Produce or perish: no longer think service, think product (1/2)

Produce or perish: no longer think service, think product (1/2)

Analysis based on closed and invited sessions during the San Francisco Tech Week 2025including a private session featuring Aliisa Rosenthal (Head of Sales, OpenAI). All this is rounded off by a 2020-2025 review (HBR, MIT Sloan, Wharton,Berkeley) and our transatlantic transatlantic work from our pitches and IM.

‍Chroniqued'un virage que le marché commence à pricer

‍SanFrancisco, last week. In the crowded corridors of SF Tech Week, we saw a familiar yet new sight: founders talking "results", decks selling "interfaces" rather than "deliverables", executives from major groups murmuring that they were no longer buying "man-days" but "living solutions". And, at two trade shows where a16z brought together operators, investors, CEOs and CPOs, the same intuition was repeated: services are not dying out - they're changing shape.

As a transatlantic investment bank, we have one foot on the escalator of the European markets and the other on the launch pad of Silicon Valley. Our reading is simple: the productization of services is not a fad, it's a new economic grammar. It is transforming the way we sell, the way we operate... and the way we are valued.

‍Concretely,what does it change?

Imagine your service offering in a software cockpit. Suddenly, you can measure, test and iterate.
The cost of acquisition per offer(CAC) is no longer a blurred line but a controllable lever; customer lifetime value(LTV) becomes an asset that we grow through extension (modules, options, seats) rather than opportunistic relaunches.
Pricing ceases to be a scale to be determined: we experiment with tiering (good/better/best),usage-based, premium SLAs, A/B-testing of packs - and align pricing with perceived value rather than time spent.
On the revenue side, we're talking ARR/MRR, NRR and expansion; on the cost side, we're aiming for a target gross margin and driving down marginal cost through AI and processes.
As a result, management is moving away from project-based tinkering to the mechanics of scalability.
In this way, you get closer to the business model of the companies with the highest valuation multiples in the world: software... SaaS.

In practice, this changes everything for a service CEO: we're moving from a random economy (pipeline of missions, bumpy results) to an instrumented economy (cohorts, retention, CAC payback, time-to-value).
We see what creates adherence, we industrialize what repeats the value, we personalize only where the impact is.
And - a bonus of interest to the markets - this product language (legible recurrence, measurable retention, tangible IP) rewrites your valuation narrative.
Some have already begun this shift, without really realizing that they're on the road to productization.

Alvora Partners presents a framework to help you frame your approach and put words and concepts to these changes.

First, we return to productization to understand the shift from time × price to result × repeatability.

Secondly, we show howAI (supported by cloud/data/no-code) makes this changeover possible: automation of the bottom of the pyramid, codification of knowledge, product cockpit.

Finally, we detail what's at stake for an executive and how the markets will react, before addressing key objections, proposing a concrete playbook, and then setting out the timing and valuation implications.

From high-flying craftsmanship to assertive product

What is "productizing" a service? It means taking an ad hoc service and turning it into a defined, packaged and priced offer- a "package" that can be understood and repeated.

• Defined, because the value is formalized, the steps are standardized and often modular.
• Branded, because it carries a name, a promise, a proof of impact.
• Priced, because we know in advance what we're buying, how much it costs, and at what rate (often recurring).

In other words, we've gone from saving time × price to saving results × repeatability.
In the kitchen, it's the difference between an "off the menu, we'll see" dish and a mastered menu : tried-and-tested recipes, a tried-and-tested brigade, constant experience - with always, for strategic customers, a chef's table where you can add a tailor-made touch where it counts.

Why does this change appeal to the market? Because it removes uncertainty and adds clarity: shorter sales cycle (we understand), more stable satisfaction (we repeat), decreasing marginal cost (we industrialize).
And at the end of the chain, this is reflected in margins and multiples.

Why didn't it happen sooner? Let's talk AI

AI is invoked at every turn, at the cost of debates with no real added value for a manager.
Here, however, we must recognize that LLMs and other emerging technologies are accelerating productivity by offering a competitive technological layer.
Our approach to the role of AI can be summed up as follows: displace the human, not erase it.

Indeed, one might think that AI "replaces". In fact, it reorganizes.
The bottom of the pyramid (research, synthesis, formatting, repetitive checks) becomes an automated pipeline.
What your teams did "as best they could" becomes an asset: prompts, playbooks, co-pilots, datasets. Tacit knowledge is coded.

Immediate consequence: the expert goes back to where he creates the most value - framing, judging, arbitrating, relating.
And the service, relieved of some of its variability, finally lends itself to quality repetition.
This is the tipping point where recurrence becomes natural, and where product logics (trial, sandbox, lite) open up faster, cheaper distribution channels.

