An insurance firm kept solving the same problem. Four times, for four different carriers, from scratch.

Carriers drowning in claim forms, policy files, medical reports, and underwriting packages were coming to them repeatedly, each time as though it were a new engagement. Each time, the firm built a custom AI solution from scratch.

After the fourth build, someone in the room asked the question that changed the commercial trajectory of the firm: “why are we doing this again?”

The answer was that they were not wired to do anything else. Their model billed for effort, not for accumulated knowledge.

Amol Punekar, Regional Director of Business for Europe and the UK at Lyzr AI, has watched this pattern repeat across mid-market consulting firms throughout his career scaling commercial functions at Thoughtworks, Slalom, AND Digital, and Valtech. The firms that break it, he argues, are the ones that recognize a repeatable delivery as something more valuable than a project. They recognize it as a product.

TL;DR

• Mid-market consulting firms are being squeezed from both sides: large firms are productizing their offerings from above, and AI-native boutiques are delivering outcomes at speed from below. The model that got a firm to 20, 50, or 100 million in revenue will not protect it over the next 3 years.
• The starting point for productization is pattern recognition. If your firm has solved the same business problem 3 or more times in the last 18 to 24 months, you already have a candidate. The question is whether it passes 3 filters: market size, client pull, and monetization clarity.
• A cross-functional team of 5 to 6 people, a ring-fenced budget of €150K to €200K, and CEO-level sponsorship can take a single use case from pattern to piloted product in 90 days. Without CEO-level protection, it will not survive contact with the quarterly billing cycle.
• The financial case is in the margins. Productized services generate 45 to 50%+ gross margin, improve win rates from roughly 25% to 40 to 50% in a focused sector, and lift exit EBITDA multiples by 1 to 2 times relative to pure-services peers.

Your Revenue Model Has a Ceiling. Most Firms Hit It Before They See It.

Ask the managing directors of mid-market consulting firms how the business is going, and most will tell you it is fine. Revenue is holding. Some projects are strong. The team is busy.

Then ask them how it feels.

"They're working harder to maintain growth. They're adding headcount, they're winning more projects. But it feels a little fragile, and they feel like they are on a treadmill and the treadmill keeps speeding up all the time," says Amol Punekar, who has spent the last several years advising mid-market consulting firms on growth strategy after senior commercial leadership roles at Thoughtworks, Slalom, AND Digital, and Valtech, firms ranging from 11,000 to 12,000 people.

He now works with consulting organizations as Regional Director of Business for Europe and the UK at Lyzr AI, an enterprise agentic AI platform, and has watched the same pattern repeat across firms at every stage of the revenue curve.

The pattern shows up in firms that look healthy on paper and feel precarious to lead. Current revenue is real. The model producing it is becoming less defensible by the quarter. Clients are shortening engagement cycles, compressing daily rates, and in more and more conversations asking for the same output at lower cost, a pressure that is landing across the mid-market with increasing regularity.

"Slow erosion is harder to notice than sudden decline."

The firms he worries most about are growing at reasonable rates, maintaining margins, winning new work, while the commercial model underneath is quietly losing ground. And at every pitch, the same question keeps appearing: "If you've solved a problem with me 3 times, why, as a consulting firm, are you starting all over again?"

That question is a signal. The firms that treat it as one will be better positioned than the firms that answer it with a new proposal.

Two Forces Are Compressing Your Market. Here Is Exactly What They Are.

The structural reason for the erosion is a competitive squeeze that most mid-market firms underestimate because neither threat looks fatal on its own.

From above, the large firms, Accenture, Deloitte, and their peers, are investing heavily in platforms, partnerships, prebuilt frameworks, and productized methodologies. They are selling repeatable infrastructure backed by global brand and deep client access.

From below, a generation of AI-native and data-native boutiques has emerged with outcomes attached to a platform. These firms are small, hyper-focused on a single industry or problem type, and fast. They arrive at a client conversation with a working solution already built for that sector, so the question they are answering is not "can we solve this?" but "how quickly can we deploy what we already have?" They compete on specificity and speed to value in a way that a generalist proposal cannot match.

"You're too small to compete on the big scale and you're too big to compete on price," Amol says. "You've got to compete on outcomes in this world."

The firms caught in the middle are competing on relationships and reputation, which matters, but which Amol describes as a fragile position when it is the only thing on offer. Pure services revenue is linear: more work requires more people, more people requires more margin to fund, and the ceiling on growth is the ceiling on headcount. Clients know this dynamic and apply pressure accordingly. "Your revenue is linear. The clients know it, and they are putting pressure on these companies every time."

