Luk Smeyers

Proof.

• Path.” framework asks firms to name the important problem they solve, show why buyers should trust them, and explain how they will reach the outcome.
• AI should strengthen that proposition, not trigger a new service every time a model appears.
• He says a firm should reconsider its proposition if AI materially changes the problem, outcome, proof, or path.

Smeyers recounts a leader at a roughly 300-person UK specialist firm saying AI was pushing the firm to change its offer almost every other week. He argues that repeated changes overload teams, fragment marketing and make services harder to sell.

Read original

Smeyers argues that buyers want solutions to business problems, with a clear outcome, credible approach, and evidence—not just a list of capabilities. The test is whether accumulated expertise has made the firm easier to choose.

A data consultancy may be skilled in analytics, AI, data engineering, and governance yet still look like many competitors. Smeyers says that makes relevance harder to see, lengthens sales cycles, and increases price pressure.

Read original

The risk appears when those sources weaken or a firm seeks unfamiliar markets. A clear reason to choose the firm can help attract relevant leads and reduce reliance on individual rainmakers.

He describes the warning signs as growth slowing, referrals declining, a key rainmaker leaving, or the firm entering a new market. Without a clear proposition, winning work can then require more outreach and senior selling, with longer cycles and more price concessions.

Read original

He calls the gradual move from selling capacity toward value-based, proposition-led work “The Shift.”

• Firms should invest in recurring problems, build reusable expertise and delivery methods, and decline work that pulls them away from their focus.
• He acknowledges that established firms cannot replace hourly revenue overnight.

He argues that a firm that reinvents delivery for every client accumulates complexity and remains dependent on selling more labour. Repeated, improved methods can instead support more predictable delivery, stronger propositions, and healthier margins.

Read original

He says firms should first identify recurring client problems, codify expertise, and define AI’s role in repeatable workflows. In his investor perspective, evidence should include pricing, delivery consistency, and commercial measures—not activity alone.

His suggested measures include revenue-per-FTE trends, pipeline reliability, margin development, and repeatability. He contrasts these with firms that report growing AI use but cannot show its return.

Read original

Vague claims leave clients to work out what a firm can achieve, compare approaches, assess risk, and justify the expense. Clearer expected impact can reduce that burden.

He asks how much effort a consulting firm demands before a client can say yes, noting that unclear claims can slow a decision or stop it altogether.

Read original

Read it first for the thesis, then the Problem/Proof/Path post for how it gets applied.

Smeyers argues that consulting firms have treated full-service flexibility as a strength, redeploying people and expertise to almost any client request.

• That flexibility helped the industry grow, but it now shows up as fragmented expertise, growing internal complexity, limited repeatability, constant reinvention of delivery, dependence on selling more labour, and inefficient marketing, all of which pressure profitability.
• Product leaders face the opposite discipline: they must choose which customer problems to solve, where to invest, what to build, and what to stop doing, because they cannot rebuild the product for every request.
• AI sharpens the problem, since clients can do more work internally and technology reduces the labour required for delivery, making additional billable hours a fragile growth strategy.
• Smeyers and a collaborator called Florian describe the response as 'The Shift': moving revenue progressively from capacity-led work toward value-based, proposition-led work, with deliberate decisions about which client problems deserve sustained investment, where expertise and intellectual capital accumulate, what can become repeatable, and which requests should be declined.

For brands planning a campaign

Use this as the framing piece for a campaign aimed at consulting leadership about repositioning or AI-era operating models. The post supplies a concrete list of symptoms (fragmented expertise, complexity, limited repeatability) that a brand can use to describe the problem before presenting a solution.

It answers the question a consulting leader actually asks when a new AI model lands every few weeks, and it does so with a three-part framework a reader can reuse.

Smeyers recounts a leader at a roughly 300-person UK specialist consulting firm saying that AI pushes them to change their offering almost every other week, and responds that this is wrong thinking: a specialist firm should keep a stable core instead of chasing the AI release cycle.

