Whether you are starting out or highly successful in terms of deployments made and value gained, choosing the right use case can help create early wins, speed widespread adoption, and bolster support from leadership.

Select a maturity level to explore top use cases by industry.

As a group, Pathseekers are achieving widespread value from relatively few deployments of AI, indicating they’re adopting what’s proven and aligning to leading practices. Common successes include cost reduction and efficiency. Next step? Generating revenue and driving innovation.

While Consumer Pathseekers also deploy AI tactically, they place greater emphasis on forward-looking applications like predictive maintenance in supply and distribution and customer feedback analysis—two deployment areas that can lend themselves to more immediate value creation.

Pathseekers in LSHC seem to emphasize tactical use cases, such as internal audit and sales. The fact that they’re achieving high outcomes based on that focus suggests the low-hanging fruit for AI in the LSHC industry may be day-to-day functional applications.

For Pathseekers, the top three use cases all focus on areas of optimization. Given this group’s stated ability to achieve high outcomes and widespread value from AI, this may well indicate that optimization is where faster, surer AI payoff resides within the ER&I industry.

Operational efficiency applications are top among TMT Pathseekers. An emphasis on streaming services and cloud may in part be driving their choices and the value of those use cases. Back-end operational use cases are logical choices, given the rush to standardize business practices within the industry.

FS is highly regulated and competitive. Pathseekers focus their AI efforts in these two areas predominantly. Top use cases include financial reporting, R&D and cash forecasting, with contact center optimization and personalization not far behind (see full report).

As in all industries, GPS Pathseekers are laser-focused on applying AI where value can be generated quickly. They’re doing that by deploying AI to help manage costs and enhance performance across a range of assets and infrastructure—technological and traditional.

Transformers have largely adopted the practices associated with the strongest AI outcomes and are in the process of transforming and creating value across their enterprise. Many have moved beyond cost reduction and are now innovating products and generating revenue from AI.

Consumer Transformers are much more focused on higher-impact AI deployments such as personalization and optimization, and they implement across a wider range of domains. These tend to pay off sooner and in more discrete, measurable ways.

LSHC Transformers, like Pathseekers, focus on practical use cases (recruitment, audit, customer service) rather than innovative ones to a greater degree than expected. Their outcomes reinforce practicality as a way to gain value faster from AI.

Transformers in ER&I pursue optimization, affirming that such use cases offer value. But these AI leaders go further to drive value in financial reporting and accounting as well as in customer service. This speaks to having organizational advantages over low-outcome ER&I organizations.

TMT had the greatest percentage of Transformers in our survey. This maturity is likely due to telecommunication's long-standing focus on operational efficiency and media’s rapid uptake of digital marketing. Beyond the top cases, Transformers also use AI for experimentation and R&D (see full report).

Interestingly, FS Transformer use cases are less focused on customer acquisition and retention, overtly. Rather, they prioritize cash and cost management and uninterrupted operations—both vitally important to creating seamless and elevated experiences for customers.

Agencies within GPS vary widely in their missions. Transformers adopt AI accordingly, but with a focus on generating value from the core of what they deliver, out. Top use cases include enhancing reliability, safety and maintenance, as well as environmental modeling (see full report).

Starters may be behind in terms of deployments and outcomes, but each has an opportunity to rapidly advance their learning curve by adopting leading practices and leaning into packaged solutions proven in their industries and based on drivers of value in their companies.

Starters within the Consumer industry show a greater focus on fundamental applications like customer interaction and IT management, reflecting a less-evolved state of AI maturity. Some starters are showing instincts toward more evolved applications like predictive maintenance.

Top use cases for Starters in LSHC show an emphasis on industry-specific, tactical applications. But a broader view (see full report), shows Starters often tackle difficult use cases too soon, which may impede higher rewards and faster returns on AI.

AI has made fewer inroads in ER&I than in other industries, with back-office applications of AI most prevalent. Starters bear this out, using AI for tech optimization and recruiting, although areas like predictive maintenance and conversational AI are on the rise (see full report).

As an industry, TMT’s application of AI tends to be customer-centric, with many prepackaged solutions available to choose from. Not surprisingly, Starters in the TMT space often begin their AI journeys focusing on areas that create value through customer acquisition and retention.

Starters in the highly regulated and customer-centric FS industry tend to find early value in applying AI at the nexus of customer support and regulatory compliance, such as applications that keep systems updated, customer-facing chatbots, and financial reporting and accounting.

Throughout GPS, organizations are finding great value where AI creates efficiencies through automation and optimization. Starters tend to begin their AI journey there, with a focus on data intelligence, price optimization and process automation.

These large companies are rapidly deploying AI; however, they haven't adopted the leading practices required to generate value and impact in all cases. Such companies should focus on what drives value for their businesses and look to packaged solutions and services to gain wins.

Consumer Underachievers, like Starters, tend to focus on tactical and operational AI applications primarily in CX, but in greater deployment numbers. As the title implies, Underachievers have yet to successfully drive significant or widespread value with AI despite a high number of implementations.

LSHC Underachievers tend to pursue a mix of tactical and forward-looking applications among their top use cases, but in greater numbers across more domains than Starters. This decision to push in multiple directions at once can impede the ability to achieve consistent results.

Top use cases for Underachievers mirror those for Starters. Go deeper (see full report) and you find personalization and workforce scheduling optimization uniquely high on their lists. Their lack of success in those areas may speak to the industry’s unpredictability as much as to each company's AI maturity.

Underachievers mirror the rest of TMT in their focus on customer-centric AI. Interestingly, the percentage of Underachievers in TMT was the highest in our survey. One reason? New players in TMT often deploy AI rapidly and broadly but lack the leading practices to generate sustained value.

Like all of FS, Underachievers deploy AI frequently in customer use cases—chatbots, customer service and personalization (see full report). But in the rush to compete for customers, they may also be deploying too broadly without the operational and cultural support required for sustained value.

Underachievers, more than the other cohorts in GPS, focus AI on citizen services. Such applications are not new to GPS, but they are proliferating quickly, resulting in a landscape of options that create fragmentation across departments and can impede value.