Productizing AI services - why most consulting firms fail

Every AI consulting firm I talk to has the same dream. Turn those custom implementations into a product. Build it once, sell it many times. Stop trading time for money.

Most fail.

Research shows less than 10% of AI proof-of-concepts actually turn into real products. When you look at broader professional services productization efforts, failure rates hit 40-70% even in the best conditions.

The problem is not lack of technical skill. It’s approaching productizing AI services from the wrong direction entirely.

The backwards approach that kills productization

Here’s what I keep seeing. Consulting firm builds custom AI solutions for clients. Each project is different, tailored to specific needs. After doing this for a while, someone says “we should productize this.”

So they look at their methodology. The process they follow. The frameworks they use. And they try to turn that into software.

This fails for a simple reason: clients do not buy your process. They buy solutions to their problems.

When McKinsey launched McKinsey Solutions in 2007, they did not productize their consulting methodology. They built specific tools for specific recurring problems. Periscope for marketing analytics. Wave for change management. Tools that solved the problems clients kept hiring them to solve.

The difference matters.

Your consulting process is how you work. Pattern recognition in client problems is what creates product opportunities. Professional services firms struggle because they confuse these two things.

Finding the 80/20 in your client work

The firms that succeed at productizing AI services do something different. They analyze their client engagements looking for patterns in the problems, not patterns in their solutions.

This is where the 80/20 principle becomes critical. McKinsey, BCG, and Bain have used this for decades. Look at your last 20 client projects. What are the recurring problems? Not the recurring tasks in your methodology, but the actual business problems clients keep hiring you to solve.

You’ll usually find something interesting. A small number of core problems account for most of your engagements. Maybe 3-4 fundamental challenges that show up in different forms across different industries.

That’s your product opportunity.

Real example from the research: A consultant noticed 90% of prospects wanted help with the same core challenges in their sales funnels. Not 90% wanted the same consulting process. 90% had the same underlying problem. He built a productized audit specifically for that problem.

The approach works because you are solving a repeated problem, not trying to sell a repeated process.

Why hybrid models beat pure product transitions

Here’s something that surprised me when researching this. The successful AI companies that started as services did not fully transition to products. They built hybrid models.

Look at Palantir’s business model. They sell software subscriptions, but professional services remain a significant revenue stream. Same with DataRobot. They offer both subscriptions and professional services for implementation and optimization.

Why does this work?

Because AI implementation requires significant professional services to succeed. Companies cannot just buy your product and figure it out themselves. The complexity is too high. The integration challenges are too real.

Research on product-service hybrids shows this is becoming standard in complex technology. You need both. The product provides scalability. The services ensure successful implementation.

Most consulting firms think they need to choose: pure services or pure product. The companies winning at productizing AI services rejected that false choice. They built both, intentionally.

The operational and financial realities

Productizing AI services requires changing how your entire company works. Not just what you sell.

Your sales process changes. Selling consulting means custom proposals, lengthy sales cycles, relationship-driven deals. Selling products means standardized pricing, shorter cycles, demand generation at scale.

Your team structure changes. Consultants optimize for customization and client-specific expertise. Product teams optimize for repeatability and systematic improvement.

Your support model changes. Consulting means dedicated teams per engagement. Products mean scalable support systems that handle many customers simultaneously.

These operational shifts are why firms massively underestimate how difficult it is to add a product business model alongside a services business model. The skills, processes, and mindsets are different.

Companies that succeed treat this as a multi-year transformation, not a product launch. They build separate teams for the product side. They invest in different infrastructure. They accept that productizing AI services means becoming a different type of company.

The economics do not work the way most people think, either.

For SaaS products, customer acquisition costs must generate returns of 2-3x minimum. The median CAC payback period runs about 12 months for early-stage companies, stretching to 20 months for larger firms.

But here is the challenge. Your first product customers will cost more to acquire than your consulting clients did. Why? You’re entering a new market. Building a new sales motion. Learning what messages work.

The timeline is longer than you want. Professional services productization typically takes 12-24 months just to get a viable product to market. Then you need another 12-18 months to validate product-market fit and iterate based on real usage.

Expect three years minimum from decision to meaningful product revenue. Some firms do it faster. Most take longer.

Budget for this. Productizing AI services while maintaining your consulting business means you are funding product development from services revenue. That puts pressure on margins. Many firms underestimate this and run out of resources before the product gains traction.

When to walk away from productization

Not every consulting firm should productize. Sometimes the honest answer is: keep doing services.

Walk away if you cannot identify clear patterns in client problems. If every engagement is truly unique, you do not have a productization opportunity. You have a consulting business.

Walk away if you are unwilling to make the operational changes. A half-hearted productization effort that tries to keep the consulting model while adding a product typically fails. It satisfies neither market.

Walk away if the market for your product is too small. Consulting can work with niche markets. Products need scale to justify the investment.

The opportunity exists when you see the same core problems repeatedly, when you can standardize solutions without losing effectiveness, and when the market is large enough to support a product business.

For firms in that position, productizing AI services is not just about building software. It’s about identifying the patterns in what clients actually need, then building repeatable solutions around those patterns rather than around your process.

That distinction makes all the difference between productization efforts that scale and those that fail.