• That core is 'Problem.
• Proof.
• Path.' Which important client problem has the firm built deep expertise around?
• What repeated experience and evidence give buyers confidence?
• What credible path takes a client from that problem to the desired outcome?
• AI can strengthen each part, improving diagnosis, accelerating analysis, codifying expertise, increasing delivery consistency, and making parts of the client journey faster and more predictable.
• Changing the offering instead has a commercial cost: teams get overloaded, marketing keeps rewriting the story, the proposition fragments into more variations, and salespeople cannot explain or sell the growing set of AI-flavoured services.
• Smeyers's rule is that a proposition deserves another look only when AI materially changes the client problem, the outcome, the proof, or the path; when it only strengthens how an existing high-value problem is solved, it should be folded into the current proposition.

For brands planning a campaign

A brand selling AI tools into consultancies can use Problem/Proof/Path to position its product as strengthening an existing proposition rather than as a reason to launch new services, which fits how this audience is already being advised.

It separates specialising in a capability from being easy to buy.

Smeyers points to specialist boutiques and to practices or industry groups inside large firms that have impressive expertise yet fail to convert it into client attraction, pricing power, and profitability.

• The reason he gives is that they specialise in capabilities while clients are shopping for solutions to specific business problems.
• A data consultancy or data practice may have strong analytics, AI, data engineering, and governance expertise and still present a proposition that looks like dozens of competitors.
• The commercial consequences are that buyers cannot see the firm's relevance, sales cycles get longer and more persuasion-heavy, price pressure increases because differences are hard to assess, and hard-earned expertise fails to produce the advantage it should.
• Specialisation starts paying off, in his account, when expertise is translated into propositions built around specific client problems, desired outcomes, a proven approach, and credible evidence, which requires deliberate choices about which problems to pursue, which expertise to deepen, and which opportunities to decline.
• Smeyers closes with the test question: has years of accumulated expertise actually made the firm easier to buy from?

For brands planning a campaign

For a brief about positioning, 'has this made the firm easier to buy from?' works as a diagnostic question to open a piece, especially with technical or specialist audiences who assume expertise speaks for itself.

It shows when the weakness becomes visible.

Smeyers is responding to a reader who challenged an earlier claim that most consulting firms look similar, asking whether every such firm is therefore struggling and whether clients actually care about differentiation.

• Smeyers concedes that many firms succeed on deep expertise, long-standing relationships, referrals, strong reputations, and good delivery, but says that a reliable flow of new leads is the concern he hears from consulting leaders almost daily, and that sameness contributes to it.
• Buyers do not shop for differentiation explicitly; they want confidence that a firm understands their problem, can solve it, and is an acceptable risk, and they may perceive differences the firm never designed, through relationships, reputation, industry experience, and prior engagements.
• The commercial risk is that a firm can perform well inside its existing network while giving unfamiliar buyers few reasons to engage, and that this stays hidden for years until growth slows, referrals decline, a key rainmaker leaves, or the firm tries to enter a new market, at which point new business takes more outreach, more senior involvement, longer sales cycles, fewer wins, and more price concessions.
• Smeyers treats commercial distinctiveness as a performance advantage because it attracts more relevant leads, reduces dependence on relationships and rainmakers, strengthens pricing power, and makes growth more predictable.

For brands planning a campaign

A demand-generation or brand campaign can borrow the thought experiment at the end: if traditional sources of new business weakened tomorrow, how confidently could the firm win new clients? It gives a buyer-side reason to invest in distinctiveness without arguing that referrals are worthless.

Good for a newcomer who wants the core complaint in a few lines before reading the frameworks.

Smeyers looks at consulting purchases from the client's side.