Selling time and materials, project by project, without real IP behind it, means competing in a market that is contracting against firms moving faster from both directions.

IP-Led, Productized, SaaS, Accelerator: A Precise Map of Four Models and Why Only One Is Right for Your Firm

Before anything else, a definition. The word productization is used loosely enough in the industry that two managing directors can have an entire conversation about it while describing completely different things. Amol draws a precise map of 4 models, and the distinctions matter.

The SaaS trap is worth naming directly. Amol has seen it repeatedly: a consulting firm, often owner-led, decides to build a software product on the side because the recurring revenue model is attractive. What follows is typically a painful lesson in the gap between building a product and running a product business. The go-to-market motion is different. The talent model is different. The customer success function is different. The capital requirements are different. "SaaS is a standalone product model. I wouldn't advise companies to go down that route because organizations are wired very differently."

The lane Amol recommends for mid-market firms is productized services, offerings where 80% of the delivery is standardized and reusable, and the remaining 10 to 20% is client-specific customization. You are selling an outcome with a pre-built path to it, so you are not reinventing the foundation from scratch on every engagement.

"Productization does not replace services; it strengthens them," he says. "It allows you to sell repeatable outcomes instead of undefined effort. It's about turning knowledge into assets. And assets create leverage."

Your Delivery History Contains the Answer. Here Is How to Find It.

The most common mistake firms make when they start thinking about productization is looking forward. They run workshops about what they could build, what the market might want, what the future of their industry looks like. Amol's advice runs in the opposite direction: look at what you have already built, and find the pattern inside it.

"Look for repeatable architecture. Look for measurable ROI. Look for industry specificity. Specific business problems are the foundations of productization."

His starting framework is the 3-time rule: if your firm has solved the same business problem 3 or more times in the last 18 to 24 months, you have a productization candidate. The qualifier matters. Amol is explicit that the number is a heuristic. "It could be three, it could be two, it could be four — but where you have confidence." What the rule is pointing at is a pattern of repetition that has generated genuine organizational learning.

That learning moves through 3 named stages, and knowing which stage you are in determines how ready you are to productize:

The first time teaches complexity. You are solving the client's specific problem. Every design decision is shaped by their constraints, their environment, their internal politics. The solution is bespoke, and nothing about it is reusable yet.

The second time teaches refinement. You start to see the shape of the pattern. Some architectural choices look familiar. Some tools reappear. But significant bespoke decisions remain, and you cannot yet be certain which components would transfer to a third client. You are learning what the repeatable core might be, without being able to confirm it.

The third time teaches economics. The pattern clicks. You can see which components are genuinely repeatable, which decisions are always the same, and what the cost structure of the next delivery would look like if you had pre-built the foundation. "That's when you think of that as a product-to-market fit," Amol says.

Repeated delivery alone is not enough. Before committing to a candidate, Amol applies 3 filters, the productization candidate criteria:

Market size: Is the business problem specific enough to be replicable, but common enough to represent a real market? A use case that is too narrow has no commercial future. One that is too broad resists productization.

Client pull: Are clients coming to your firm for this problem specifically? Organic, repeated inbound demand around a specific problem type is one of the clearest signals that a market exists and that your firm owns a credible position in it.

Monetization clarity: Can you describe, concisely, how you would charge for this and what the client receives in return? A vague answer here means the offering is not ready.

Once you have run your delivery history through the 3-time rule and the 3 filters, Amol recommends a structured use case identification workshop, bringing together your product and delivery leadership, your industry leads, and your commercial leads. The question is backward-looking: what business problems have we solved 3 or more times in the last 18 months? For each answer, apply the 3 filters. Most firms will surface 5 to 7 candidates.

Then comes the discipline that separates the firms that make this work from the firms that do not: "Pick one. Don't pick five. Don't pick two. Pick one."

Trying to productize multiple offerings simultaneously dilutes the focus, the funding, and the organizational commitment that the first one needs. The first productized service is proof to your own organization that you can execute this kind of shift. It needs to succeed on its own terms before anything else is built behind it.

90 Days, One Use Case, One Pilot Client: The Build Plan That Works

Finding a candidate is the analytical work. The next question is operational: what happens from the moment you decide to build?

