Inside How JP Morgan Chase, Walmart, and AT&T Bet on AI Upskilling

On a typical workday at Genpact, the most important AI transformation wasn’t happening in a lab or a hiring pipeline. It happened quietly, within the flow of everyday work. As generative AI gained momentum, the folks at Genpact faced a familiar fork in the road: join the escalating war for scarce AI talent, or upskill the people it already had. The company chose the latter, which may have appeared to be the slower path, but ultimately proved faster.

Genpact embedded the need for AI upskilling into the organization's very fabric. For instance, its internal learning platform, Genome, was built around practical AI skills tied directly to real roles, promotions, and career progression. Genpact made AI upskilling social, gamified, and inseparable from career advancement. 

“We find that they [employees] want to go and lap up a lot more [knowledge].”

 - Shalini Modi, Global Leader of Employee Learning at Genpact

As a result, more than 90,000 employees were upskilled in AI, accompanied by a measurable shift in behavior: higher engagement, stronger retention, and broader AI fluency across teams.

Genpact is not alone. Genpact’s experience is one of several upskilling and reskilling examples that show how enterprises can build AI capability. Across industries, enterprises are asking the same question: How to upskill in artificial intelligence? Increasingly, the answer is not to hire more specialists, but to equip existing employees with practical AI skills they can use in their daily work.

Rather than relying solely on recruiting scarce AI talent, many organizations are pairing internal upskilling efforts with flexible external AI experts. These specialists support internal teams by bringing real-world experience, designing role-based AI upskilling programs, and shortening the trial-and-error phase that often slows early AI adoption.

In fact, analysts estimate that 80% of today’s engineers will need to upskill within the next few years, and leaders are responding by reallocating investment away from pure hiring and toward internal capability building. 

But just investments aren’t enough. Successful AI upskilling requires guidance from experienced AI practitioners and a clear plan to ensure training translates into real, organization-wide change. 

In this article, we break down five practical AI upskilling strategies, each illustrated through a real enterprise case study, showing how leading organizations scaled AI by upskilling their existing workforce.

Proven Ways to Upskill Employees for an AI-Driven Workplace

Reskilling in the age of AI rarely follows a single, linear path. Instead, organizations that scale AI successfully offer multiple learning routes, allowing employees to build skills based on their roles, experience levels, and how they actually work. 

Here are five key strategies enterprises use to implement AI upskilling:

1. Bringing in external AI experts to accelerate upskilling and delivery: As demand for AI skills accelerates, many organizations are engaging external AI experts to work directly with internal teams on active projects (for example, co-developing a recommendation engine or automating a claims workflow). With 85% of business leaders expecting skills development to surge in the next three years, these experts don’t just teach - they embed alongside teams to share proven practices, guide real-world implementation, and transfer knowledge in context. This project-based approach enables employees to build practical AI skills through hands-on experience, while continuing to deliver on day-to-day business priorities with minimal disruption.
2. Segmenting AI education by skill level: To overcome widespread AI skill gaps, large enterprises are driving adoption through layered learning paths that range from basic AI literacy to advanced, role-specific training (such as executives learning AI decision frameworks while engineers train on model deployment). Nearly 46% of leaders cite AI skill gaps as a major barrier to successful adoption, making tiered AI upskilling programs essential for allowing employees to progress at their own pace rather than forcing a one-size-fits-all model.
3. Role-based AI reskilling using Gen AI: To future-proof the workforce, many organizations are slowing hiring for automation-prone roles and investing instead in structured reskilling, such as transitioning analysts into AI product owners or operations staff into automation supervisors. This shift is increasingly practical, as employees already use AI regularly and are three times more likely than leaders to expect AI to replace 30% of their work within the next year, making Gen AI–powered career guidance an effective way to help workers transition into new roles while preserving institutional knowledge.
4. Investing early and pacing the transformation: Large-scale AI reskilling works best when organizations commit early and give employees time to adapt as roles evolve (for example, rolling out AI training 12–18 months before automation milestones). When AI and data training are tied to clear career pathways, promotions, and internal mobility, upskilling becomes a credible route to staying relevant rather than a reaction to disruption. That expectation is already shaping employee sentiment, particularly among early-career workers. As Microsoft India COO Himani Agrawal puts it, “Any organisation they go to which is not AI-ready, they will feel disillusioned.”
5. ‍Using microlearning and gamification to drive engagement: Short, practical lessons grounded in real business scenarios (such as five-minute prompts on improving customer emails or forecasting demand) help make AI learning easier to adopt and easier to sustain. When reinforced through gamification, participation tends to stay high, and learning feels more relevant. As Oleg Fonarov, CEO of Program-Ace, notes, “Companies that adopt gamified training report higher employee satisfaction and reduced turnover rates.”