• Buying support requires more work than firms realise: buyers have to interpret broad, vague claims about expertise, work out what a firm could actually achieve in their own situation, compare approaches, assess risks, and justify the investment internally.
• When firms stay vague about the expected business impact, that discovery work falls on the client, and in a crowded market the hidden burden slows decisions or stops them altogether.
• Smeyers ends with the question he puts to firms: how much effort does a consulting firm demand before a client can say yes?

For brands planning a campaign

This post is a ready-made hook for a campaign angle about reducing evaluation friction, for example content that helps consultancies state expected business impact earlier in the buying process.

It answers how someone decides whether a consulting firm's AI work is producing commercial results or just activity.

Smeyers writes about assessing consulting firms as investment opportunities, looking at reliable commercial performance, repeatable propositions, scalable expertise, and stable profitability.

• AI makes that assessment harder, he says, because it can both improve firm economics and disrupt the traditional consulting model.
• The pattern he criticises is buying licences, giving people access, encouraging experimentation, celebrating usage, and hoping productivity turns up in the economics, which produces a consistent message from firms: 'We are using AI more, but we still struggle to prove the ROI.' That worries him as tool costs rise alongside usage, since loose experimentation can generate more activity, more token consumption, and more individual productivity stories without gains at firm, practice, or vertical level.
• In his view sustainable AI efficiency starts upstream, with knowing which client problems a firm solves repeatedly, which expertise it can codify, which delivery methods can become repeatable, and where AI improves the quality and consistency of delivered value, so AI becomes part of a commercial system rather than hundreds of personal experiments.
• The post lists what Smeyers looks for: focused propositions around recurring, high-stakes client problems; codified IP, repeatable methodologies, and delivery workflows; clearly defined AI roles inside those workflows; pricing that captures part of the added value, with evidence of premium pricing; partner ownership and incentives that drive adoption inside a defined commercial model; a resilience plan for work clients will increasingly do themselves; and evidence of 'The Shift' toward proposition-led revenue.
• Then the numbers: revenue-per-FTE trend rather than benchmark, pipeline reliability, margin development, delivery consistency, repeatability.
• Smeyers's summary line is that AI activity is easy to demonstrate while commercial progress is much harder.

For brands planning a campaign

This gives a checklist-style frame for content aimed at consulting firm owners, partners, or investors, and a natural comparison axis for a brief that needs to distinguish firms on economics rather than on AI claims.

The core, in their framing, is “Problem.

• Proof.
• Path.”: the important client problem the firm has built deep expertise around, the repeated evidence that gives buyers confidence, and a credible route from that problem to the desired outcome.
• AI should strengthen those three elements — better diagnosis, faster analysis, codified expertise, more consistent delivery — rather than fragment the story into more service variations.
• They also point out that capability-led specialisation is not enough: data and analytics practices with strong expertise can still look interchangeable to buyers because they describe what they do rather than the business problem they solve.

Problem–Proof–Path framework: define the recurring client problem, the proof that the firm can handle it, and the path to the outcome; then ask how AI makes that existing proposition stronger.

Smeyers allows the proposition to be revisited when AI materially changes the client problem, the outcome the firm can deliver, its proof, or the path to that outcome.

For brands planning a campaign

A brand selling to professional-services leaders can use this as the basis for a diagnostic offer, a repositioning engagement, or content that helps firms test whether their proposition is built around a buyer problem rather than a capability list.

Examples: Source 1 · Source 2

Their argument is that sameness hides the problem until the traditional sources of new business weaken — a key rainmaker leaves, referrals decline, or the firm enters a new market.

• At that point an unclear proposition means more outreach, more senior selling time, longer cycles, fewer wins, and price concessions.
• Smeyers treats distinctiveness as a commercial performance advantage: it supports relevant lead flow, reduces dependence on existing relationships, strengthens pricing power, and makes growth more predictable.

Smeyers explicitly notes that sameness is not the only reason lead generation has become harder and points to tighter budgets, cautious buyers, and changing purchasing behaviour.