Amol's 90-day structure is designed to get from pattern to piloted offering with a real client, real data, and real outcomes, quickly enough to generate feedback before organizational momentum stalls.

Month 1: Identify and prioritize.

Run the use case identification workshop with your cross-functional team. Apply the 3-time rule and the 3-criteria filter to your delivery history. By the end of week 4, commit to one high-priority candidate. The output is a decision: this specific business problem, in this specific sector, is what we are building.

Month 2: Build the accelerator and package the commercial offering.

Take everything your firm has learned across 3 or more deliveries and extract the repeatable components. What architecture decisions were made the same way each time? What tools appeared repeatedly? What templates, data models, or integration patterns could be pre-built for the next engagement? These become the accelerator, the technical foundation that sits inside the productized service. Alongside the build, develop the commercial packaging: what is included in the base offering, what are the add-ons, what is the pricing model, and how do you enable your sales team to present this differently from how they have been doing it?

Month 3: Pilot with a real client.

A real client, ideally one you already have a relationship with, who trusts you enough to experiment alongside you. The pilot should generate real data, face real constraints, and produce real outcomes. By the end of month 3, you have deployed your accelerator in a live environment and have something to show the market.

What the insurance case shows.

A firm Amol worked with had a strong presence in the insurance sector and kept returning to the same client problem: carriers drowning in claim forms, policy files, medical reports, and underwriting packages that required heavy manual processing at high cost and slow turnaround. Each engagement was a custom AI project built from scratch, with no carryover of architecture, templates, or models from the previous build.

After recognizing the pattern, the firm changed its approach. They built a standardized document intelligence accelerator specifically for insurance workflows, with pre-built models, templates, and automated processing components tuned for the claims and underwriting environment. They ran it 3 to 4 times, refined the components, and took it to market as a structured offering with defined scope and repeatable economics. Monetization followed two paths: IP licensing of the platform to clients, and a services layer that was substantially smaller in scope and higher in margin than the bespoke engagements that preceded it. The outcome was a new commercial identity in that sector, built from work the firm had already been doing.

The team that executes the 90-day build is small by design: 5 to 6 people, cross-functional, with a dedicated product owner who is fully engaged on this initiative. Amol recommends a product owner, an industry lead, a strong architect who can see repeatable patterns, and a commercial lead. "Your best people should be on this one," he says.

The budget benchmark is €150,000 to €200,000, ring-fenced and treated as cost of sale. "It signals to the organization 'this is real.' It's not optional; it's something we are building."

CEO-level sponsorship is the governance requirement Amol treats as non-negotiable. "Without protection, it will die. You need to ring-fence it. Otherwise, sales will revert to short-term billing because their incentives are tied very differently." Productization is strategic business model transformation. It demands the same top-down commitment a firm would require from a client undertaking the same kind of change.

What the co-creation conversation actually sounds like. When approaching the pilot client, Amol's recommended framing is deliberately low-threat: "We have a repeatable problem, and we can solve this for you very differently. That requires us to build something. We would like to extract reusable components as part of this exercise, and we will offset the cost, or a significant part of it, in exchange for your commitment to work with us closely." The IP stays with your firm. The client receives a perpetual license to use what you build together.

One hard rule on pricing: do not go to zero. "Free means no commitment," Amol says. You need business sponsorship from the client side, access to their business owners, and their full engagement throughout the build. A client who paid nothing for the engagement has no skin in the outcome.

45% Gross Margin. Better Win Rates. A Higher Exit Multiple. The Numbers Behind Productization.

Before the projections, one question worth sitting with honestly:

If your firm lost 15 to 20% of its revenue tomorrow, which clients would you want to lose?

The ones where you are simply billing time, with thin margins and no carryover from one engagement to the next? Or the ones where you are delivering recurring, high-margin components and the client returns because you have built something they depend on?

If the answer is immediately clear, you already know something important about your current revenue portfolio. If you find yourself hesitating, unable to identify which revenue you would protect and which you could afford to lose, that hesitation is a diagnostic. Your portfolio may not have a strong enough foundation of defensible, high-quality revenue to sustain the next phase of growth.

The financial case for productization operates on 3 dimensions: margin, revenue trajectory, and exit value.

Gross margin is the headline metric. Amol's benchmark for productized services is 45 to 50%+ gross margin, meaningfully higher than standard delivery economics. The services layer sitting on top of a productized offering is also smaller and higher-margin than a bespoke engagement, because the foundation is already built.

