I worked for and with many companies and organisations.They are all successful and they are all struggling. Each successful in its own way. And each struggling in its own way. Although, that is how they like to think about themselves: we are unique. Well, yes, you are unique, just like everybody else. And just like everybody else, you are not as unique as you think you are. I started to notice patterns, especially in the struggling. This essay is about those patterns.
In this post I distill common patterns of companies stumbling towards success into a maturity model of the consultancy versus Saas struggle. How to deal with the tension between project and product. Or, implied in “maturity”, how to get from project to product.
I start with a section on definitions, to avoid confusion. Then I will give a short description of each of the 5 maturity stages:
- Consultancy
- Automated consultancy
- Hybrid
- Big Head SaaS
- Long Tail SaaS
Then, we’ll have a look at how this model is especially relevant for service providers in the environmental supply chain risk sector. We wrap it up with a short reflection, a use case and a future outlook.
Definitions
Consultancy: organisations that sell the value of their time on a project basis
SaaS: organisations that sell the value of a service or product on a subscription basis
Maturity: a description, not a judgement. A higher maturity stage is not inherently better. The goal is to know which stage you are in, and to make a conscious choice about whether to move.
Consultancy builds trust, SaaS builds leverage The tension between them is not a problem to solve. It is the business.
Maturity Stages
1. Consultancy
Every delivery is custom. Every project starts from scratch. Your value is in the people who show up and do the work. You get paid for time and expertise, and you deliver something the client calls theirs, even though it sits on your laptop.
Trust is built person by person, project by project.This is fun.
The problem is, it doesn’t scale. Every new client requires the same investment. Revenue grows linearly with headcount. Your most valuable knowledge lives in the heads of your best people, and when they leave, it walks out the door with them.
Typical signals: long sales cycles, high margins per project, hard to forecast revenue, dependence on senior people, the same problem solved for different clients every six months. No incentive to extract and distill common patterns into revenue engines.
2. Automated Consultancy
You spent some time solving the same problem for different clients. You know what the answer looks like before you start. So you build tools, scripts, templates, models, internal platforms that do the repeatable parts faster. The last mile is still custom, still human, still consultancy. But the middle is increasingly automated.
This stage has two shapes, which look similar from the outside but lead in very different directions.
2a. Vertically scaling automation (projects)
The automation makes your projects faster and cheaper to deliver. You can do more projects with the same team, or the same projects with a smaller team. Revenue still comes from projects, but your margin per project improves. You are selling time more efficiently.
Side note: this is where most AI efforts go in most companies, doing the same thing but faster or more (Jevons paradox).
2b. Horizontally scaling automation (subscriptions)
The automation becomes something you can deliver repeatedly to multiple clients simultaneously. Instead of one project at a time, you can run the same process for ten clients at once. The business model starts to shift: recurring revenue, subscriptions, ARR, MRR, churn. You are no longer selling time — you are selling access to a system.
The fork between 2a and 2b is one of the most consequential decisions a consultancy makes, usually without realising it is making it. 2a makes you a more efficient consultancy. 2b is the beginning of a product company.
Side note: “scaling” is often interpreted as “technical scaling”: can you process the world? Can you handle 1 million HTTP requests? “Scaling” here means scaling across the organisation: HR, legal, marketing, front office, back office, MT. Often, only 1 or 2 business functions are scaled and the others are left as an afterthought.
Typical signals: outcomes from internal tools that clients keep asking for repeatedly; the same analyst running five client accounts simultaneously; your 25 year old consulting colleague who did a Python course in university asking for a company CodeRabbit subscription.
3. Hybrid
You have a product, or something that looks like one. But it does not run without your people behind it. Configuration is done by your front office delivery team (operations or account management or forward deployed engineers). Exceptions are handled on the fly by your backoffice teams. The client sees a clean interface; your team sees a rat’s nest of bespoke parameters, manual overrides and client-specific edge cases held together with goodwill,institutional memory, rubber bands and duck tape.
This is the stage where companies spend the longest time, and the stage they most often mistake for something else. You will hear: “we are basically SaaS.” You are not. You are a consultancy with a good interface.
That is not a failure. The hybrid model can be highly profitable, especially in (new) markets where trust and interpretability matter more than scalability. Many companies should stay here. The mistake is not being in the hybrid stage; the mistake is not knowing you are in it, and building a sales motion or an org structure optimised for a stage you have not reached yet.
The transition out of hybrid requires hard decisions: what gets built into the product, and what stays consultancy? The configuration that your ops team does manually, can it become self-service? If not, stop doing it. The exceptions your engineers handle, are they bugs or features? If it’s a feature, your PM should own it and decide if it goes on the backlog or not. The institutional memory, can it be documented, automated, encoded into the system? If not, it doesn’t exist.
