Why Are We Struggling To Capture The Full Value Of AI?

Why Are We Struggling to Capture the Full Value of AI?

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By now, I believe most leaders are aligned on the immense potential AI offers. As I explained in prior blogs, it enables us to create entirely new offerings for our clients, elevate the value of existing solutions, and unlock efficiencies across internal operations to drive cost savings and margin expansion. These benefits are no longer speculative. They are visible, attainable, and increasingly expected.

Yet across industries and functions, I continue to see a gap between aspiration and execution. Despite considerable energy and investment, we are not moving fast enough or going far enough to deliver on AI’s full value.

This is not simply a technology problem or a resourcing issue. It is an organizational problem rooted in uncertainty, misaligned incentives, and organizational inertia.

The Real Obstacle: Uncertainty

We are not making better, faster progress toward realizing the promise of AI because often, many executives resist the journey, not always overtly, but through hesitation brought on by uncertainty. While many label this as a change management challenge, I believe it is more fundamental. It is not just that change is hard. It is the nature of this change that creates doubt and reluctance at every level of the organization.

Leaders intuitively understand that AI is not just another tool to slot into the existing stack. The implications are more far-reaching. I often observe that organizations are uncertain of what this new environment will look like. Introducing AI often requires a change to the technology foundation itself. These changes ripple outward, reshaping customer interactions, operating models, and even the roles and responsibilities of internal teams.

What begins as a tool deployment quickly triggers larger, more strategic questions: What will this mean for customer experience? How will core processes interconnect? What are the implications across the broader ecosystem?

These questions introduce ambiguity. And ambiguity, in my experience, often leads to hesitation.

Executives may not always voice their concerns, but the signals are clear. Projects stall. Ambitions shrink. Momentum fades. It is not a lack of desire to be better. It is, I believe, a lack of clarity on how to build the roadmap to get there, create confidence in what they are doing, and ensure that the journey will not derail what is already working.

The Risk Equation

Change always involves risk. But the risk here becomes personal.

If an executive fails to deliver on their current responsibilities, either to their customers or internal stakeholders, that’s unacceptable. Yet the aspirations we have with AI are to change how we’re doing what we’re currently doing. Consequently, the risk is very high at every level of leadership, from mid-level managers to senior executives.

To commit fully, leaders must believe two things: first, that the reward justifies the disruption; and second, that success is achievable. Without both, the logical choice is to retreat to safer, more familiar ground.

Three Approaches to AI Transformation

In our work with clients, we have observed three primary approaches to AI transformation. All of these can be pursued in parallel. Each offers advantages, but each also introduces limits.

1. Incrementalism

The most common path that we see is incremental. Organizations add AI tools to existing workflows, encourage teams to experiment, and look for small wins. This is often framed as a proof-of-concept approach. It is a manageable, low-risk approach that enables learning without large-scale disruption.

In my view, this approach is also exhausting and rarely unlocks substantial value. It requires sustained pressure to maintain momentum, and the benefits tend to plateau quickly. So, they generate a lot of activity and excitement for modest results.

Incremental change is appealing because it feels manageable. But in most cases, it does not lead to transformative outcomes.

2. Follow the Leader

Another approach is to wait. Companies monitor what others do, assess results, and follow once a proven model emerges.

This is particularly common for large-scale transformations or use cases where the risk is perceived to be high. This is a well-established adoption pattern, as described by Geoffrey Moore in Crossing the Chasm. Early adopters take on the heavy lifting, and the majority follow once the path is clearer.

There is nothing inherently wrong with this approach. I see it as a rational response to uncertainty. But it comes at a cost. Companies that wait risk missing the opportunity to shape the market, capture early customer loyalty, or develop the internal capabilities required for long-term advantage. In some cases, by the time they move, the opportunity has passed.

3. Future-Back Planning

The third path is to build a forward-looking scenario that defines a future state and works backward to the present. This approach requires constructing a clear, detailed vision of what the organization could look like once AI is embedded into its operating model.

It involves anticipating where technology will be one to two years out, and designing systems, structures, and talent models that align to that destination. We call this “the visible future.” It may not exist yet, but it is close enough to be modeled with rigor.

The challenge is that these scenarios must be robust and credible. The underlying thinking must withstand scrutiny from seasoned executives who are being asked to place big bets. Any forward-looking model that hasn’t been done yet by your organization or by others will be viewed skeptically.

From what I’ve observed, for this strategy to succeed, the organization must work through the full implications: how the tech stack will evolve, how workflows will change, what impact this will have on customers, employees, partners, and the broader ecosystem. Only then can leaders build a roadmap they believe in and are willing to execute against.

This is the most difficult path, but also the one most likely to produce transformational outcomes. For companies seeking to accelerate benefits and drive transformation more quickly than others, the forward-looking scenario offers an alternative to the traditional, incremental improvement approach.

Tailoring Strategy to Appetite

Most companies will not follow a single path. They will mix and match strategies based on the maturity of the business function, the competitive context, and the perceived upside.

In some areas, incremental improvement may be sufficient. In others, it may be wise to wait until models have matured. But in areas where there’s deemed to be enough customer benefit, customer experience, or efficiencies to be worth the effort, they will need to take the forward-looking approach.

The key is to match the approach to the ambition. Too often, organizations pursue transformation-level goals with pilot-level tactics. The result is predictably disappointing.

Standing Still Is the Greatest Risk

The greatest risk in AI is not failure. It is inertia.

What we are asking our organizations to do is fundamentally difficult. To change how they operate. To question assumptions. To move toward an uncertain future with conviction. Yet I believe that is exactly what is required.

For AI to deliver its full value, we must reduce the uncertainty enough that leaders can act decisively. The only way to address risk is to reduce the uncertainty and to be sure that what you’re going to do is going to work. And to be sure about how you’re going to do it. In some cases, that will mean small, steady steps. In others, it will mean bold moves supported by thoughtful scenarios and rigorous planning. The only unacceptable choice is to stand still.

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