Vivek Jetley is President and Chief Analytics Officer at EXXLa global leader in data, analytics and AI for Fortune 500 companies.
Some cracks are beginning to appear in the hype cycle around generative artificial intelligence (GenAI). After reaching “peak expectations” in August 2023, many companies have begun to struggle when it comes to extracting real, meaningful value from the technology. Now is the time to address these concerns head on or face a long road of slowly losing and rebuilding trust in the tools that leverage GenAI.
To get a better idea of where the world currently stands regarding overall perceptions of GenAI and specific concerns regarding its implementation in the professional setting, we recently surveyed 158 senior executives, vice presidents and directors “engaged in strategy, technology and business processes of the 20 largest non-bank lenders, 100 largest insurers and Tier 1, 2 and 3 financial institutions.”
It’s no surprise that, given all the attention being paid to the potential of generational artificial intelligence (GenAI) to transform knowledge work, the vast majority of professionals are excited about its prospects. A total of 91% of respondents have launched GenAI solutions of some type in the past year. The specific business functions attracting the most attention are product development (93%), customer service/customer experience (82%), human resources (82%), and corporate strategy (75%).
The GenAI revolution has a data problem.
Despite the widespread enthusiasm, many companies are beginning to face challenges in extracting tangible value from these initiatives as they move from the pilot phase to full-fledged enterprise solutions. Many companies report a significant decrease in the number of users of their GenAI tools, and others are suggesting that their GenAI pilot projects are not meeting their ROI targets.
In almost every case, the root cause of the disconnect between the promise of GenAI and the reality of implementing it in a real business context is the lack of seamless integration between AI-powered solutions and enterprise workflows. In fact, according to our survey, less than half of respondents said that any of their AI-powered business functions were integrated with at least one other business function. The reason for this is data silos.
Nearly three-quarters (74%) of respondents who have implemented AI pilots in their organization said data silos are the biggest barrier to integrating AI across the enterprise. Of this group, 33% said data is siloed within each business function, and 41% said data is siloed within some business functions but shared across others. In both cases, the bottom line is the same: the biggest barrier to GenAI initiatives reaching their full potential within large enterprises is data.
AI outcomes start with a data strategy.
It is now accepted that data is critical to the accuracy of AI results and is the foundation upon which any successful AI is built. The more reliable the AI results, the more valuable the solutions are to end users.
While everyone seems to understand this concept, very few companies have successfully done the data governance and management work needed to enable the free flow of data across business functions and the software platforms that enable GenAI to synthesize vast volumes of information and support enterprise-wide initiatives.
This process won’t happen automatically if companies blindly buy off-the-shelf chatbot technology and let it run wild with their customers. It requires significant internal planning and choreography between customer-facing staff, data and analytics teams, and software development teams to bring together all the disparate data needed to provide a comprehensive 360-degree view of the customer that can be used to train the system.
Consider customer service co-piloting tools, which are one of the most common use cases for GenAI and have shown promise in the insurance and financial services industries. These solutions, which deploy GenAI-based agent assistance technology to monitor live customer support interactions and simultaneously search the company’s knowledge base to provide real-time guidance to customer service staff, help reduce the time spent searching for answers and improve the overall customer experience. A simple example is when a customer calls to file an insurance claim and the AI co-pilot instantly retrieves that customer’s complete claim history, details about other bundled policies, information about local repair resources, and previous records of customer service interactions.
In the old world of claims, each of these pieces of information would have been housed in different systems with different levels of administrative access and different formats. As a result, the customer was often put on hold, transferred to multiple reps, and forced to repeat everything at every step of the way. GenAI-powered co-pilots have the power to remove this barrier, putting all relevant information at a rep’s fingertips in real-time.
However, this only works if the database behind the scenes can support this level of seamless information transfer. Achieving this requires a focused effort and close collaboration to connect all these disparate data sets into a centralized technology architecture. Without it, the GenAI co-pilot will be no more effective than a human customer service representative who has to transfer a call to another department.
For companies that understand the critical role data management plays in realizing the promise of AI, the gains in efficiency and customer experience they see are immediate. For those that rush to build GenAI tools on a broken database, disillusionment is in store.
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