What is hard is that is the point where decision making has to be delegated to the organisation. The role of the top management changes from making decisions to leading by creating principles, guidelines and a strategy that allows the organisation to make decisions.
Typical signals: clients think they are using software; your team knows they are running a service; onboarding takes weeks; the product manager and the delivery manager are fighting about the same backlog; front office teams (support, FDE, account management, customer success) are ingesting client feedback and dumping it in the organisation; engineering teams get overloaded with each additional client.
4. Big Head SaaS
The product is real. Clients can configure it themselves. Your ops team no longer runs the system for each client; they run the system once, clients use it. A lot of your margin is still eaten by front office teams: onboarding still requires effort, relationship, trust-building, this is the “big head” of the distribution: high-value clients who need hand-holding, onboarding support, account management, bespoke reporting. But the core delivery is automated.
Pricing is value-based. You charge for the outcome the product enables, not for the hours it took to deliver or operate. Revenue scales with clients, not with headcount but is not yet independent of it. Adding a new market or a new vertical requires meaningful investment, but it creates a multiplier rather than a linear addition.
The growth model here is typically a mix of referral and direct sales. Your clients talk to prospects. Your sales team closes deals that your account managers retain. The pipeline is manageable and foreseeable for the first time.
The risk at this stage is product debt: the pressure to close deals leads to promises, workarounds and special configurations that slowly pull the product back toward hybrid. Every client-specific customisation is a step backward. Discipline here is one of the hardest organisational skills to maintain.
Typical signals: ARR becomes forecastable; sales cycles are getting stable; the same demo closes most deals; your biggest clients are also where you spend most front office costs; engine of growth is a mix between paid and referral.
Side note: “Adding a new market or a new vertical requires meaningful investment, but it creates a multiplier rather than a linear addition.” is true as long as you realise that this new vertical is probably less mature than its parent, a step backward.
5. Long Tail SaaS
The product sells itself, or nearly. A new client can sign up, configure and get value without speaking to anyone at your company. The long tail of smaller clients can be served at near-zero marginal cost. Growth is driven by the product itself: through virality, through content, through a freemium model, through integrations that put the product in front of the right people at the right moment.
Pricing is cost-based or usage-based. The engine of growth is paid, viral, or a mix. Margins are lower per client than in Big Tail SaaS, but there are a lot of clients. Scaling within a market is exponential, not linear.
This is the stage most startup pitches describe. It is also the stage that almost no B2B company in a new, trust-dependent market reaches quickly. The road to long tail SaaS (especially for new products in new markets) runs through every preceding stage. Trust is built by humans before it is maintained by software. The product that eventually scales to ten thousand clients was first tested on ten clients who needed a lot of handholding.
The companies that try to skip to this stage typically end up with a very polished, hollow, interface for a problem they have not fully understood yet. The consultancy stage is not a failure state to escape. It is the R&D process, it is product discovery.
Typical signals: self-serve sign-up; low or zero marginal cost per new client; product-led growth metrics; the support team handles volume, not complexity; engine of growth is a mix between paid and viral.
This is what going wrong looks like
A common cause of unaware struggling: a company built successful service X up to Big Head SaaS level, introduces service Y for upselling and x-selling without realising that service is not at SaaS level yet.Sales sells service Y like service X. The company can’t deliver Y like X and has to rely on R&D and sales engineers to fill in the non-automated gaps. Sales and support get frustrated because they are flooded with client feedback about overpromised service Y. Management fixes the gaps with people (hiring more customer support engineers, offloading operational tasks to R&D and product development), leading to higher marginal costs, slower R&D and product development, increased churn, higher pressure, burnouts, misery, doom, apocalypse. Well, ok, you get the gist.
The key is that probably everybody is right, but right on a different level. “We are SaaS”. Yes, service X is SaaS, but that doesn’t mean your whole company is SaaS. Every new product or service you introduce starts as consultancy and needs to go through the maturity stages. Winning companies understand this and set up an organisation structure and development flow that accelerates this.
Environmental Supply Chain Risk Sector
Sure, but why is this relevant? What does it matter? Who cares? Jeff started Amazon as a Long Tail SaaS from the start and look at him now! That is correct. This is not a recipe or model for every business in every sector. But if you are building a new product in a new market, this is what you go through. Because the most important thing, the quod sine qua non, the thing you are doing that will make or break your product is: trust. The market won’t buy your product if it doesn’t trust it. So what you have to do is build trust. You might call it sales, but it’s not sales in the classic term, it’s relationship building, building trust and product discovery at the same time. Rename it to consultative selling, sign the deal after 18 months lead time, have another front office team take over part of the trust building (Account Management, Sales Engineering, Customer Success, Delivery, R&D), it’s still not sales, it’s consulting. You are building trust and discovering what your product actually looks like. And that is good. But that’s not the goal.
