| The Strategy-Execution Gap in 2026 In 2026, the strategy-execution gap is widening — not because businesses lack capability, but because AI abundance has removed the structural pressure that once forced discernment. Only 39% of organisations report EBIT impact from AI, and 74% show no tangible value despite significant investment (McKinsey & BCG, 2025). The problem is not doing less. It is choosing better. |
The quarter ends. The team has been busy — genuinely busy. Initiatives ran, campaigns launched, projects delivered. And yet the number didn’t move the way the plan said it would.
The instinct is to do more. To execute harder. To close the gap with activity. But the gap may not be an activity problem. It is very likely a discernment problem — and in the AI era, it is getting wider, not narrower, precisely because doing more has never been easier.
The strategy-execution gap is not new. For decades, organisations have wrestled with the distance between a well-constructed plan and its delivery. But over the past two years, something fundamental has shifted. The gap has morphed. It no longer only sits between strategy and execution — it now increasingly sits between execution and the commercial impact it creates.
Why More Capability Is Making the Strategy-Execution Gap Worse
AI has removed the resource constraints that once enforced discernment — but it hasn’t replaced them with anything. Historically, execution failure was partly explained by capacity limits. Not enough time, capital, or capability. Those constraints were real — and they partly explained why 60 to 90 percent of strategies fail in execution (a finding consistent across decades of management research, including HBR and McKinsey).
AI and the tools surrounding it have stripped away most of those constraints: intelligence is abundant, creation capacity is abundant, and the cost of building has collapsed toward marginal. But rather than closing the strategy-execution gap, this abundance has exposed the deeper organisational flaw beneath it — the inability to connect activity to the commercial value it creates.
McKinsey’s 2025 State of AI survey found that only 39% of organisations report any EBIT impact from AI, and only 6% qualify as high performers — attributing 5% or more of their EBIT to AI. BCG’s findings align: 74% of companies have yet to show tangible value from AI, despite significant investment and activity.
The resources arrived. The commercial impact didn’t follow. That is not a technology failure — it is an execution discernment failure, hyper-exposed by abundance.
How Abundance Removes the Discipline to Choose
Scarcity used to force the question: is this worth doing? When building an app required months of engineering and significant capital, the gap between idea and delivery was long enough to demand an answer. Constraints enforced discernment structurally, not culturally.
Abundance removes that embedded discipline almost entirely. When a prototype can be built in hours, a campaign launched in minutes, and a workflow automated before lunch, the natural brake on undisciplined activity disappears.
The question shifts from “Should we do this?” to “Why wouldn’t we?” — and that is a fundamentally different cognitive starting point. One that requires far more organisational discipline to manage precisely because the external pressure to be discerning no longer exists.
We are not executing more boldly. We are executing more indiscriminately. And in that environment, the thinking that should precede execution is the first thing to get cut.
The 4-Point Execution Breakdown Chain
Execution breakdowns rarely live in a single place. Consider a business committed to growing its recurring revenue base. The intent is clear at leadership level. But by the time it reaches the team, it has become a list of activities — each owned by a different function, each measured by a different metric, none explicitly connected to whether recurring revenue is growing or why.
The breakdown runs as a chain. Most organisations are dealing with more than one link simultaneously — and treating symptoms of the later breakdowns without addressing the earlier ones:
- Definition — The commercial value to be created is not defined precisely enough to guide real trade-offs. Ask: could every person on your team state the specific commercial outcomes the business is building toward this quarter — precisely enough to know what not to do?
- Directional — Without that definition, work expands to fill capacity instead of aligning to purpose. Ask: if you listed every active initiative right now, could you connect each one directly to commercial outcomes — and would any fail that test?
- Organisational — Structures, information flows, and rhythms don’t support the work that actually matters. Ask: does your operating rhythm — your meetings, decision cadences, reporting — actively protect your highest-leverage priorities, or does it distribute attention equally across everything?
- Executional — The work itself suffers, not because people are incapable, but because the foundations make effective execution structurally improbable. Ask: are the people doing the work clear on what success looks like — commercially, not just functionally?
Each breakdown compounds the next. Organisations that address execution quality without first addressing the three breakdowns that precede it are solving for the wrong problem.
| The M3 Framework: Mission → Means → Machine This 4-point breakdown chain maps directly onto Marinda’s proprietary M3 framework — the methodology built to close the strategy-execution gap in founder-led SMEs and scale-ups. Mission establishes commercial clarity before direction is set. Means narrows focus to the 3–5 highest-leverage bets. Machine builds the operating rhythm that keeps execution connected to strategy. |
The Precondition That Closes the Strategy-Execution Gap
Before any execution framework, operating rhythm, or technology choice can make a material difference, something more fundamental has to exist: clarity on what commercial value should actually be created — and disciplined identification of which work is most likely to deliver it.
