Aanchal Gupta, Founder of Agents Stack
Aanchal Gupta, Founder of Agents Stack

Gartner reports that global artificial intelligence spending is projected to reach $2.52 trillion in 2026, driven heavily by hardware vendors, cloud capacity, and data center infrastructure. However, the primary challenge remains the readiness of human capital and organizational processes, highlighting a significant gap between acquiring technology and altering institutional operations.

Adoption is no longer the constraint. Recent research by McKinsey found that 70 percent of employees say they feel personally prepared to work with AI, while only 27 percent of leaders believe their organizations are ready to make the shifts that would help that work pay off. Employees are adapting faster than the institutions around them, and executives often read that gap as a training problem rather than a structural one.

Buying technology and transforming an organization can be fundamentally different challenges, yet the industry usually treats them as the same thing. A license can be procured in weeks. Rewiring processes, incentives, decision rights, and culture take much longer and require people with both the time and the authority to see the work through. That work is unglamorous and slow, which is often why it loses the budget argument to a visible tool rollout.

The failure modes are already visible. Projects stall after proof of concept, licenses go unused, and unsanctioned use appears when employees route around controls. Gartner has projected that at least 50% of generative AI projects will be abandoned after proof of concept, citing poor data quality, inadequate risk controls, escalating costs, and unclear business value. Boards press for pace, and chief information officers respond with the safest available use cases.

Aanchal Gupta, founder of Agents Stack, who has led readiness work across Asia Pacific, the Middle East, and the United States, frames the gap directly. "Companies don't have an AI technology problem. They may have an AI transformation problem," she explains. "Businesses can buy AI technology quickly, but transforming an organization around it may require something far harder, which is company-wide commitment."

Her assessment starts with the team rather than the tool. "The readiness of the organization in terms of a team that understands what a transformation will entail, in terms of change management, is far more important for the outcomes that companies are trying to achieve," she observes. In practice, that means stable teams, genuine leadership commitment, cross-functional participation, and people with the bandwidth to implement change. Her method begins with a 28-day scoping sprint aimed at the use cases that could drive 80 percent of the impact.

Executives commonly misjudge how much organizational involvement the work requires. "Leadership actually may not know what the processes are," Gupta notes. "It's in the mind of the people; they could never have written their processes." That knowledge gap derails scoping, because the workflows that are to be redesigned tend to live in scattered spreadsheets or nowhere at all. Resistance compounds the problem when new tools arrive with unfamiliar interfaces and no process mapping to bridge the gap.

The commercial cost surfaces as wasted investment, abandoned tools, and fatigue. Gupta describes a structural bind. "Chief information officers carry AI governance and cybersecurity obligations while boards push for visible progress," she said. "So they may select simple use cases that protect the attack surface but deliver little material impact. The leaders who resist that pressure prepare the organization first, and in some cases they push back on the board for a slower, more durable sequence."

The outlook differs by organizational size, and Gupta is direct about what that means in practice. Larger enterprises may lack the organizational agility to retrofit existing operations around AI at meaningful speed. For those companies, she suggests that acquiring smaller, AI-native businesses or building a separate greenfield operation may produce more durable results than attempting enterprise-wide transformation from within. Smaller organizations, by contrast, can move faster when the chief executive has both the vision and the appetite to assemble the right team. The constraint in that case is not scale, but leadership commitment.

What that adds up to is a reframing of where the AI race is actually being decided. The companies investing most heavily in tools and licenses are not necessarily the ones building durable competitive positions. The organizations that appear to be pulling ahead are those treating transformation as a distinct discipline, one that may require its own resourcing, its own leadership mandate, and its own sequencing logic. Gupta states, "AI transformation is not something companies can purchase. It is something organizations must collectively commit to executing. The technology has never been the bottleneck."