Next, let’s take a look at five real-world upskilling and reskilling examples that show how organizations used these AI upskilling strategies to strengthen their workforce and drive measurable business impact.

‍Walmart and OpenAI: Training Millions for Everyday AI Fluency

Walmart, a multinational retail corporation, needed to prepare a massive frontline workforce for AI-driven changes across inventory, scheduling, and customer service. And they wanted to do it without slowing down their internal operations. So the company partnered with external AI experts from OpenAI, the AI organization behind ChatGPT, to support the initiative.

With the partnership with OpenAI, Walmart delivered an expert-driven, hands-on AI upskilling program to its employees. This included providing tiered certifications and workflow-integrated learning, so workers could build new AI skills while doing their jobs. 

"At Walmart, we know the future of retail won’t be defined by technology alone; it will be defined by people who know how to use it. By bringing AI training directly to our associates, we’re putting the most powerful technology of our time in their hands, giving them the skills to rewrite the playbook and shape the future of retail.” 

- John Furner, CEO, Walmart U.S.

Starting this year, the company will also offer free, customized AI training and certification to its 3.5 million associates and frontline workers through Walmart Academy. They expect this to turn their workforce into one that’s ready to work with AI. 

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This effort builds on Walmart’s nearly $1 billion investment in skills training through 2026. Walmart is expecting to upskill and certify around 10 million people by 2030. 

Walmart’s partnership with OpenAI reflects a broader upskilling strategy many enterprises are adopting: bringing in external AI experts to accelerate AI learning while building capability internally. These experts help employees develop practical AI skills without disrupting ongoing operations.

JPMorgan Chase: Segmenting AI Upskilling Across a 300,000-Person Workforce

JPMorgan Chase faced the challenge of building AI skills across a global workforce of more than 300,000 employees. Being a multinational banking institution, their AI use cases differed significantly between frontline staff, technologists, and senior leaders. A single, one-size-fits-all training approach wasn’t reliable. They needed a segmented approach to make AI accessible to beginners while still providing deep, role-specific learning for more advanced users. 

"Training needs are varied, just like AI applications. The best way to approach this is segment by segment. Everyone from rank-and-file workers to company leaders will have to learn new skills." 

- Derek Waldron, Chief Analytics Officer at JPMorgan Chase 

To do that, the firm invested in broad skills development and local workforce readiness, particularly in markets like the San Francisco Bay Area, while expanding apprenticeships across technology, business, and finance. By pairing these programs with guidance from experienced AI practitioners and collaborations with public and private partners, JPMorgan lowered barriers to entry, supported a more inclusive workforce, and built AI capability at scale.

‍How DBS Reskilled Its Workforce Without Disrupting Jobs

Similarly, DBS, a global banking institution, faced the challenge of adopting and integrating AI across its organization. The bank needed to do so at scale without triggering widespread job losses, even as automation reshaped many traditional banking roles. At the same time, it had to retrain thousands of employees for AI-enabled roles while maintaining morale and keeping day-to-day operations running smoothly.

The solution to reskilling in the age of AI was generative AI. BDS embedded AI literacy and role-based upskilling into its long-term workforce strategy, supported by Gen AI-powered platforms like iCoach and iGrow that provide personalized, on-demand learning and career guidance. iCoach provides personalised, AI-powered career coaching and guidance, while iGrow uses AI/ML to support career planning by mapping skills, identifying gaps, and recommending learning paths and internal opportunities.

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As a result, DBS scaled AI adoption to more than 370 use cases supported by over 1,500 models, while training more than 10,000 employees. In 2024, these efforts generated over $750 million in AI-driven value, exceeding S$1 billion, and tools like iCoach also improved employee performance, confidence, and engagement.