For brands planning a campaign

Useful for a brand building demand-generation or positioning content for consulting firms. The test Smeyers poses — how confidently could the firm win new clients if traditional sources of new business weakened — can anchor a campaign brief or assessment tool.

Examples: Source 1 · Source 2

Consulting firms, in their account, have enjoyed the freedom to pursue almost any client request, and that flexibility has produced fragmented expertise, internal complexity, limited repeatability, constant reinvention of delivery, dependence on selling more labour, and inefficient marketing — pressures that AI makes harder to ignore as clients do more work internally and technology reduces delivery labour. The proposed response is “The Shift”: progressively moving revenue from capacity-led work toward value-based and proposition-led work, where engagements build on accumulated expertise, proven methods, and reusable intellectual capital.

Borrow product-management discipline: pick recurring high-value problems, let expertise and IP accumulate around them, make delivery repeatable, and decline requests that pull the firm away from its core.

Smeyers acknowledges that established consulting firms cannot suddenly abandon hourly-based revenue, presenting The Shift as a progressive move in the revenue mix rather than an immediate replacement.

For brands planning a campaign

A brand can commission or sponsor analysis of a consulting firm’s revenue mix, repeatability, and proposition portfolio, or build campaign content around the practical steps from capacity-led to proposition-led work.

Examples: Source 1 · Source 2 · Source 3

The pattern they criticise is buying licences, giving people access, encouraging experimentation, and celebrating usage while still being unable to prove ROI.

• Their concern is that loose experimentation produces more token consumption and individual productivity stories without gains at firm, practice, or vertical level.
• Their screening criteria include focused propositions around recurring, high-stakes client problems; codified IP and repeatable delivery workflows; clearly defined AI roles inside those workflows; premium pricing that captures part of the value created; partner ownership and incentives aligned to a commercial model; a resilience plan for work clients will do themselves with AI; and evidence that the revenue mix is shifting toward proposition-led work.
• They then look at revenue per FTE trend, pipeline reliability, margin development, delivery consistency, and repeatability.

Investment-style criteria list, followed by operating metrics: revenue per FTE, pipeline reliability, margin development, delivery consistency, repeatability.

For brands planning a campaign

A campaign aimed at consulting firm leaders or investors can use this as a framework for an AI-readiness or commercial-progress assessment, contrasting visible AI usage with measurable economics.

Examples: Source 1 · Source 2

Potential clients must interpret broad, vague claims, work out what a firm could achieve in their situation, compare approaches, assess risk, and justify the investment internally.

• When expected business impact is vague, that discovery work lands on the buyer, and in an overcrowded market it can slow decisions or stop them.
• Their closing question — how much effort does a firm demand before a client can say yes — frames clarity as a commercial issue, not just a copywriting one.

For brands planning a campaign

Useful for brands selling to consulting firms as a message about proposal clarity, buyer-experience audits, or content that maps the work a buyer must do before choosing a provider.

Examples: Source 1

That article and the linked newsletter issue are not included in the reviewed evidence, so their arguments cannot be summarised here; only the pointer and the topic are visible. This is an actionable gap if a campaign depends on Smeyers’s views about testimonials and social proof in consulting buying.

For brands planning a campaign

If a brief needs the testimonial or proof-material angle, obtain the full article or newsletter issue before relying on it. Otherwise, treat the newsletter as a distribution channel Smeyers promotes rather than a substantiated topic.

Examples: Source 1

Both treat published thinking as a commercial asset: Erickson calls content the product sample a prospect reads before a call, while Smeyers argues that accumulated expertise has to be turned into something buyers can recognise and act on.

Erickson concentrates on marketing consistency and the feast-famine pipeline cycle, including why marketing always loses to delivery pressure when the principal owns it. Smeyers works more from proposition design, firm economics and the effect of AI on delivery and pricing.

Read original · Original 2

Both describe creating a practical assessment tool to help a leadership team judge where its commercial position is strong and where the gaps are — Bagley for Amazon Vendor negotiations, Smeyers for AI-era consulting propositions and firm economics.

