From Hype to Impact: Getting Gen AI Right

Mathi Venkatachalam

Co-Chief Operating Officer, MResult

Hype cycles, waves, quadrants, or boardroom discussions, Gen AI is ubiquitous everywhere. There is urgency to act, pressure to match the pace of peers, and an expectation to show visible impact. However, instead of hurrying to adopt the next new wave, one needs a moment to reflect. A pause, not to delay action, but to examine where and how this powerful capability has the right fit. The question that begs answer is not how soon, but what and why.

Adoption Without Alignment Is a Trap

The Gen AI narrative is moving quickly. Yet the return on many of these efforts remains elusive.

Nearly one third of enterprise Gen AI pilots are discontinued before scaling. Over 40 percent of organizations have walked away from most AI initiatives. More than 80 percent report little to no measurable business impact. Fewer than one in four have built the capability to move beyond experimentation and deliver sustained value.

These outcomes are not the exceptions. They point to a pattern of action taken without anchoring. In many cases, initiatives are driven by external pressure or internal enthusiasm, not by a defined the right priority.

Impact Begins with Identifying a Problem, Not a Model

Organizations that succeed with Gen AI do not start with technology stack. They begin by identifying the problem that needs solving.

Most common issues faced by organizations are prolonged decision cycles due to fragmented systems, knowledge silos that slow response times, or operational inefficiencies from manual, repetitive work.

In some cases, generative solutions may help unlock speed or scale. In many others, automation or targeted search capabilities deliver greater value. The difference lies in how.

Gen AI Cannot Be the Default Answer

Not every challenge requires a generative response. Starting with Gen AI without understanding the problem increases risk, complexity, and cost.

Instead of throwing tools and systems at the problem, a better way would be to ask:

Where are teams spending disproportionate time on low-value work?

What decisions are delayed because the right data is not easily accessible?

Where do customers encounter friction?

How are we addressing challenges in efficiency, decision quality, or experience?

Often, traditional AI, intelligent automation, or improvements to existing processes solve these problems more effectively. Gen AI has a role, but it must earn its place based on fit, not novelty.

Build with Intention, Not Just Ambition

Structured adoption begins by taking stock of the current state of business, its immediate priorities and long-term vision. At MResult we use a practical framework that considers the current context, existing barriers and readiness factors that must be addressed before making technology decisions. This approach enables leaders to prioritize effectively, test responsibly, and scale with confidence.

Lead with Purpose in Gen AI Era

Gen AI will continue to evolve. New use cases will emerge. Enterprise platforms will expand their capabilities. But the core leadership challenge remains the same. The focus remains on solving meaningful problems with solutions that work in real conditions, not just on paper.

Responsible leadership requires investing in what creates impact, not what appears impressive. It also means surrounding the business with partners who ask hard questions, push for clarity, and help filter ambition through the lens of readiness. In my experience, effective teams concentrate on a few essentials:

Building reliable and connected data foundations

Selecting use cases tied directly to measurable impact

Embedding governance that supports both experimentation and accountability

Clarity Is the Real Competitive Edge

Organizations that realize the value of Gen AI are not reacting to the wave. They are designing for what lies beneath it. Their focus is not on how quickly they deploy, but on how clearly, they define what needs to change.

Let me emphasize again, pausing to reflect is a deliberate act of preparation. And that begins with the fundamentals. A strong data foundation, simplified processes, and connected systems, setting the stage for aligned teams, sustainable value, and resilient operations. This is not about being first. It is about being ready.