“I have personally benefited from upskilling programmes, which helped me to progress from a Call Centre Team Manager into a client-facing role as an Assistant Relationship Manager. The supportive learning environment encouraged me to leverage the various opportunities for development to build a fulfilling career, enabling me to make four internal moves over the course of my tenure at DBS.” 

- Zulkarnain Barudin, an Assistant Relationship Manager with DBS

Role-based AI reskilling using Gen AI shaped DBS’s approach to workforce transformation. By grounding upskilling in individual skills assessments and role-relevant learning pathways, and using generative AI to guide career development and internal mobility, the bank supported role transitions alongside training - helping retain talent and fill critical positions from within.

‍AT&T: A $1B Early Bet on Reskilling for an AI-Ready Workforce

A telecommunications company that had to upskill its workforce was AT&T. By the 2010s, AT&T’s workforce was largely built around traditional telecom and hardware roles, even as the company was shifting rapidly toward software, data, cloud, and AI-enabled services. Nearly half of its employees lacked the technical skills required for these modern roles, creating a serious risk of widespread redundancy or costly external hiring.

To address this challenge, AT&T launched a multi-year reskilling initiative, Future Ready, in 2013, committing $1 billion to retrain its existing workforce rather than replace it. Employees gained access to online learning platforms, university partnerships, and clear career pathways that outlined the data, AI, and digital skills required for emerging roles. These programs helped employees understand how AI could augment their day-to-day work.

“When you have workers that already possess much of what you need, it makes a lot more sense to retrain them than to go out and hire new workers, who may be more educated, and then wait a year or more for them to get up to speed with how the company operates.” 

-  Anthony Carnevale, Research Professor at Georgetown University’s Center on Education and the Workforce

By the mid-2020s, AT&T had embedded AI across its operations, establishing a centralized Chief Data and AI Office and deploying hundreds of AI and machine-learning models across fraud detection, network optimization, and customer service. 

Forbes reports that tens of thousands of employees completed internal AI training, helping them apply data and AI tools in their daily work. By investing early and pacing the transformation, the company supported its shift to a data- and AI-driven organization while steadily reducing reliance on external hiring, demonstrating that large-scale change can be achieved by reskilling before replacing talent and by linking learning directly to real job pathways.

‍Gamifying AI Upskilling: How Australian Law Firms Built Practical AI Skills

Multiple law firms in Australia faced the challenge of building confidence and practical AI capability across their organisations, particularly among lawyers and professional staff. Many employees were also hesitant to use AI due to unfamiliarity, skill gaps, and concerns around accuracy, ethics, and professional responsibility. So the law firms had to introduce AI to them in a practical, trustworthy, and regulatory- and ethical-compliant way.

Unlike the other examples we’ve discussed earlier, these law firms took a more engaging route by gamifying AI upskilling and tying learning directly to real tools and incentives. 

Here’s a closer look at how some law firms gamified AI upskilling:

• King & Wood Mallesons introduced Legal Transformation Belts, allowing employees to progress from beginner to advanced levels through hands-on modules using AI chatbots and research assistants.  
• MinterEllison reinforced adoption through “Mintcoin” rewards and innovation challenges linked to mastery of AI tools, while other firms used badges, points, and tiered certifications to make progress visible and motivating.
• Gilbert + Tobin ran a six-week program offering A$20,000 for the best generative AI ideas, helping even first-time users see AI training as an accessible and confidence-building entry point.

The gamified learning frameworks led to much higher engagement than traditional training, with lawyers actively building confidence to use AI in everyday drafting, research, and workflow tasks. Over time, this helped drive a broader cultural shift, where AI capability became part of career development and a recognised measure of firm-wide competence.

‍AI Upskilling Is a Human Strategy, Not a Technology Bet

AI transformation is often framed as a technology race, but in reality, it is a human one. Genpact’s experience shows that speed in the AI era doesn’t come from doing more, but from designing learning that fits naturally into how people already work. When AI skills are treated as career infrastructure rather than optional training, employees stop resisting change and start pulling it forward. 

The organizations seeing rapid AI adoption are not those that react to every new model or tool, but those that build systems where skills can continuously evolve without breaking trust or culture. Reskilling in the age of AI is no longer a defensive move to stay relevant - it is how companies future-proof themselves, one role, one workflow, and one person at a time.