This shows up in a recognisable pattern. A leadership team that left their planning session aligned finds itself, six weeks later, making decisions that quietly contradict the priorities they agreed on — not because anyone changed their mind, but because the pace of execution outran the clarity of the plan. New inputs arrived. Pressure shifted. And without a system to protect original intent, discernment defaulted to urgency.
Establishing this clarity requires two things:
- A commercially anchored destination — specific enough to make trade-offs visible and decisions easier.
- An honest leverage assessment — which activities have the highest probability of closing the distance between where the business is and where it needs to go.
Without both, execution defaults to doing what is possible rather than what is necessary. In an environment of abundance, where almost everything is possible, that distinction becomes the most important strategic judgment a leader can make.
When AI Adds Value vs. When It Generates Noise
AI compounds value when it is deployed against clearly defined problems — and generates noise when it is deployed against everything else. The organisations seeing meaningful impact from AI are not the ones deploying it most broadly. They are the ones deploying it against clearly defined commercial problems, with strong information foundations, in service of outcomes they can actually measure.
| AI Adds Value When… | AI Generates Noise When… |
| Commercial outcomes are defined before deployment | Activity velocity replaces strategic clarity |
| Deployed against highest-leverage, identified work | Applied broadly to everything simultaneously |
| Strong information foundations exist (customer data, domain knowledge) | Information quality is low or unstructured |
| Success is measured commercially (revenue, EBIT, retention) | Success is measured in output volume only |
| It amplifies human judgment and expertise | It substitutes for the thinking that should precede execution |
The rules of engagement are straightforward: define the commercial outcomes first, identify the highest-leverage work, build the organisational conditions to support it — then, and only then, apply AI and automation where they compound the impact. Capability and capacity without that sequence is just faster noise.
Three Questions Worth Asking Before Your Next Initiative
Before you add another initiative, approve another project, or deploy another AI tool, sit with these:
- Can you state, in a single sentence, the specific commercial outcomes your business is building toward this quarter — precisely enough that it tells your team what not to do?
- If you mapped every active initiative against those outcomes right now, how many would survive the test?
- Does your current operating rhythm — your meetings, your cadences, your reporting — protect your highest-leverage priorities, or does it distribute attention equally across everything?
The answers will tell you more about your execution discernment than any framework will. The distance between execution and meaningful commercial impact can be closed — but it closes from the inside out. With clarity and discernment, not more activity.
Impact = Clarity × Focus
Frequently Asked Questions: Strategy-Execution Gap in 2026
What is the strategy-execution gap?
The strategy-execution gap is the distance between what a business plans to achieve and what its day-to-day activity actually delivers commercially. Research consistently shows that 60–90% of strategies fail not in design but in delivery — typically because the link between individual activities and commercial outcomes is never made explicit.
Why is AI making the strategy-execution gap worse in 2026?
AI removes the resource constraints that historically forced discernment. When execution was expensive and slow, businesses were forced to ask whether each initiative was worth the cost. AI makes execution cheap and fast — which means the external pressure to choose carefully has disappeared. Without an internal system to replace it, organisations execute more, and more indiscriminately.
Why are most companies not seeing ROI from AI?
According to McKinsey’s 2025 State of AI survey and BCG research, the majority of AI value is captured by a small minority of organisations. The pattern is consistent: companies that see strong AI ROI deploy it against clearly defined problems with strong data foundations. Companies that don’t, deploy it broadly without commercial clarity.
What is execution discernment?
Execution discernment is the organisational discipline to identify which work has the highest leverage in creating commercial value — and to protect that work from displacement by lower-value activity. It is distinct from prioritisation (which is a planning act) in that it operates continuously as an embedded decision-making capability throughout execution.
How do I close the strategy-execution gap in my business?
Closing the strategy-execution gap requires addressing it at four levels in sequence:
- Definition: Establish precise commercial outcomes — specific enough to guide trade-off decisions
- Directional: Align every active initiative explicitly to those outcomes
- Organisational: Redesign operating rhythms to actively protect highest-leverage work
- Executional: Ensure every person can articulate commercial success, not just functional success
This is the work Marinda does directly with SMEs and scale-ups as a Growth Adviser, Consultant and Fractional Chief Growth Officer.
Sources & Citations
- McKinsey & Company — The State of AI 2025 (mckinsey.com)
- BCG — Capturing the AI Value Opportunity 2025 (bcg.com)
- Consistent strategy-execution failure rate: HBR, McKinsey, and academic research aggregations