This is especially true in the environmental supply chain risk sector. A new market, without standards, benchmarks or decades of best practices to refer to. In this new market there are ton of tiny, niche service providers each with their own new product. Some striving to be a SaaS company, some happy with some hybrid form, some … .
And that’s why models like this can be helpful:
1. The market is too young for shortcuts. There are no established benchmarks, no dominant platforms, no decade of best practices to copy. Every company is figuring out what the product actually is while simultaneously trying to sell it. The maturity model gives you a map for that process. A way to talk about it internally without it feeling like an admission of failure.
2. Trust takes longer here than almost anywhere else. Your buyers are sustainability managers, procurement leads and financial controllers making decisions that will end up in annual reports and regulatory filings. They will not buy a self-serve product from a company they do not know. The consulting stage is not a phase to escape, it is the mechanism by which trust gets built. Understanding that makes it a strategic asset rather than an embarrassing legacy.
3. The consolidation is coming. The ESG data and supply chain risk space is full of niche providers, each covering one piece of the puzzle. At some point, through acquisition, partnership or attrition, the market will consolidate. Knowing where your service sits on the maturity curve is directly relevant to how you will be valued, how you will be integrated and whether you are the acquirer or the acquired.
You cannot escape building trust and discovering what your product actually is. You do that through consulting. In early stages, it looks like pure consulting, later via other business functions as well. The trick is:
- To be aware of this process
- To make sure you don’t get stuck in consulting and promote your service or product to a higher maturity level when it’s ready
When to move: transition triggers
No company wakes up one morning and decides to advance a maturity stage. The transitions are not planned. They are recognised, usually after the current stage has already started causing pain.
1 → 2 Consultancy to Automated Consultancy
You are ready when…: You have solved the same problem for the third client; Your best analyst spends more than 30% of their time on tasks a script could do; Clients are asking why delivery takes so long.
The risk of waiting: Your best people leave because the work stopped being interesting. Margins erode as headcount scales with revenue.
2a → 2b Vertical to Horizontal automation
You are ready when…: Clients keep asking for the same outcomes of your internal tooling; You are running the same process for more than five clients; A new client does not require a fundamentally different setup.
The risk of waiting: You keep optimising delivery efficiency instead of building leverage. The business stays linear.
2 → 3 Automated Consultancy to Hybrid
You are ready when…: You have a thing that looks like a product. Clients refer to it by name. Sales is starting to demo it. But your ops team is still running it manually for every client.
The risk of waiting: Sales overpromises what the product can do. Delivery scrambles to catch up. The gap between what is sold and what is delivered becomes a management problem.
3 → 4 Hybrid to Big Head SaaS
You are ready when…: Clients can configure the core use case without your team’s involvement. Onboarding still requires effort, but delivery does not. Your ops team is managing exceptions, not running the system.
The risk of waiting: Client-specific workarounds accumulate. The product slowly becomes hybrid again. Technical debt disguised as customer success.
4 → 5 Big Head to Long Tail SaaS
You are ready when…: Small clients are signing up without talking to anyone on your team. Support tickets are about volume, not complexity. A new market
segment could be reached without a new sales team.
The risk of waiting: You over-invest in enterprise clients at the expense of the engine that would scale the business. The long tail never develops.
Wrapup
In the intro I wrote about struggling companies. In reality, many companies are struggling but are not aware of their struggling.This maturity model can help companies understand if and why they are struggling.
But first, please note that this is a model, not reality. No company will be in exactly one of the maturity stages. Most companies are somewhere on a gradient from consultancy to SaaS. And companies might have several services, each in their own maturity stage.
There can also be a difference in intention or ambition. Some companies are laser focused at becoming a Long Tail SaaS. The classic Silicon Valley startup archetype. They know what they want. I call these intentional SaaS companies. Other companies happen to find themselves at one of the stages at some point in their existence. Most companies are of that type. In most cases it’s unplanned. Reasons can be plenty: new enabling or accelerating technologies, a new hire, changing market demand, leadership change. I call these serendipitous SaaS companies.
The Consultancy to SaaS maturity model can help both intentional and serendipitous companies:
- assess the current maturity stage of your services and products;
- explore implications for business functions like GTM, engineering or organisation roles;
- understand if it makes sense to go to the next stage and how to get there;
- Understand if and how finance can accelerate service & product development.
The next essay in this series explores implications on business functions like Go To Market (GTM).
Life is great.