Strategy Consulting After AI

Generative artificial intelligence (AI) has redrawn the boundaries of strategy consulting. Work that once absorbed weeks of interviews, modelling and slide production can now be completed in hours. Market scans, competitor benchmarks, scenario drafts and even polished strategy narratives are no longer scarce outputs. The productivity gain is not marginal but is a structural shift in the economics of strategic advice. This change matters because much of traditional strategy work rested on analytical intensity and presentation craft. If that is where value is perceived to lie, technology will compress margins and erode differentiation. Moreover, AI is also creating a new class of strategic problems that organisations struggle to solve precisely because they are systemic. With AI comes more than a tool to optimise activities, it is reshaping value propositions, revenue models, operating structures and skill requirements. In the next generation of strategy consulting, the winning firms will reposition from being expert analysts to becoming what I call “fit architects”. I envision them as professionals who design, test, and continuously recalibrate the alignment between strategy, business model, and organisational renewal in AI-shaped environments. This is a new value proposition for companies.

Strategic performance is about ensuring coherence between (1) strategic choices, (2) the business model that operationalises them, and (3) the innovation practices that sustain them. When these elements diverge, performance suffers. A firm cannot claim to differentiate through advanced AI-enabled services while governing experimentation through rigid cost-control routines. Such inconsistencies create internal friction.

Today, AI intensifies these tensions by shifting business models more quickly with personalised services, automation or predictive offerings. Revenue logics are also evolving towards subscription, usage-based or outcome-based pricing. Furthermore, operating models must accommodate new forms of data governance, model risk management, and software delivery. Finally, renewal is a continuous process. As AI capabilities evolve rapidly, strategic renewal becomes an ongoing cycle, where it was a “transformation program” with an end date.

In short, AI reduces the cost of producing strategic options while raising the cost of getting the system of choices to hang together.

If coherence is the scarce resource, the role of the consultant must evolve accordingly. Firstly,they diagnose misalignment across the whole system. Traditional strategy engagements often start with questions of market attractiveness or competitive positioning. Fit architecture starts by examining where the organisation is incoherent. In the AI context, this may appear when a firm invests heavily in AI-enabled differentiation but continues to treat AI as a support function governed by restrictive procurement rules. Another possible misfit is not adapting the metrics and tracking tools to the new reality. AI relies heavily on data for training and being efficient, hence data management policies are central to avoid misfit. The issue is not technological competence alone but the absence of systemic alignment. Fit architects’ role will be to make these inconsistencies explicit and to assess their performance implications.

Secondly,fit architects design business model change as a strategic choice architecture. AI creates opportunities for new forms of customer value, but realising them requires decisions about revenue logic, asset ownership, partnerships and the degree of standardisation. These decisions are straightforward to describe yet difficult to coordinate. Recent research increasingly treats the business model not as a “nice depiction” of strategy, but as a central unit where strategic choices are made, tested, and made coherent. Fit architects help leaders move beyond slogans to an integrated configuration design. This is a new decisional environment where strategic choices, activity systems, governance, economics, and execution logic reinforce one another.

Thirdly, the strategic fit architects operationalise strategic renewal as a repeatable capability. In many organisations, “renewal” remains tied to leadership transitions or a periodic planning exercise such as welcoming a new CEO or implementing a new business plan. AI disrupts that model by pushing firms in continuous renewal cycles in a fast-moving environment where sensing shifts, seizing opportunities, reconfiguring assets and processes, as well as learning faster than rivals are the new norm. This requires clear portfolio rules, decision rights that enable speed without sacrificing control, incentive systems that sustain experimentation and metrics that connect AI adoption to business outcomes. Supporting these mechanisms draws on the most human dimensions of consulting: clarifying trade-offs, mediating competing interests and fostering executive alignment.

When you translate the “fit architect” role into practice, five types of intervention stand out, each harder for AI to commoditise because they require organisational judgment, design, and governance.  One of them could be described as “Strategic Fit Stress Test”. Rather than starting with market opportunity, it subjects the organisation to a structured assessment of organisational coherence. Where do stated ambitions conflict with incentive systems? Where does the business model contradict operating routines? Where does the innovation portfolio drift away from economic logic? The value of such an exercise lies less in diagnosis for its own sake than in clarifying which inconsistencies matter most for performance.

A second intervention resembles what might be called “AI Business Model Design Sprints”. The objective is to confront the hard choices around value creation and capture led by the implementation of AI systems, it is an emphasis on configuration of the organisation. If AI enhances speed, personalisation or risk reduction, how is that translated into pricing logic, cost structure and ecosystem positioning? How do those choices reshape organisational responsibilities?

A third domain concerns the construction of a “Strategic Renewal Operating System”. This type of intervention aims at designing the cadence for revisiting assumptions, clear rules for allocating resources between exploration and exploitation, and governance mechanisms that prevent fragmentation.

Closely related is the question of “Innovation Portfolio and Scaling Governance”. AI programmes often multiply as isolated pilots which demands explicit criteria and disciplined oversight to come to fruition. It relates to deciding which experiments deserve scaling, which should be terminated, and which must be protected.

Finally, there is the embedding of AI into core decision processes, what might be coined “AI-Augmented Strategy Processes”. To avoid technological sophistication to coexist with organisational inertia, analytical tools must be integrated into accountability structures and leadership behaviour.

Taken together, these practices are not a new subfield labelled “AI strategy”. They reflect a broader reorientation of the profession toward designing and maintaining strategic fit under conditions of accelerating complexity.

The hardest part of strategy is rarely generating options. It is making choices mutually reinforcing, then sustaining those choices through execution and renewal. AI shifts the basis of advantage from insight production to coherence production. That is why the future of strategy consulting is not a race to adopt better models for slide making. It is a race to become better designers of strategic fit, system-architects who help leadership teams keep strategy, the business model, and renewal mechanisms aligned as technologies and markets move faster. Consequently, AI will make “answers” cheaper, and alignment more valuable.