GenAI for creating code, content, and research is great, but I’m looking for use cases that drive digital transformation and can become business model game-changers. I’m looking for force multipliers – initiatives that can drive multiple outcomes, including growth plus efficiencies, innovation plus security, or customer plus employee experiences.

I’m looking for use cases that justify and ideally go beyond the hype.

Move beyond AI experiments to game-changers   

Deloitte’s State of Generative AI in the Enterprise reports that nearly 70% have moved 30% or fewer of their gen AI experiments into production, and 41% of organizations have struggled to define and measure the impacts of their gen AI efforts.

Keep up with the experiments, but It’s time for organizations to think big – but not moonshot – and find their one big idea to drive digital transformation.

When I run StarCIO digital transformation workshops, I help leaders move past a portfolio of too many tactical priorities to generate one game-changing idea.

I asked several experts for their promising AI use cases for this article. Some of these could be force multipliers, and with a dash of innovation and significant organizational change management, they can become game-changers.

1. Agentic AI to drive customer success

Can I vent? Over the last several weeks, I opened three tickets with different SaaS tools after encountering major issues.

• A security issue, which SaaS #1’s customer support helped me resolve quickly, though their product’s UX and customer communications need improvement so that other customers avoid similar issues.  
• An integration issue that SaaS #2 could not resolve without a fix from SaaS #3, but SaaS #2 provided excellent communication and support until the issue was resolved.
• SaaS #3 fought me around the issue, first claiming it was a problem with my automations and suggesting I recreate them. Two weeks and several escalations later, they realized the issue was theirs to fix.

So that’s the very good and very ugly of customer support. However, the real issue is that support didn’t lead to success, and all three SaaS companies missed the opportunity to learn my objectives and provide guidance beyond the immediate issue.

AI agents can drive a change in operations so that more support issues can be turned around into actionable product feedback and customer successes.

“AI agents represent the next evolution of AI, serving as a superset of assistants and advisors that offer insights, execute tasks, and automate workflows to drive efficiency and results,” says Raju Malhotra, chief product and technology officer at Certinia.com. “A key use case is semi-autonomous customer success agents, which augment and bring value to customer success teams by automating tasks instead of just advising or assisting them.”

2. Customer service AI agents in large call centers

How many times have you pressed pound to speak to an operator? Do you loathe rigid chatbot choose-your-own-adventures that generally solve only the basic issues?

My second game-changer is to recognize the importance of human-guided support and where AI agents can be used to partner with human agents or operate independently.

“AI agents can be invaluable counterparts for human customer service agents,” says Nikola Mrkšić, co-founder and CEO of PolyAI. “AI agents dramatically cut down on call volume by operating as the first line of defense for FAQs and other common inquiries. As long as the organizational data the AI agent pulls from is up to date; the AI agent will always be able to provide the most current, accurate information to callers. This shift in workload frees human agents’ time to focus on higher-level tasks, including professional development.”

3. Reimagine a faster and more strategic marketing organization

It’s amazing how many tools, skills, and people are needed to execute a marketing strategy, even with all the available marketing automation and integration tools. Fixing the data, integration, and automation challenges leads to the next question: How can marketers personalize their messages, scale their campaigns, and evolve tactics as fast as customer needs change?

“By 2025, marketers and sales leaders should aim to automate 80% of repetitive tasks, working with CIOs to integrate AI effectively,” says Raj De Datta, CEO of Bloomreach. “This shift lets marketers manage thousands of AI-driven customer journeys, focusing on high-value interactions while AI handles routine tasks like data entry and segmentation. The result is a reimagined marketing role that fully unlocks AI’s business potential.”

Reimagining marketing is one of my five recommendations for where CIOs should place their 2025 AI bets.

4. AI will back transformational changes in fintech payments

James Barrese, SVP of Fintech at Intuit, says that in 2025, the fintech landscape will be shaped by four key trends that will collectively transform how individuals and businesses interact with financial services, paving the way for a more inclusive and accessible financial ecosystem.

