As artificial intelligence moves from buzzword to business reality, major corporations are sharing their strategies for transitioning AI pilots into operational use cases. At a Business Insider Workforce Innovation roundtable, executives from Accenture, Infosys, Mastercard, IBM, JLL, and AARP discussed the challenges and successes of AI integration across their organizations.
Jack Azagury, group chief executive for consulting at Accenture, highlighted a critical industry problem: “A lot of our clients have dozens of AI pilots everywhere. Very few have a coherent business case and a true reinvention and transformation.” This observation underscores the gap between AI experimentation and meaningful implementation.
The roundtable participants identified five major strategies for moving AI from theory to operations:
1. Starting with HR as an early adopter: IBM’s Justina Nixon-Saintil reported that their AskHR product answered over 94% of employee questions, freeing HR staff for higher-order work. Mastercard’s Lucrecia Borgonovo emphasized that HR has been leading the way in embedding AI to enhance employee experience from recruitment through post-employment.
2. Investing in continuous training: Infosys’s Anant Adya described mandatory AI certifications and courses for all employees, focusing on education about generative AI, large language models (LLMs), and use cases. AARP’s Marjorie Powell shared that they posted over 20 AI workshops in the past year, encouraging staff to experiment with AI tools behind their firewall in a safe environment.
3. Unlocking peer-to-peer learning: AARP implemented idea pitch competitions and a year-round idea pipeline program, along with a generative AI community of practice that meets monthly. JLL’s Neil Murray identified “super users” across departments whose advanced prompts now appear in pull-down menus to help less experienced users.
4. Solving customer pain points: Mastercard deployed chat-based assistants for customer onboarding processes, streamlining hundreds of pages of documentation and creating greater efficiency.
5. Reinforcing responsible leadership: Companies emphasized the importance of guiding employees to use AI effectively and responsibly, with particular focus on privacy, policy, and efficiency.
A key insight from Mastercard’s pilot with coding assistants revealed that software engineers who received training performed significantly better, highlighting the critical importance of prompt training before rolling out AI platforms. Borgonovo noted that knowledge management and software engineering are seeing the greatest internal demand for AI tools.
Key Quotes
A lot of our clients have dozens of AI pilots everywhere. Very few have a coherent business case and a true reinvention and transformation.
Jack Azagury, group chief executive for consulting at Accenture, identified a critical challenge facing businesses today: the gap between AI experimentation and meaningful implementation. This observation highlights why strategic planning is essential for AI success.
Before we go and tell our clients to embark on AI fully, we want to be an AI-first organization. We want to show our clients we are using AI, whether it is in HR when it comes to driving better employee experience or when it comes to recruitment.
Anant Adya, executive vice president at Infosys, emphasized the importance of practicing what you preach. By becoming an AI-first organization internally, Infosys can provide credible guidance to clients based on real-world experience.
We implemented AI across our business and multiple functions, and one of the first things we did was our AskHR product, which I think answered over 94% of questions employees had.
Justina Nixon-Saintil, vice president and chief impact officer at IBM, shared concrete results from IBM’s AI implementation. The 94% success rate demonstrates how AI can effectively handle routine inquiries while freeing human workers for more strategic tasks.
One of the really interesting learnings from this pilot was that the software engineers who were using the coding assistants probably the best were people who had received training.
Lucrecia Borgonovo, chief talent officer at Mastercard, revealed a critical insight from their coding assistant pilot: training is essential for maximizing AI tool effectiveness. This finding underscores the importance of prompt engineering education before deploying AI platforms.
Our Take
What’s particularly striking about this roundtable is the consensus around human-centered AI implementation. Rather than rushing to deploy AI everywhere, these Fortune 500 companies are taking measured, strategic approaches that prioritize employee training and experience. The revelation that trained software engineers performed significantly better with coding assistants challenges the notion that AI tools are inherently intuitive. This suggests that prompt engineering and AI literacy will become essential workplace skills, not optional add-ons. The emphasis on HR as the proving ground for AI is also telling—it’s a function where mistakes have limited external impact but success can be easily measured and scaled. Perhaps most importantly, these leaders are framing AI as augmentation rather than replacement, which may prove crucial for employee buy-in and successful adoption. The peer-to-peer learning models and communities of practice they describe could become the standard playbook for enterprise AI transformation.
Why This Matters
This roundtable discussion provides crucial insights into the practical realities of enterprise AI adoption at a pivotal moment in the technology’s evolution. While generative AI dominated headlines throughout 2024, many organizations struggled to move beyond experimental pilots to achieve measurable business value. The strategies shared by these Fortune 500 companies offer a roadmap for successful AI integration that prioritizes employee experience, continuous learning, and responsible deployment.
The emphasis on HR as an early adopter signals a significant shift in how companies approach workforce transformation. Rather than viewing AI as a threat to jobs, these leaders frame it as a tool for amplifying human potential and eliminating repetitive tasks. The focus on comprehensive training programs and peer-to-peer learning addresses the critical skills gap that threatens to slow AI adoption. Most importantly, the measured approach to deployment—exemplified by Mastercard’s coding assistant pilot—demonstrates that successful AI integration requires intentional planning, training, and iteration rather than rushed implementation. This practical wisdom from industry leaders will likely influence how thousands of other companies approach their own AI transformation journeys in 2025 and beyond.
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