Building AI-Ready Organizations: 5 Strategic Approaches That Actually Work

AI adoption is picking up speed. According to McKinsey, 78% of companies use AI in at least one part of their business. A recent Gartner report also found that generative AI is the most commonly deployed form of AI, especially in the U.S., U.K., and Germany.

However, the depth of this adoption is often limited. Many organizations only experiment with tools like ChatGPT, without fully integrating AI into their core workflows, team structures, or decision-making processes. This disconnect between widespread experimentation and real business impact reflects that many companies face a deeper challenge. There’s a common misconception that simply using AI tools means a company is AI-enabled, but access doesn’t equal transformation.

“Simply using AI isn’t enough. Businesses have to completely rethink their organization and workflow to best harness the power of the technology. Investing in AI products is potentially only half the battle. The whole leadership team, the culture and the learning structure, is as important as developing the product in [and of] itself.” 

— Caroline Basyn, Chief Digital and IT Officer, The Adecco Group

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That’s where the idea of an AI skills gap comes in. So, what is the skills gap in AI? It’s not just a shortage of technical talent. The real issue is that many organizations lack the internal capabilities to use AI effectively in everyday operations across teams, systems, and leadership.

Too often, companies assume that experimenting with tools or buying AI-powered software means they’re leveraging AI. But there’s a critical difference between using AI and embedding it into how a business runs. 

Without the skills to identify use cases, redesign workflows, and make decisions alongside intelligent systems, AI remains underutilized - more of a feature than a transformation. This deeper skills gap limits the impact of AI and holds back real progress. Closing it requires more than hiring data experts.

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This article will examine 5 practical strategies to help organizations close the AI skills gap. These strategies focus on transforming work by building internal capability, encouraging cross-functional collaboration, and making AI a core part of business operations.

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1. Breaking Down Silos to Strengthen AI Skills Across Teams

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A crucial reason why AI efforts often fall flat is that technical teams build solutions in isolation, without enough input from the people who use them. These tools may work in theory, but don’t meet business needs or get adopted.

Studies show that nearly 7 in 10 AI projects fail to achieve their intended goals, often because they aren’t well aligned with business priorities or don’t fit smoothly into existing workflows.

This is caused by an AI skill gap in converting cutting-edge technology into real-world impact. To close this gap, companies can create cross-functional teams that bring together strategic, operations, IT, and data leaders. These groups help align AI efforts with business priorities and make it easier for teams to share knowledge, collaborate, and learn from each other as they work.

For instance, this approach helped the mining company, Freeport-McMoran. Faced with rising costs and aging infrastructure, Freeport partnered with McKinsey to co-create an AI model that could optimize mill settings in real time. Rather than relying on fixed daily parameters, the model adjusted hourly based on ore characteristics and sensor data.

By involving metallurgists, engineers, and data scientists throughout the process, Freeport ensured the technology met on-the-ground needs and that teams felt ownership of the solution.

“This helped with the acceptance and the adoption, creating co-ownership across the team,” 

- Cory Stevens, President of Mining Services at Freeport-McMoRan.

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The outcome was a 5–10% productivity boost and an additional 200 million pounds of copper produced annually across its operations. Beyond this, Freeport built internal AI fluency and gave teams the confidence to approach future challenges with a more collaborative, data-driven mindset.

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2. Teaching AI Skills in Company-Wide Literacy Programs

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While cross-functional teams can build interest in AI, broader adoption depends on raising AI literacy across the company. There’s still a common misconception that AI is only relevant for technical roles or IT departments, which can limit its potential. AI is a general-purpose technology that can improve work in every part of the business, not just in IT. 

For example, marketing teams can use it to personalize customer campaigns, human resources can streamline recruitment and forecast workforce trends, and finance teams can automate reporting or detect fraud. However, many non-technical employees still don’t fully understand how AI applies to their roles. This lack of understanding can slow collaboration and keep AI siloed within technical teams.

More than 20% of employees say they’ve received little to no training or support, making engaging with new tools harder. Making education more accessible, through hands-on, practical training tied to real business problems, helps employees build confidence and apply AI in meaningful ways.

Take Levi’s, the brand known for denim innovation. Rather than hiring new tech talent and trying to teach them fashion, Levi’s launched a machine learning bootcamp to upskill its global workforce. The eight-week program taught retail, design, and logistics employees how to code, explore data, and apply design thinking to digital solutions.

The results were immediate. A warehouse technician built a predictive maintenance app. A design coordinator used computer vision to automate color matching. Another created an algorithm to superimpose artwork on garments.

“You can teach someone in fashion data science, but to teach someone with a data background the nuances of fashion? That’s much harder,” 

— Ronald Pritipaul, Associate Data Project Manager, Levi Strauss & Co.