• A shift towards banking payments and a preference for instant payments will drive the demand for faster and more efficient money movement.
• Robust digital ID frameworks, potentially government-backed, will be essential for securing transactions and building trust in the digital economy.
• Embedded fintech solutions will continue proliferating, offering seamless financial services within non-financial platforms.
• AI agents will necessitate the development of payment infrastructures that can support AI-initiated transactions, further blurring the lines between technology and finance.

MPOV: Transformational technologies, including web, mobile, and cloud, were all major catalysts that evolved the back end of financial transactions and, most importantly, the user experiences.

5. Improve supply chain forecasting and issue management

Lisa Henriott, SVP of product marketing at Logility, says AI is truly changing how companies leverage their supply chain data for actionable insights. Henriott shares, “A compelling example comes from a leading specialty coffee and tea retailer. When the brand experienced a lag in forecast accuracy, it leveraged AI to improve short-term forecasting by 30% while reducing working hours to create the forecasting plan by 70%, improving bottom-line results and freeing their planners to perform other strategic decision-making and customer-centric activities.”

MPOV: 2025 will see major supply chain disruptions due to U.S. policy changes. Organizations need real-time data to improve forecasting, ML capabilities to identify supply chain risks, and agentic AI to scenario-play issue resolution options.

6. Finding defects in manufacturing and suspicious activity in retail

Over ten years ago, I wrote an article on how AI would solve IoT’s big data challenges. In 2025, AI running on the edge can be a game-changer for organizations where real-time sensing improves quality, reduces risks, or enables radically new customer experiences.

“Serverless AI inference at the edge, combined with retrieval-augmented generation (RAG) and vector databases, enables real-time and contextually-rich decision-making with reduced latency while removing complex management of underlying infrastructure,” says Kevin Cochrane, CMO of Vultr.

Cochrane shares two use cases:

• Manufacturers can enhance quality control by instantly detecting defects on production lines more accurately.
• Retailers can instantly detect and respond to suspicious in-store activity through live video feeds.

I’d love to see B2C retailers find game-changers in personalizing customer experiences and swing back from ecommerce to in-store differentiators.   

7. Automation to drive sustainability and address labor shortages

I recently wrote about how emerging tech, including dynamic work management, ML, and AI, can help address labor shortages in the construction industry.

Erik Nieves, founder and CEO of Plus One Robotics, says that as labor shortages and high wage demands strain the logistics and supply chain industries, the conversation will shift from “automation vs. job security” to “automation for job security.”

“By 2025, major companies and unions will increasingly acknowledge that automation is not a threat to employment but a solution to sustainability and growth,” says Nieves. “Large organizations will continue preparing to justify automation’s role in their operations, presenting robots as key to maintaining productivity and ensuring that workers are not replaced but upskilled.”

8. Developing new materials, proteins, and pharmaceuticals

While studying electrical engineering at Binghamton University, I worked in a material science lab during my sophomore year. After studying medical imaging, computer vision, and AI at The University of Arizona for my Masters, my first job was at a bioscience company where we developed one of the first (if not the first) commercial protein database software.

R&D in material science, protein synthesis, and drug discovery share a common challenge: experimenting with millions of permutations to find a solution that meets many criteria – in an affordable, safe, and compliant way. AI, ML, and automation are significant digital transformation opportunities.

 “AI is a powerful tool to help companies not only fuel but accelerate their digital transformation efforts by orders of magnitude,” says Mike Connell, COO at Enthought. “Just look at materials and chemistry R&D where materials informatics is now advancing the prediction and identification of new materials, bringing groundbreaking applications to market in record speed.”

Finding AI game-changers and transformation force  multipliers

Connell shares some excellent advice for StarCIO Digital Trailblazers.

“To realize ROI, it’s important for leaders to understand first and foremost why they want to use it and what it will help their business ultimately achieve,” says Connell. “The problem isn’t that AI is an ‘overhyped’ technology–far from it; it’s that large investments are often made in advanced technologies without the proper forethought for how it will drive value beyond incremental improvement.”

Review the eight examples I share, and you’ll find some commonalities around AI game-changers: sizable customer impact, a scale of solution that transforms the workforce, the need for quality data at scale, and a challenging change management effort.

I’m happy to chat with you about your organization’s challenges!