For decades, the five-year strategy has been a familiar feature of corporate planning. Organisations set a destination, establish their investment priorities and build a roadmap to take them there. That model depends on confidence that the assumptions behind the plan will remain valid, and that confidence is becoming much harder to sustain.
AI is accelerating the problem, but it is only one part of a wider shift. Technology cycles are shortening, workforce requirements are changing, new commercial models are emerging and organisations face more choices than ever. The question is how to make long-term decisions when the conditions around them keep moving.
That question was at the heart of a recent Leading Lights roundtable. Hosted by Roc, Leading Lights brings together IT leaders from a range of sectors to compare experiences and challenge each other’s thinking. This article draws on that conversation to ask how organisations can keep a clear strategic direction without pretending they can predict the next five years.
Long-term strategy is not going away, but its purpose is changing.
A business still needs a clear view of what it wants to become, particularly where major investment is involved. Boards need visibility of strategic intent, likely priorities and the capabilities the organisation will have to develop. What is losing credibility is the assumption that the route to that future can be plotted in detail years in advance.
A more realistic model separates long-term direction from short-term planning. The five-year view becomes a North Star: clear enough to align leadership and guide investment, but not so rigid that it locks the organisation into assumptions that may no longer hold. Beneath it, the executable plan operates over a much shorter horizon, with a sharper focus on the next 12 months and on no-regret decisions.
That changes what today’s investments are for. Spending on data, infrastructure, security, governance and skills matters less because it serves one fixed destination and more because it preserves future choices. The question shifts from “what do we need in five years?” to “what do we need in place now so that we can respond well when the next change comes?”
Flexibility and indecision are different things. Where the market, technology or operating model is still developing, holding some choices open is often the right call. Strategy then becomes less about predicting every stage of the journey and more about building the capability to adapt without losing direction.
The ability to adapt depends heavily on the quality of the foundations already in place.
It is natural to associate progress with whatever is newest. AI, automation and new digital services attract attention because they promise visible change, but they still depend on the basics working well. Poor data remains poor data when AI is placed on top of it. Weak processes do not become effective simply because they are automated. Weak governance becomes more consequential when technology increases the speed and scale of decisions.
The challenge for technology leaders is that investment in fundamentals is harder to position as transformational. Modernising infrastructure, improving resilience, strengthening governance and fixing data quality rarely carry the appeal of a new capability. Yet these are often the investments that decide whether future innovation succeeds.
This also changes how organisations should think about transformation. Wholesale replacement creates a visible sense of progress, but it can also create long-term dependencies and reduce room to manoeuvre. Iterative improvement is often the more strategic option because it strengthens the environment while preserving flexibility. There is a balance to strike, though: foundational investment can create lock-in of its own, and “iterative” must not become an excuse for never retiring legacy systems.
The same discipline applies to technology selection. Vendor roadmaps, analyst rankings and market momentum can all influence decisions before the business problem has been clearly defined. The order needs to run the other way: be clear about the problem, understand the process that needs to improve and define what the outcome should look like before deciding which technology or supplier fits.
It matters because technology choices shape operating models for years. Organisations should not end up following a supplier’s strategy simply because they adopted the platform first and asked the strategic questions later.
The uncertainty affecting technology strategy is now affecting people strategy.
Organisations have traditionally been able to make reasonable assumptions about the skills and roles they will need in three or five years. That is much harder now. AI, automation and changing operating models are shifting the balance between technical skills, judgement, creativity, commercial understanding and the ability to work across disciplines. Roles are likely to change before many organisations have finished recruiting for them.
One response has been the growing search for “unicorn” candidates: people expected to combine an unusually broad range of technical, commercial and interpersonal capabilities. That may be a symptom of the problem rather than the solution. If organisations cannot confidently predict which skills they will need, continually adding requirements to today’s job descriptions narrows the talent pool without resolving the underlying uncertainty.
A more resilient approach is to place greater value on aptitude, attitude and the ability to learn, then invest properly in training and development. If future requirements are hard to predict, organisations need people who can grow with the role rather than people who happen to match an increasingly specific list of current requirements.
That has implications for how organisations think about capability. The question is less whether every skill already exists in one individual and more how the organisation builds, accesses and develops capability over time. Some expertise will need to remain in-house, some can be developed and some can be accessed through partners and specialist networks. Uncertainty about the future is not a reason to demand impossible certainty from candidates today.
There is a cultural consequence too. Employees hear constant talk of AI, automation and productivity while many remain unclear about what it means for their own roles. If organisations want people to adapt, they need to explain how work is likely to change, where human judgement continues to matter and what opportunities there will be to develop.
That makes training and workforce development part of strategy rather than simply an HR activity. Organisations may not be able to define every role they will need in five years, but they can build a workforce better equipped to move with them.
AI brings many of these questions into sharper focus because it affects more than technology capability. As it becomes embedded in day-to-day operations, it changes how work is allocated, how costs are incurred, how decisions are made and where accountability sits.
Token-based pricing creates a very different relationship between usage and cost. It is no longer enough to know what an AI service costs; organisations need to understand what drives consumption and how it maps back to activity and output. That gets harder as AI agents begin making choices themselves. If an agent decides which model to use or how many interactions a task requires, technology consumption is no longer entirely the result of a human decision.
This points to the need for a new efficiency layer around AI: in effect, routing each workload to the model that best balances capability, cost and required outcome, much as cloud cost management (FinOps) emerged to control cloud spend. The most powerful model is not necessarily the right one for every task, and organisations and vendors that get better at making those choices stand to gain a real commercial advantage.
Approved AI tools inside the organisation are not the same as unrestricted use of public ones, but employees may not automatically see the distinction. Clear guardrails are essential, particularly around data, intellectual property and security. Governance also has to do more than restrict access: it needs to give people safe, workable routes to use AI productively.
The deeper question is who is responsible. An AI system may recommend, automate or increasingly execute decisions, but responsibility for the outcome still sits with the organisation. As autonomy increases, governance must become clearer about what AI can decide, where human intervention is required and who owns the consequences if something goes wrong.
AI adoption has an environmental cost, while many organisations are working towards sustainability and net-zero commitments. AI capability is also increasingly built into platforms by default, so organisations may consume extra compute without ever making a conscious choice to do so. That creates a real tension between rapid adoption and environmental ambitions, particularly where organisations depend on the sustainability performance of major technology suppliers.
Taken together, these pressures make it more important to separate valuable AI use cases from activity driven by novelty. If AI carries financial, environmental and organisational costs, the case for using it has to start with the problem being solved and the value being created.
Organisations still need direction, but good strategy no longer means predicting the future in detail. The stronger measure is how well prepared the organisation is to respond: robust foundations, room to manoeuvre, people who can develop as requirements change and clarity about the problems technology is being asked to solve. Some decisions should be made now; others are better left open until more information arrives, and the judgement lies in knowing the difference. AI is making that challenge more visible, but it did not create it. It is exposing weaknesses that already existed in long-term planning, workforce strategy, governance and technology decision-making.
The value of strategy may increasingly lie not in predicting what the next five years will bring, but in ensuring the organisation is ready to make good decisions when the future becomes clearer.
Strategic Transformation
For over 25 years, Nigel has led and advised high-growth technology and transformation businesses across global markets. He has worked across sales leadership, process transformation, operating models, AI, automation, go-to-market strategy and business growth. Nigel is a published author and co-founder of The Compassion Exchange, focusing on bringing compassion into the workplace.
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