Prolego's principles

Hiring Prolego means hiring a point of view as well as a team. These principles shape what we recommend, what we build and how we operate it. We hold strong opinions and change them when the work teaches us something better.

1. We are betting that AI will keep improving exponentially

We expect AI to continue improving at an exponentially fast rate. Every investment decision should be made through that lens.

If you share that view, the only rational response is to take an honest look at your business. What belongs in its future, and what belongs in its past? Which services, ways of working and sources of competitive advantage still make sense when intelligence is abundant?

The question is: what does your company look like in the era of AI Abundance? Imagine AI at the core of every economically consequential decision. Consider what that changes about the customers you can serve, the services you can offer, how you deliver them and how the business makes money.

That assessment should change where you invest, what you build, what you buy and what you stop doing. Some capabilities deserve investment now because they prepare the business for that future. Other activities may be familiar and profitable today but have little place in the business you need to become.

We want to work with leaders who are excited by that possibility and willing to act on it. That means rethinking the business, challenging established practices and letting go of investments or assumptions that no longer serve its future. Making today's operation a little more efficient is too small an ambition.

You have to want to transform your business. We bring the experience, judgment and engineering to help you pursue that transformation, starting with useful work and expanding from there.

2. Start with a systems view, not a solutions view

Think big, start small and keep moving toward AI Abundance.

Prepare for a future in which AI is part of every economically consequential decision. We expect your customers, suppliers, partners and competitors to move in that direction too. Their changing capabilities and expectations will reshape the system your business operates within.

You need to consider that entire system: how your company creates value, how it works with others, how those relationships will evolve and where you want the business to go. Deploying a basic agentic workflow or plugging in a new product is easy. A collection of point solutions will not, by itself, rewrite the economics of your business.

For example, the ambition is not a better lead-scoring system. It is rethinking how your company sells and delivers its solutions from the ground up: how customers find you, how you understand their needs, what you offer, how you price and deliver it, and how each customer experience improves the next. Lead scoring may be one useful piece of that much larger change.

Start with a clear picture of the business you want to become, then choose a small first investment that creates tangible value and moves you toward it. Build the shared knowledge, connections and operating capability through that work. Use what you learn to choose the next step, adapting as AI and the surrounding market advance.

The first application is a beginning. The destination is a different business, capable of applying intelligence throughout its decisions and workflows. Business reinvention explains how we connect that ambition to delivery.

3. Reliability is the central challenge

Reliability isn't part of the work; it is the work.

Building software is getting easier. Agreeing on what the AI should do, and engineering it to do that consistently, is the central task. Executive sponsorship is necessary and often straightforward. The harder agreement is among experienced managers and staff who run the business. Their confidence determines whether the system gets used.

An employee question such as "fraud?" required clarification before retrieval could help. A later debt-consolidation workflow exposed conflicting calculation methods. Three or four stakeholders had to settle the method; more engineers could not decide for them.

Performance-Driven Development starts with their examples and standards. The client evaluates the results, not Prolego.

4. Velocity is a principle. Work like a startup

The biggest risk is not changing fast enough.

In our view, the overwhelming majority of companies underestimate how fast AI is advancing and how capable it will become. They organize decisions around preventing problems with AI. In doing so, they take the bigger risk: protecting today's way of working while the world moves past it.

The ability to adapt as AI advances will be necessary for survival. Customers will change their expectations. Competitors will find new ways to serve them. A company that needs months to approve each step can fall behind even when every individual decision appears prudent.

We're startup people ourselves. We bring that mindset to our clients: make decisions, put useful work into practice, learn quickly and change course when the evidence calls for it. We want to help the organization develop that velocity throughout its work with AI.

Reliability engineering makes that pace possible. Agree on the behavior, evaluate changes and use human review for critical decisions so the team can release and learn in short cycles. Business decisions and deployment practices need to move with the engineering.

Velocity is an organizational capability. The goal is a business that can keep adapting as the technology, its customers and its opportunities change.

5. Smaller teams will deliver greater value

As AI improves, smaller and smaller teams can create bigger and bigger value.

We expect the efficiency and output of teams that use AI well to keep growing at an exponentially fast rate. Today's productivity is a starting point. What a small team can build, operate and improve should keep expanding as the technology advances.

That changes the economics of services. We use better AI to increase what the team can accomplish, automate more of its work and support more applications. Shared knowledge and infrastructure compound that advantage: each useful addition makes subsequent work faster and more valuable.

Our model is a small expert team whose capacity keeps growing. The investment is in its judgment and ability to use improving AI to deliver more business value over time. That is the direction we are building toward for Prolego and our clients.

6. Building is becoming more attractive than buying

AI is rewriting the economics that made buying enterprise software the default.

For decades, software was expensive and difficult to build. It made economic sense for one vendor to build a solution and sell it repeatedly to many customers. The vendor spread the development cost across those customers, and companies bought standardized products instead of funding their own systems. Businesses adapted their work to the software they could buy.

That advantage is eroding as AI makes software cheaper, faster and easier to build. At the same time, more of the value comes from applying AI to your company's own data, judgment and workflows. The opportunity is to build around how your business creates value and keep adapting the system as the business and AI change.

A standard product is designed for many companies. Your customer relationships, operating knowledge and decisions are specific to yours. Those are increasingly the assets that make an AI system valuable. Building lets you put them at the center and develop a capability you own.

We expect this shift to continue as AI improves. Evaluate investment choices against where the economics are going, rather than assume that buying a packaged solution remains the natural starting point. Our approach is to build the company's own Operations Intelligence, with the freedom to improve it, change models and expand into new work.

The client owns what we create, apart from open-source libraries. Working with Prolego explains ownership and the option to keep us operating the systems or take them over yourself.

7. AI needs a new playbook

There is no finish line in AI. There is no done.

AI will keep evolving, and your business needs to keep evolving with it, at a faster and faster rate. A deployment is a milestone. The enduring capability is being able to operate, improve and expand what the business does as the technology advances.

The old software playbook was built around defining a solution, building or buying it, deploying it and maintaining it. Software was expensive to build, so much of the discipline focused on avoiding the wrong investment before construction began.

AI changes that logic. Coding tools make software faster to build, and LLMs can supply reasoning that once required extensive business logic. The central work shifts toward agreeing on what the AI should do, making it reliable and adapting it as the business and models change.

The new playbook is continuous: build, operate, measure, adapt and expand. Expert feedback and evaluations guide improvements. Better models create opportunities to simplify earlier work or pursue something newly practical. What the team learns in production informs the next business decision.

Your investment should leave the organization better able to repeat that cycle, with growing knowledge, reusable capabilities and a faster decision rhythm. That is how a first application becomes a path toward AI Abundance.

Why AI needs a new playbook explains the shift. Performance-Driven Development is how we put it into practice.