI systems. Together, hybrid multicloud and a data-fabric architecture create an elastic, composable foundation for enterprise-wide AI integration.

A truly effective AI program requires clean quality datasets. Your organization’s digital systems must reliably capture the right data at key touchpoints, and that data must be accessible to the teams performing data analysis and building and refining AI models. This approach includes cleaning and standardizing data before feeding it to AI models trained with machine learning algorithms. It also involves maintaining pipelines that keep datasets current, tracking versions of training data and monitoring for drift that can degrade model performance over time.

Proper data governance helps organizations build trust and transparency, strengthening bias detection and decision-making. Effective governance helps ensure that data is accurate, well-managed and appropriately accessed, while also documenting how it moves and transforms through systems. It helps organizations to meet regulatory requirements, protect privacy, detect and mitigate bias and ensure that AI decisions remain explainable and accountable—especially in high-stakes applications. When data is accessible, trustworthy and accurate, it enables companies to more effectively implement AI throughout the organization. 

Humans should be at the beginning, center and end of AI adoption. Once a strong data strategy is in place, organizations need employees who understand AI concepts, can prioritize high-value use cases and can work effectively with new tools. This effort includes upskilling teams in AI literacy and data analysis, and building cross-functional groups that pair technical talent with domain expertise. It also involves fostering a culture of experimentation and teaching employees how to use AI assistants and automation platforms to boost productivity.

AI in business holds the potential to improve a wide range of business processes and domains, especially when the organization takes an AI-first approach.

Over the next five years, businesses will accelerate AI adoption by focusing on areas where the technology is rapidly advancing, including digital labor, IT automation, security, sustainability and application modernization. A key driver of this growth will be the rise of agentic AI and AI agents.

Agentic AI is the next frontier: autonomous systems that can reason, plan and execute complex tasks with minimal human oversight. Unlike traditional AI, which relies on prompts or follows predefined workflows, AI agents can set their own goals, develop strategies, adapt to new information and make independent decisions. They function more like autonomous collaborators than tools.

For businesses, AI agents unlock significant advantages. They can manage end-to-end processes, operate around the clock and execute decisions at a speed and scale far beyond human capacity, all while applying consistent rules and best practices. They are a powerful force that enables organizations to operate more efficiently, intelligently and resiliently.

Ultimately, success with new technologies in AI will rely on the quality of data, data management architecture, foundation models and trustworthy, transparent governance. With these elements—and business-driven, practical objectives—leaders and their teams can realize the scalable benefits of using AI in business.