Around AI, the cloud, data platforms and no/low-code lower the cost of industrialization: a practice manager can create a mini-product (dashboard, workflow, business copilot ) without mobilizing an army of developers.
As a result, productization drops down to team level.
Suddenly, we're talking about micro-SaaS, "packaged" services, and subscription-based offerings in businesses where yesterday we used to bill by time spent.

What's at stake for an executive and why markets react

First issue: clarity.
A productized service tells its own story. It has a name, a promise, a measurable before-and-after.
This shortens the sale, facilitates onboarding and reduces perceived risk.
You're no longer buying "work", you're buying capacity.

The second issue is efficiency.
Standardization at the right level increases quality (less variance, more QA) and lowers the marginal cost of delivery.
This is precisely where P&L improves: more fixed costs (platform, data, product maintenance) amortized over a larger installed base, and fewer variable costs that rise with each mission.
One of the main driving forces behind the productization of services is the quest for scalability - in other words, the ability to decouple sales growth from linear headcount growth.

Third issue: differentiation.
Yes, standardization can be frightening ("commoditization"). But it's a useful fear: it forces us to create real moats - proprietary data, a living customer experience (a cockpit, not a PDF), an offer branding that counts.
We stop "doing everything for everyone" and become opinionated: this is what we do, this is how we do it, this is the value.

On the markets, all this is reflected in indicators that investors understand:

• recurring revenue share up ;
• NRR(net revenue retention ) on the rise;
• gross margin close to software standards ;
• and, in M&A, a credible IP narrative (platform, data, method).

Product + data + offering brand" assets mechanically drive multiples.
We're already seeing this in mergers: firms acquiring niche publishers, publishers internalizing service teams to guarantee results, and build-up strategies where several service players join forces on a single platform.
The common thread: turning work into assets.

And yet we are told:

"Our customers want tailor-made solutions. Very well: keep some where the impact is (10-20% of the offer), but make everything reproducible that doesn't create alpha. This is the foundation of quality.

"We're going to lose our human touch. No, if we formalize Human-in-the-Loop: at what moments the expert intervenes, why, with what added value. It's not a concession, it's a standard.

"The initial investment is heavy." Yes: documenting, tooling, naming, versioning - it's an intellectual capex. The return comes from recurrence andscale. Finance it as you would finance a product.

"What about price risk? It exists. We counter it with data (benchmarks, historical data, models), with a UX that makes value visible on a daily basis, and with a clear promise (SLA, time-to-value, indicators monitored with the customer).

In concrete terms, the playbooks we observe

1. Take stock of your IP. Methods, datasets, heuristics, templates.
   What can be reused must be identified and... reused.
2. Choose use case 10x. Recurring, painful, measurable. Give it a name.
   You don't buy hours; you buy "RiskWatch", "DealDesk AI" or "ClaimsPilot".
3. Produce. Scope, outputs, QA, SLA.
   Deliver an interface (cockpit, alerts, benchmarks), not a static deliverable.
4. Modularize. A standard core + a few differentiating options.
   Customization becomes a clear choice, not a project drift.
5. Put AI at the center. Extraction, generation, classification, internal co-pilots.
   Document the human moment (validation, arbitration, explanation).
6. Switch to recurring.
   Subscription or usage, clear pricing, PLG path (trial, lite, sandbox).
7. Product governance. Quarterly roadmap, versioning, metrics (NRR, gross margin, time-to-value).
   Pilot an offer, not a line of projects.

Simple test: if you can't pitch your offer like a product in 30 seconds - name, promise, proof - it's not ready.

Why timing matters

At SF Tech Week, we saw a discreet consensus: customers now buy continuous results rather than "one-off missions".
Those who get in early lay the groundwork: they lock in the relationship through data (what we measure),interface (what we see) and frequency (what we experience).
While others negotiate TJM, they build installed bases.

For an investment committee, this changes everything: recurring mix trajectory, net revenue retention that tells a story, converging margins, legible M&A (asset acquisition, product carve-out, AI build-up on a vertical).
The window is open to offer equity stories 2.0 based on what counts: repeatability and assets.

By way of compass

Productization is not the enemy of service; it is the modern form of service.
It makes the intelligence of a profession, the interface, measurement and repetition tangible.
It allows us to create more value... and, above all, to capture it more effectively.

Our conviction: the next decade of the service sector will be more like software: named offers, continuous experiences, visible revenues.

Leaders who embrace this paradigm early on will no longer be selling time: they will own assets and be able to decorrelate sales growth from headcount growth, but also decorrelate the value of their assets from their own value as leaders.

In our next series, we'll be presenting real-life cases of productization in progress - theory over.

‍

TheAlvora Partners team

Ship the deal, not the deck

NextGen merchant banks

‍