You wouldn’t be reading this if you didn’t have the sense that AI is the future. But the breadth of solutions can be so paralyzing that it stops business leaders cold. Firms like PricewaterhouseCoopers and McKinsey predict that the global AI economy will grow to $13-15 trillion by 2030. But today only 9% of US companies use any form of AI like machine vision and natural language processing.

Despite all of the good news surrounding AI, adopting an AI solution is not a simple decision to make. In fact, many organizations don’t make the decision at all. They wait for a compelling event to drive them to adoption.

A sudden influx of customer calls to a call center, for example, might show business leaders that their systems are not built for the stress of the current world. Customers today have 24/7 access to your company via their tablets, laptops, and smartphones. About 47% of customers will abandon your brand if they have an unsatisfactory customer experience, which is why so many organizations have become laser-focused on providing excellent customer service.

Case study: Crédit Mutuel

French bank Crédit Mutuel found their compelling event when looking through their data. They discovered that client advisors were spending a significant chunk of their day answering repetitive and straightforward questions via email. A large percentage of customer service tasks could be automatically taken off their human agents’ plate and instantly resolved by a virtual agent. Once Watson was adopted it became possible for agents to find the right answers to customer’s questions 60% faster, and client advisors found that they had more time to focus on higher value clients.

Of course, the business landscape is ever-changing. Today, we are all trying to understand how the current public health crisis will affect work in the future. Due to COVID-19, many companies have had to rethink how they work entirely. They’re keen to deploy an AI solution because they need to remain competitive. They can no longer rely on a compelling event, because it’s no longer a question of “if we adopt an AI solution,” it’s “when we adopt one.”

Case study: Clerk of the Superior Court in Maricopa County

Maricopa County is a perfect example of a post-COVID transformation. The Clerk of the Superior Court in Maricopa County, based in Phoenix, Arizona, receives thousands of unique service requests from its 4 million residents. Citizens often reach out with time-sensitive inquiries, everything from requesting a marriage license to accessing court records. The Clerk of the Superior Court doesn’t deal with consumers in a competitive industry — but like a business, their goal is to provide services to their citizens in the most efficient and cost-effective way possible. Our Data and AI Experts supported the Clerk’s Office in building a virtual assistant powered by watsonx Assistant. We developed the initial set of intents, customized and stood up the environment, and trained their team in best practices on how to manage, run and maintain the virtual assistant going forward.

The Clerk’s Office deployed their virtual assistant just as the COVID-19 outbreak was beginning. Due to the state’s shelter-in-place order, the Clerk’s Office’s virtual agent was critical in answering questions and helping citizens file documentation without going to the court in person or waiting on the phone. In just the first month, watsonx Assistant handled around 70% of conversations without human intervention, and agents found they saved 100 hours of direct handling of inquiries.

Start planning your AI transformation

For a little more perspective on how effectively AI solutions scale, we commissioned an independent study on the total economic impact of watsonx Assistant and found that organizations using watsonx Assistant had an ROI of 337% over three years.

So my honest advice is to start planning your AI transformation right now. There’s no reason to wait for a compelling event to start your business on its AI journey. If you’re unsure where to begin, take a look at the data you already have on hand, like Crédit Mutual did, to find any easy wins, repetitive or time-consuming tasks. Next, try thinking about the outcomes you’d like to see as a result of streamlining or eliminating these tasks. For a deeper dive into thinking about outcomes, read the previous posts in this series by my colleague Ritika Gunnar.

If you’re not quite sure what your business use case could be, check out our webinar on building your business use case.

Read my next post to learn the importance of breaking apart your processes.

Talk to one of our experts

Was this article helpful?
YesNo

More from Artificial intelligence

Conversational AI use cases for enterprises

10 min read - Today, people don’t just prefer instant communication; they expect it. Conversational artificial intelligence (AI) leads the charge in breaking down barriers between businesses and their audiences. This class of AI-based tools, including chatbots and virtual assistants, enables seamless, human-like and personalized exchanges.Beyond the simplistic chat bubble of conversational AI lies a complex blend of technologies, with natural language processing (NLP) taking center stage. NLP translates the user’s words into machine actions, enabling machines to understand and respond to customer inquiries accurately. This…

Operationalizing responsible AI principles for defense

7 min read - Artificial intelligence (AI) is transforming society, including the very character of national security. Recognizing this, the Department of Defense (DoD) launched the Joint Artificial Intelligence Center (JAIC) in 2019, the predecessor to the Chief Digital and Artificial Intelligence Office (CDAO), to develop AI solutions that build competitive military advantage, conditions for human-centric AI adoption, and the agility of DoD operations. However, the roadblocks to scaling, adopting, and realizing the full potential of AI in the DoD are similar to those…

Unlocking financial benefits through data monetization

6 min read - Data monetization empowers organizations to use their data assets and artificial intelligence (AI) capabilities to create tangible economic value. This value exchange system uses data products to enhance business performance, gain a competitive advantage, and address industry challenges in response to market demand. Financial benefits include increased revenue through the creation of adjacent industry business models, accessing new markets to establish more revenue streams, and growing existing revenue. Cost optimization can be achieved through a combination of productivity enhancements, infrastructure…

IBM Newsletters

Get our newsletters and topic updates that deliver the latest thought leadership and insights on emerging trends.
Subscribe now More newsletters