Why AI Transformation Is 70% People: Insights from a BCG Leader

AI transformation is 10% technology, 20% tools and processes, and 70% people.

That’s how Ernesto Pagano, a Managing Director and Senior Partner at Boston Consulting Group (BCG), frames the challenge of AI adoption when he sits down with PMI President & CEO Pierre Le Manh on The Shift Code Podcast.

He’s right because, as powerful as AI is, its success inside organizations doesn’t depend on the code. It depends on how people work with it, trust it, and change because of it.

In this conversation, Pagano breaks down what’s really happening on the ground with AI transformation across industries and why most companies need to rethink their approach.

Where AI Is Already Working and Where It’s Stuck

Every company is using AI. The difference is to what extent.

According to Pagano, the first wave of adoption is happening in support functions, like finance, HR, legal, and IT where the risks are lower.

Moreover, automating expense reports or legal document searches won’t make headlines, but it delivers measurable efficiency.

The real hesitation shows up in the core business, especially in creative industries. In Hollywood, for example, the use of AI in production or writing is still controversial due to intellectual property (IP) concerns and the relationship with creators. Instead of large-scale rollouts, companies are experimenting with pilots—testing AI in areas like post-production, special effects, and editing.

Meanwhile, in industries where IP isn’t as sensitive, like news or textbooks, AI is already cutting costs and speeding up production. The dividing line isn’t whether AI can help, but where stakeholders are willing to accept it.

The Human Side of AI

AI isn’t primarily a technology challenge, Pagano stresses—it’s a people challenge.

“Change is hard. People like doing things in certain ways, and they don’t want to change,” he explains. “It’s scary because many people don’t know how to use AI in the right way.”

That’s why BCG approaches AI transformation like any other major organizational shift. Instead of dropping a 200-page playbook that no one uses after the consultants leave, they work side by side with clients: training, coaching, and embedding change agents who can carry the transformation forward.

This slower, more deliberate approach pays off in adoption. When employees understand AI as a tool that helps them, not one that replaces them, they’re more likely to embrace it.

The Impact on Jobs

Pagano notes that some entry-level roles are disappearing, especially those built around repetitive tasks. However, he emphasizes that senior-level roles are still essential.

“You still need the upper layers because you need to monitor, you need to control, you need to ensure that there is the right data governance, that the use of AI is consistent with the strategy of the firm,” Pagano says.

At BCG, AI tools are already shifting work dynamics. A chatbot helps consultants search prior BCG projects, while Dexter, another internal AI tool, builds presentation decks. In other words, AI doesn’t eliminate the need for consultants. It changes the kind of work they do.

Centralized vs. Decentralized AI

One of the biggest organizational challenges is how to structure AI initiatives.

Some companies centralize AI within a single function, creating consistency, governance, and scalability across the enterprise. Others take a decentralized approach, letting business units experiment freely. While decentralization fosters innovation, Pagano warns it often leads to “pilot purgatory,” where projects never scale.

The winning model, he argues, is hybrid: give business units room to experiment, but pair it with strong governance and frameworks that enable successful pilots to scale quickly. Without this balance, companies risk scattering resources and missing real ROI.

Responsible AI: The Non-Negotiables

For Pagano, responsible AI isn’t a side note; it’s a prerequisite for success.

“Data infrastructure and data governance are extremely important because if you don't have the right data in the right place, you cannot really use it in the AI world,” he explains.

Reliable, high-quality data is the foundation, but it’s not enough. Governance, ethics, and agility in project management are equally essential.

Pagano says that leaders must build governance structures that are flexible enough to evolve as tools change. And crucially: never pilot without planning for scale.

Responsible AI, he emphasizes, is about more than compliance; it’s about ensuring AI initiatives align with organizational strategy, protect stakeholders, and remain adaptable in a rapidly evolving landscape.

Technology Is the Easy Part

For many leaders, technology feels like the easy part. Buying a new tool is simple. Implementing a large language model is possible with the right expertise.

However, as Pagano underscores, “AI transformation is 70% people.” The companies that win will be the ones who master the human side of change.

Tags: AI | Organizational Change | Tech Leadership | AI Project Management

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