Revenue trajectory, with Amol's qualifications intact:

The timeline to see a productized offering at genuine scale in the market is 15 to 18 months. Leadership teams that expect results in month 4 will revert to the old model before the new one has had time to establish itself. That reversion is the most common failure Amol observes.

The compounding effects build quickly once the model is established. Amol projects win rates moving from roughly 25% to 40 to 50% in a sector where your firm has a focused productized offering, because you are presenting a solution that already exists in a sector where you have built visible expertise, rather than a proposal to build one. He has observed future deal pipelines increase by 20 to 25% in firms that have made this shift, and top-line revenue uplift of 7 to 10%+ from adding an IP-led asset model to the core offering.

Exit value. The structure of your revenue is one of the most important variables in your exit multiple. Market data on consulting and IT services M&A shows that productized service components increase EBITDA multiples by 1 to 2 times relative to pure-services peers. Firms with hybrid models combining services delivery with recurring, IP-led revenue components consistently reach 8x EBITDA or higher in transactions, against a baseline of 3.5x to 5x for pure project-based consulting firms. The shift from project revenue to recurring, IP-led revenue is a valuation decision as much as a strategic one.

The Bigger Picture

The firms that will make this shift share one characteristic that has nothing to do with budget, team quality, or analytical sophistication.

"The firms that will thrive are the firms which are willing to cannibalize their own revenue," Amol says. "The firms that shrink will be the firms which cling to the old model."

Going slower in the short term, absorbing the discomfort of a business model in transition, protecting a small team building future revenue while the rest of the organization continues billing for current revenue, this is harder than it sounds when the quarterly numbers are on the line. It is also the only test that matters. The analytical work is not the hard part. The commitment is.

"Don't wait for the perfect moment. Most companies have a strong client portfolio. They've seen repeatable patterns. Find those patterns."

Frequently Asked Questions

◾️ What is the difference between a productized service and a SaaS product for a consulting firm?

A productized service is a structured consulting offering with defined scope, pre-built components, and 80%+ reuse across engagements. The firm still delivers and customizes, but the foundation is already built. A SaaS product is a standalone software business with a fundamentally different go-to-market motion, organizational structure, talent model, and capital requirement. For most mid-market consulting firms, attempting a SaaS business means entering a market they are not wired to compete in. The productized services lane delivers many of the commercial benefits including recurring revenue, defensible margins, and client stickiness, without requiring a full business model rebuild.

◾️ How do you know when a service is ready to productize?

The 3-time rule is the practical starting point: if your firm has delivered a solution to the same business problem 3 or more times in the last 18 to 24 months, the pattern exists. The number is a heuristic. Two deliveries may be enough if the pattern is clear; 4 may be needed for confidence. What you are looking for is organizational learning compounded across repetitions, the point at which you know which components are genuinely repeatable, what the cost structure looks like, and what the client outcome reliably is. Then apply 3 filters before committing: is the market large enough, is there genuine client pull, and is there a clear path to monetization?

◾️ What does CEO-level sponsorship actually mean in practice for a productization initiative?

It means the CEO is personally accountable for protecting the initiative from competing priorities, specifically from the quarterly billing pressure that will otherwise redirect the team toward short-term revenue. In practice: the team is ring-fenced and not pulled onto client work; the budget is protected and not subject to quarterly reallocation; and the CEO personally reviews progress on a weekly sprint cadence during the first phase. Without this level of protection, sales teams whose incentives are tied to billing will consistently prioritize client work over an internal build that will not generate revenue for 6 to 18 months.

◾️ How should a consulting firm structure the commercial terms when co-creating a productized service with a pilot client?

The IP stays with the consulting firm. The client receives a perpetual license to use what is built together, typically at reduced or offset cost during the co-creation phase. The conversation with the pilot client does not need to begin with "we are building a product and you are the use case." A more effective entry is: "we have solved this problem repeatedly and we believe we can solve it very differently for you, using reusable components we will build as part of this engagement." Pricing at zero is a mistake: a client who has paid nothing has no commitment to providing access, business sponsorship, or honest feedback.

◾️ What team do you need to build a first productized service in 90 days?

A cross-functional team of 5 to 6 people: a dedicated product owner fully engaged on this initiative, an industry lead with domain expertise in the target sector, a strong architect who can identify repeatable patterns and build for reuse, and a commercial lead who can develop pricing and enable the sales conversation. The product owner role is the most critical appointment, with full attention on the build and not split across client delivery. Amol's budget benchmark for this team and the build is €150,000 to €200,000, ring-fenced and treated as cost of sale.