Bagley's subject is Amazon Vendor economics and channel negotiation, with hands-on guidance on Net PPM, terms and margin. Smeyers stays at the level of consulting firm strategy, propositions and investment criteria across professional services.

Read original · Original 2

Hauser's test — revenue alone does not determine value; profit, cash flow, concentration risk and owner dependence do — sits close to Smeyers's questions about repeatability, pricing power and revenue mix.

Hauser's audience is law firm owners and his frame is building a firm worth owning or selling, delivered through coaching events. Smeyers writes about consulting firms, propositions and AI, and frames the same commercial discipline as a move from capacity-led to proposition-led work.

Read original · Original 2

The two reviewed posts address a clear operating problem: firms stop marketing when delivery gets busy, then panic when the pipeline empties, and treat the principal's full calendar as health rather than a lagging indicator.

• Erickson argues that content is the product sample a consulting buyer uses before any call, and that authority built through specific points of view drives inbound trust.
• That is a more direct alternative for a brief on consistent demand generation, content-led authority or preventing the feast-famine cycle at a boutique professional-services firm.

Erickson does not supply the AI-driven consulting economics frame that is central to the Smeyers brief.

• There is no Problem, Proof, Path core, no shift from capacity-led to proposition-led revenue, no investor screening list separating AI activity from commercial progress, and no operating measures such as revenue per FTE, margin development or delivery repeatability.
• A buyer using Erickson for an AI-economics analysis would have to supply that analytical structure elsewhere.
• The reviewed posts also do not show commercial formats, pricing or an inquiry route, so availability for a commissioned analysis is not established by the posts alone.

Examples: Source 1 · Source 2

The reviewed posts show a specific diagnostic product: a ten-question, three-minute AVN Commercial Readiness Assessment built around whether a Vendor team can explain Amazon's Net PPM position, judge which existing terms remain commercially justified, improve Amazon's economics without giving away more margin, and align Finance, Ecommerce and leadership on negotiation boundaries.

• Bagley also describes refining a boutique agency's positioning around senior, commercial Amazon expertise and deeper client relationships.
• That is a better alternative for an Amazon Vendor brand, an ecommerce leadership team or a boutique agency that wants a self-assessment asset and a tighter positioning story.

The subject matter is a different buying problem.

• Bagley's material is about Amazon Vendor negotiations and boutique ecommerce advisory, not consulting-firm economics or AI-driven proposition strategy.
• It does not offer a framework for deciding which AI services a consulting firm should build or stop, how to prove AI ROI at firm level, or how to move a consultancy's revenue mix toward repeatable, proposition-led work.
• A buyer wanting a consulting-sector analysis would get a strong example of diagnostic design and boutique positioning, but not the consulting economics content itself.
• Audience composition and commercial availability are also not established in the reviewed posts.

Examples: Source 1 · Source 2

The reviewed posts describe how to build a law firm that is worth buying even if the owner never sells: profit, cash flow, adjusted EBITDA, concentration risk and dependence on the owner matter more than revenue alone.

• Hauser also shows the SMB Team Quarterly Coaching Event as a working format for bringing firm owners together over several days.
• That is a better alternative for a legal-sector audience, a practice-valuation or exit-readiness brief, or a campaign that needs a live coaching event rather than a written analytical framework.

This is a different sector and a different decision.

• Hauser's posts do not cover consulting-firm AI economics, proposition strategy, product-management discipline or the shift from capacity-led to value-based work that anchors the Smeyers brief.
• The investor lens is applied to law firms rather than consulting firms, and the reviewed material does not provide a method for assessing AI ROI inside a consulting practice.
• A buyer would need to translate the law-firm valuation questions into the consulting context and would still lack the AI-economics argument.
• No audience measurements, sponsorship formats or inquiry route appear in the reviewed posts.

Examples: Source 1 · Source 2