Is Your Business Transmission-Ready?
The gap between what AI produces and what enterprises can act on is where value is created — or lost.
Transmission-ready. It is a simple idea with an uncomfortable implication.
An organisation is transmission-ready when its people, processes, and data infrastructure are built to convert AI capability into business outcomes – consistently, at scale, with accountability. Not to deploy AI. Not to experiment with it. To actually use what it produces.
Most enterprises are not there yet.
The tools have outpaced the operating model. AI can now handle significant portions of complex knowledge work — research synthesis, first-draft generation, code writing, data analysis. The middle of the process has been accelerated at a scale that was not possible two years ago. And yet, across boardrooms and quarterly reviews, the same question keeps surfacing: why has the productivity dividend not shown up?
Over the past few months, I have been writing about different facets of this problem — how AI intensifies work rather than reducing it, how agentic systems shift bottlenecks rather than eliminating them, why the “system of action” layer above legacy platforms is where competitive advantage now lives, and what happens when organisations confuse AI adoption with AI capability. This article pulls those threads together into a single argument, and names the thing I believe most enterprises are missing.
A February 2026 study in Harvard Business Review offers the clearest answer yet. AI does not reduce work — it intensifies it. More output to review. More decisions to make. More velocity without more clarity. And McKinsey‘s latest research on the human-AI workforce explains the structural reason: the skills AI brings to the workplace are largely the same skills your people use. That means the question was never “AI or humans?” It was always “how do humans and AI work together — and who builds that capability?”
The organisations that have answered that question are pulling ahead. The ones that have not are running faster on the same spot.
The Last Mile Problem
AI agents are genuinely impressive at handling large portions of complex tasks. Research synthesis, first-draft generation, code writing, data structuring — the middle of the process can now be accelerated at a scale that was not possible two years ago.
But the last mile remains stubbornly human.
Whether it is reviewing and delivering a research deck in consulting, doing security and code review before shipping software, or managing customer conversations after an outreach sequence — the final stage of most tasks still requires a person. Not just any person. One who understands the whole task, not merely the piece that is left over.
As I wrote recently, the analogy that keeps coming back to me is the flyover. In dense Indian cities, flyovers are built to solve traffic congestion. They work — until you reach the junction at the other end. The congestion does not disappear. It relocates downstream to the next constraint point. AI is doing the same thing to work. It clears the middle of the process. The bottleneck shifts to wherever human judgment is required: to the review, the quality call, the ethical check, the client conversation, the final decision.
The expertise needed at that point is not marginal. It is the whole game. A person overseeing AI-generated financial analysis needs to understand the full scope of the work — not just spot-check what is left over. The judgment required to assess whether the output is right, what needs to change, and when the model has gone off course is not a residual skill. It is the primary skill.
The Horizon That Keeps Moving
If the last-mile problem were fixed — if AI moved from handling 80% of the task to 95%, then 99% — would we eventually reach full automation?
McKinsey‘s research suggests a different trajectory. As automation capability grows, so do expectations. What was once a complete deliverable becomes a baseline. The task itself expands to absorb the new capability. Today’s 99% solution becomes tomorrow’s starting point.
This is not new. It has happened across every professional domain where tools advanced significantly. Tax codes grew longer as compliance software improved. Codebases grew exponentially as development tools matured. Analytical datasets expanded as processing power made them workable. Each new tool raised the bar of what was expected — it did not lower the complexity of the work.
The same dynamic is now playing out with AI. Companies and markets will see what AI-enabled teams can produce, and simply expect more. The same analysis that once satisfied a client no longer does. The same level of product output does not hold its position. A new last mile emerges — at higher altitude, requiring sharper judgment.
McKinsey is explicit about the implication: we cannot stay at the same base level of skill alongside AI and expect to add value. Every person in the workforce needs to develop their capabilities with AI — not hand tasks off to it and step back. The goal is what McKinsey describes as “super-skilling”: using AI to raise what individuals and teams are capable of, not to reduce what they have to do.
Three Things Leaders Need to Sit With
Domain expertise becomes more valuable, not less. The person who can tell whether AI output is right – and articulate why – is the person who understands the full task. That is not a generalist skill. It is deep domain knowledge applied to oversight. As AI handles more of the routine, the premium on genuine expertise rises. Organisations that hollowed out domain depth in anticipation of AI-driven efficiency may find themselves without the judgment layer that makes AI output usable.
Adoption is not the same as capability. Most organisations measure AI deployment by usage rates – how many people opened an AI tool this month, how many prompts were submitted, how many tokens were used by developers. That is a shallow metric. The capability that matters is whether teams can direct AI effectively, evaluate what it produces, and integrate it into work that holds up under scrutiny. McKinsey frames this as the difference between using a shared skill and developing it. One is passive. The other is a structural advantage.
The ROI question has shifted. The original question — “how much of this task can we automate?” – is largely answered. AI can automate significant portions of most knowledge work. The question that now determines whether organisations actually realise that value is different: “Do we have the workflow design, data readiness, oversight capability, and governance infrastructure to absorb what AI produces?” For most enterprises, the honest answer is: not yet.
Where the Real Work Begins
The organisations seeing genuine, sustained ROI from AI deployments share a common characteristic. They did not just deploy faster. They built the infrastructure to absorb speed – and to maintain accountability over what AI produced.
That infrastructure is not glamorous. It involves workflow redesign around where human judgment actually lives in a process. It requires data architecture that makes AI output traceable and trustworthy. It demands adoption frameworks that build real AI fluency across the workforce – not familiarity with a tool, but capability with a working method. And it needs governance structures that answer the question: when AI produces something, who is responsible for what comes next?
This is the layer we work in at Quadra. Not model selection, not prompt libraries. The transmission layer – between what AI is capable of producing and what an enterprise can confidently act on.
That is what transmission-ready looks like in practice. An organisation built to convert AI capability into business outcomes – consistently, at scale, with accountability.
Most organisations are not there yet. The tools have outpaced the operating model.
The Right Question for 2026
If you are a CXO evaluating your AI strategy this year, the question worth asking is not which model to deploy or which Copilot to licence. Those are procurement decisions. They matter, but they are not where value is created or lost.
The question that determines whether your AI investment compounds or stalls is this: is your organisation built to use what AI produces?
If that does not have a clear, concrete answer — if it lives somewhere between “we think so” and “we are working on it” — that gap is worth closing before the next investment cycle opens.
The last mile has not gone away. It has just moved. And it has never required more expertise, more design, or more organisational intention than it does right now.
About the Author
Mr. Prashanth Subramanian
Co-Founder & Executive Director
Quadrasystems.net India Pvt Ltd
Mr. Prashanth Subramanian is an entrepreneur with more than two decades of experience in driving transformation through technology, and helping build agile, intelligent enterprises. We are here to build, to innovate, and to empower. Whether it’s navigating complexity or delivering measurable results, Quadra’s mission is to connect technology with business outcomes. We deliver this through a team of talented, skilled professionals who are passionate about technology and customer experience.
Mr. Prashanth Subramanian can be contacted at:
About Quadrasystems.net India Pvt Ltd
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