◾️ What financial returns should a mid-market consulting firm realistically expect from productization?

In year 1, expect productized services to represent 1 to 2% of total revenue. The primary value is proving the model and generating the first proof points. From year 2, with additional use cases in the portfolio, the share can reach 5 to 10%. By year 3, with focused execution and 1 to 2 new use cases added per year, 10 to 15% of revenue from productized services is achievable. Gross margin on productized services should reach 45 to 50%+, compared to standard delivery economics. The timeline to see an offering at genuine scale is 15 to 18 months. Firms that expect results in month 4 will typically revert to the old model before that window closes.

◾️ How does productization affect win rates and pipeline volume?

In a sector where your firm has a focused productized offering, Amol projects win rates improving from roughly 25% to 40 to 50%, because you are presenting a solution that already exists rather than a proposal to build one. Clients are increasingly asking where the acceleration comes from, and a firm that can demonstrate a pre-built, sector-specific solution answers that question in a way a generalist proposal cannot. He has also observed future deal pipelines increase by 20 to 25% in firms that have made this shift, driven by a clearer market identity and stronger referrals from clients who have seen the productized offering deliver.

◾️ What are the most common mistakes consulting firms make when they attempt to productize?

Three patterns Amol sees repeatedly. First, launching with too many candidates simultaneously: trying to productize 3 or 4 offerings at once dilutes focus and funding to the point where none succeeds. Second, expecting results too quickly: leadership teams that do not see revenue within 6 months will often redirect the team back to billing work before the offering has established itself. The realistic timeline is 15 to 18 months to market scale. Third, insufficient governance: without ring-fenced budget and CEO-level sponsorship, the initiative gets deprioritized in every quarter where a client project competes for resources. The build dies slowly, without a formal decision to stop it.

◾️ Is productization only relevant for large consulting firms, or can smaller mid-market firms execute it?

Smaller mid-market firms are better positioned to make this shift than larger ones. They move faster, adapt more quickly, and make decisions without the organizational weight of a large firm's governance structures. Larger firms have the resources but also the inertia, and Amol is skeptical that what passes for productization at large consulting firms is always genuine. "A lot of these companies may not have that element of IP-led innovation" underneath the marketing. A 50-person or 100-person firm with a clear pattern and a committed leadership team can build and take a productized service to market in 90 days. A 5,000-person firm is more likely to run a pilot that never scales.

◾️ How does having productized services affect a consulting firm's valuation at exit?

The structure of revenue is a primary driver of consulting firm M&A valuation. Market data on consulting and IT services transactions shows that productized service components increase EBITDA multiples by 1 to 2 times relative to pure-services peers. Firms with hybrid models combining services delivery with recurring, IP-led revenue components consistently command 8x EBITDA or higher in transactions, against a baseline of 3.5x to 5x for pure project-based consulting firms. The drivers are predictability, defensibility, and reduced partner concentration risk: a firm whose revenue depends partly on a productized offering that clients renew is structurally less risky than one whose revenue depends entirely on winning new projects.

About the guest:

Amol Punekar is Regional Director of Business for Europe and the UK at Lyzr AI, an enterprise agentic AI platform focused on helping consulting and professional services firms build and own the intelligence layer of their client workflows. He brings 25+ years of experience scaling data, AI, and commercial functions at firms including Valtech, AND Digital, Slalom, and Thoughtworks, organizations ranging from 11,000 to 12,000 people, giving him an unusually direct view of the commercial dynamics facing mid-market consulting firms from both inside and outside. He completed the Oxford Blockchain Strategy Programme at Saïd Business School. Connect with Amol on LinkedIn: linkedin.com/in/amol-punekar-3760671 (http://linkedin.com/in/amol-punekar-3760671)

About Lyzr AI:

Lyzr AI is an enterprise agentic AI platform that helps consulting firms and professional services organizations build, own, and deploy AI agent workflows for their enterprise clients. The platform is designed to enable consulting firms to move from implementation hours toward owning the operating layer of their clients' most critical workflows.

This article is based on an episode of the LEADERS IN CONSULTING Podcast, hosted by Sammy Gebele, Founder of SAWOO.

If you want to hear the full conversation behind this analysis with Amol, you can find the episode in the podcast section.

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