Dharmesh Acharya is the COO of Radixweb, a global tech consultation and bespoke software service provider.
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Most tech advisory sessions used to begin with a familiar question: How do we get access to better tech? However, the conversations have evolved: Why isn’t our tech working?
Every competitor your business has now runs on the same AI infrastructure, the same cloud platforms and the same vendor ecosystem that supported Fortune 500 businesses in scaling.
Fancy tools are no longer the differentiator. Now, it’s about what you do with these tools.
AI access is a commodity now, not a differentiator. Your AI stack just shows that you have access to the same tools as your competitors. The real question is: Do the people operationalizing AI in your business have the architecture or the authority to drive measurable outcomes in businesses?
And this isn’t an unrealistic concern. Gartner's top strategic technology trends point out that generic large language models are already being displaced by domain-specific alternatives because commodified AI access has made them competitively unusable. Even the KPMG Global Tech Report remarks that 50% of businesses expect to fully scale and continuously improve by the end of 2026.
But only 11% of surveyed executives consider themselves at that level today. This points out the difference between organizational planning cycles and what they deliver.
Tech leaders should be asking: What’s the organizational system to convert the investments into shipping outcomes? Across review budgets in healthcare, fintech, manufacturing, retail and several other industries, leaders assume that investments and outcomes are directly correlated.
What contributes to this gap between the investment decision and the delivered result? Unclear ownership, prioritizing actions instead of outcomes and failed change management. The KPMG survey data shows that, on average, a 200% return on tech investment is achievable, but only with execution discipline. Even McKinsey's research on transformation success documents this failure pattern across industries when leaders miss asking how they can transform investments into outcomes.
I have an observation that would make most tech leaders uncomfortable. The most disciplined execution is happening outside the tech industry, in areas traditionally considered non-tech.
Take the case of retail, where digital adoptions are accelerating not on the wings of innovation but on operational consistency. Deloitte’s 2026 Global Retail Industry Outlook surveyed 330 executive positions from businesses with at least $1 billion in annual revenue. Around 68% of agentic AI deployments are expected in the next 12 to 24 months. The Deloitte report makes it clear that adaptability is the most crucial execution discipline. It will further distinguish market leaders in retail.
I have observed a similar graph for the print and packaging industry, which is heading toward $644 billion by 2030. The fastest converters aren’t the ones with the most sophisticated presses. They are the ones who have restructured their operations entirely around tech operationalization, from AI-driven workflows to on-demand production and web-to-print platforms for removing friction across every customer touchpoint. Across every capital-intensive, traditionally non-tech industry, leadership is decided by execution quality, not access to technology. Execution discipline is an absolute table stake for every organization that wants to be technology-forward.
Most business leaders I talk to run in a common pattern. They are genuinely dedicated to AI transformation. They believe in the AI vision, their boards have passed the AI budgets, but they want to run pilots before they scale every function. While caution feels responsible in the age of evolving security risks and complicated governance regulations, this stage is often called the “pilot paralysis.” Every quarter you spend in experimentation mode is a quarter that your competitor's production system is learning, improving and widening an execution gap that’s not cheap to close.
Businesses that close the gap between tech ambition and genuine business outcomes make three decisions differently:
I have seen most businesses treat infrastructure as an engineering decision while considering strategy as a leadership concern. When these two dots do not connect, execution dies.
You cannot approve AI transformation road maps while simultaneously deferring decisions about data residency, integration design and security architecture. These are the very foundations on which the AI road maps depend. The results are quite predictable: technically successful deployments that cannot scale, cannot pass compliance audits and cannot integrate with existing systems.
Agentic AI without governance models is unmanaged liability. Businesses deploy AI agents that execute workflows, access sensitive data and make decisions. The autonomous nature of these models and the landscape of data they can touch make it crucial to have a defined failure mode, audit trails and a named owner.
If you want to build a durable advantage, design trust into the system from the start. This comes in the form of explainability, payload logging, role-based access, defined retraining triggers and human-in-the-loop checkpoints for decisions above a risk threshold. Build conditions that support execution to scale; build guardrails that regulators approve.
It’s a structural issue if you exclude front-liners from the decision wave. As a leader, you can close this loop. Name a business owner before the project starts, give the delivery team transparent architectural authority, and put the people who understand the problem in the room where the solution is designed.
The markets are unforgiving to businesses who have access to technology but cannot execute it. And this is a permanent shift. Leaders who accept this and rebuild their execution discipline will define leadership for the next decade.
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