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By training existing talent, Levi’s didn’t just teach new skills - it addressed the AI talent shortage from within, embedding AI thinking throughout the business.

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3. Align AI Skills with Business Outcomes to Maximize Impact

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Just like non-technical teams need AI literacy, technical teams need a better understanding of the business. Too often, AI models are built in isolation - technically impressive, but disconnected from the problems they’re meant to solve. To close this part of the AI skills gap, organizations can help engineers and data scientists understand what success looks like from a business perspective.

One approach that’s making this easier is vibe coding. Instead of spending time writing code from scratch, developers use AI tools to help generate code based on natural language prompts. 

This shifts their focus from just building to solving problems, giving them more time to work closely with business teams, ask the right questions, and refine solutions quickly.

“Together, these groups can ‘vibework’ and independently build projects in days that would have taken months of coordination across departments.”

— Ethan Mollick, Professor of Management, The Wharton School

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The shift is already underway: over 25% of Y Combinator startups now rely on AI for 95% of their code, and Google reports that around 25% of its new code is AI-generated. Tools like vibe coding allow technical teams to move faster and spend more time understanding the business, making their work more relevant, impactful, and aligned with real organizational needs.

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4. Hybrid Roles Help Align AI Talent with Business Goals

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Even with growing skills on both sides, sometimes gaps are inevitable. Business teams may not know how to scope AI opportunities, and technical teams may miss the bigger picture or real-world constraints. Someone on the team who can connect the dots in a hybrid role can make all the difference.

These roles, whether officially titled AI translator, product owner, or something else, bring together a working knowledge of the technology and the business. They help teams align early, reduce misunderstandings, and ensure that AI solutions are relevant, safe, and adopted. 

Demand for these roles is rising fast: estimates suggest that by 2026, the U.S. alone may need between two and four million translators to bridge technical and business perspectives. Hybrid roles like these are often why an AI tool becomes more than a pilot - they help make it part of how the business works.

For example, KPMG’s development of its internal AI assistant, KymChat, shows how this can work in practice. Rather than handing the project to IT, KPMG built a cross-functional team that included legal, risk, privacy, product, and tech experts. 

Together, they shaped the tool’s design to match real use cases, like ESG reporting (Sustania) and policy lookup (Integra), while ensuring compliance and usability. The collaborative structure meant critical decisions could be made quickly and thoughtfully.

“From the get-go, we knew the guardrails we needed as we had everyone in the same room.”

— Robert Finlayson, KymChat Product Manager, KPMG

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KPMG also rolled out the tool in phases, starting with lower-risk applications and gradually scaling. This allowed time to build trust, gather feedback, and improve governance along the way.

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5. Support AI Talent Growth with Change Management

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It's the first step when an enterprise adopts AI by rolling out new AI tools. The real challenge begins when people are expected to change how they work. Even the most advanced systems can stall without employee confidence, leadership support, and a clear adoption plan. 

63% of workers say they rarely use AI - despite 31% acknowledging that AI could handle at least part of their daily tasks. Organizations need more than technology to close the AI skills gap - they need strong governance and a hands-on approach to change.

One reliable tactic is running AI hackathons. These are not just technical sprints - they’re collaborative, low-pressure environments where employees from all departments can explore AI by solving real business problems. Hackathons foster curiosity, lower anxiety, and encourage teams to shift from passive learning to active experimentation. This creates momentum from the ground up and helps teams see AI as something they can use and shape, not something being handed down from IT.

Moderna, the biotech company known for developing one of the first COVID-19 vaccines, embraced this approach. Rather than waiting for a perfect roadmap, Moderna gave its workforce room to experiment. Employees built over 3,000 custom GPT-powered tools, automating everything from clinical trial prep to HR workflows. These tools weren’t imposed but created by those who needed them most.

Crucially, this was supported by a phased rollout and clear governance that allowed the organization to learn and scale responsibly. By combining structure with flexibility, Moderna made AI adoption something everyone could participate in.

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Handling the AI Talent Shortage Starts with Operational Change

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The five strategies we’ve explored clarify that closing the AI skills gap isn’t just about finding more technical talent or offering standalone training programs. It’s about transforming how your organization works - how teams are structured, decisions are made, and how value is delivered.

A commitment to operational change separates companies seeing real results from those still stuck in pilot mode. That means building cross-functional teams, raising AI literacy across the board, giving technical talent a deeper understanding of the business, and supporting all of it with the right roles, governance, and room to experiment.

The companies pulling ahead are those already reworking how work gets done. Recent research shows that redesigning workflows significantly impacts realizing financial gains from generative AI, more than any other organizational factor. While only about one in five companies have started to reshape their workflows, that’s a clear opportunity to get ahead of the curve.

AI can’t redefine your business unless your business transforms to make room for it. And that starts with leaders ready to rethink skill sets and systems.

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