The consulting firms told us this would be different. They arrived with slick slide decks, expensive vendor packages, and promises to revolutionize how we do business. Their message was clear: Digital transformation was no longer optional; it was existential. Companies that failed to adapt would be left behind and crushed by more agile competitors with cloud-native architecture and data-driven strategies.

That was back in 2020, when Boston Consulting Group released research showing that 70% of digital transformations failed to meet their objectives. The consulting industry rushed to publish its frameworks, maturity models, and proprietary methodologies. Enterprise IT organizations spent millions on these programs, hoping to finally bridge the gap between technological capability and business value.

Several years on, I can say that little has changed. If anything, the issue has worsened.

The damage that was done

Most digital transformations didn’t just fail to deliver ROI; they actively damaged the organizations that undertook them. Technical debt accumulated as companies deployed monolithic cloud platforms that were neither modular nor scalable. Business misalignment became endemic as transformations focused on technology for its own sake rather than on solving actual business problems. Organizations found themselves locked into expensive vendor relationships that failed to deliver on their promises.

I’ve spoken with dozens of enterprise CIOs and CTOs over the past several years, and the stories are remarkably consistent. Transformations that were supposed to take six months stretched to two years. Budgets estimated at a few million exploded to six, eight, even twelve times the original projection. The consulting firms had excellent PowerPoint skills, but when it came to implementation, the results were consistently disappointing.

The damage wasn’t just financial. Organizations lost confidence in their IT capabilities. Talented people left because they were tired of being managed by consultants who couldn’t code their way out of a paper bag. Business stakeholders grew cynical about technology initiatives, having been burned too often by projects that promised transformation but delivered chaos.

Why the consulting models failed

Let’s be direct about what happened. The consulting industry discovered that digital transformation was highly profitable. They could sell their methodologies, vendor partnerships, and “best practices” frameworks, and charge premium fees for implementation support. The problem was that these frameworks were largely one-size-fits-all approaches disguised as bespoke solutions.

A major global bank doesn’t have the same requirements as a regional healthcare provider, yet both were sold the same transformation playbook. The consulting firms had invested too heavily in their methodologies to admit that the real answer might be to start with business requirements and work backward toward technology. Instead, they assembled prepackaged solutions optimized for vendor partnerships rather than clients’ actual needs.

The talent problem was equally severe. The consulting firms could certainly talk about digital transformation; they had excellent slides and compelling narratives. But when it came to implementation, the skill gaps were glaring. These organizations assembled solutions by following playbooks rather than understanding how the technology components actually fit together. The result was systems that were overengineered, overprovisioned, and ultimately underperforming.

Cloud computing was supposed to be the great enabler, but many organizations misunderstood and misapplied it. Organizations lifted and shifted their existing problems along with their existing architecture. Monolithic applications running on expensive virtual machines became the new normal, with organizations paying cloud costs that far exceeded their on-premises expenses, yet achieving none of the scalability benefits the cloud was supposed to deliver.

What we still need to learn

As we grapple with the next wave of technology transformation—this time centered on artificial intelligence—the lessons from digital transformation failures should be front and center. The first and most important lesson is brutally simple: Focus on business needs first. I know this sounds obvious, but I guarantee you that every failed digital transformation project lost sight of its core business requirements. They built technology for technology’s sake without understanding the problems they were trying to solve.

Too many transformations focused on individual business units rather than enterprisewide strategies. The result was a collection of systems that couldn’t work together cohesively. Isolated solutions felt like progress but actually made integration harder and created new dependencies that hadn’t existed before.

Organizations also need to invest in serious training for their people. I’m not talking about sending IT staff to vendor certification programs that teach them to pass tests on proprietary platforms. I mean fundamental architectural education that helps people understand how to assemble technology effectively.

We face a massive shortage of AI, cloud, and security architects, as well as other technical leaders who can design solutions that work. Rather than addressing this gap, organizations are placing unqualified people in these roles and hoping for the best. It’s past time to invest in education.

The case for simplicity

Here’s something that also should be obvious but clearly isn’t: You can solve the same business problem with $1 million or $20 million, depending on the sophistication of the IT organization building the solution. Both designs will work reasonably well, but one solution costs 20 times more because it is overbuilt and overengineered.

Organizations are adding AI to everything under the sun, even when simpler solutions would work just as well. They’re overprovisioning cloud resources because they lack the architectural discipline to rightsize their infrastructure. They’re building systems designed for scale they’ll never reach, complexity they don’t need, and features no one requested.

The path forward requires a minimalist approach. Build systems that are good enough for their intended purpose. Focus on requirements rather than capabilities. Resist the temptation to adopt every new technology just because it’s available.

Digital transformation didn’t fail because the technology was flawed. It failed because we forgot that technology exists to serve the business, not the other way around. To succeed with AI, we need to remember that fundamental truth. And remember the consulting industry’s tendency to sell us expensive solutions to problems we don’t have.

The model of the forward deployed engineer is sweeping through enterprise IT like a gold rush, and I’m concerned that many companies don’t understand what they’re signing up for.

Let’s start with the headline numbers. AWS announced a $1 billion investment in a new Forward Deployed Engineering organization. Google Cloud committed $750 million to expand similar programs. Microsoft has been running Azure-focused embedded engineering teams for years, including partnerships with Accenture to scale forward deployed engineering practices. All three are pitching the same story: We’ll send engineers to work directly with your teams, help you deploy AI, and accelerate your digital transformation. You get top-tier technical talent for free, and we get to partner with you on your journey.

It sounds reasonable on the surface. It sounds collaborative, even generous. But I’ve been in this industry long enough to know that when a multi-billion-dollar company offers you something for free, they’re sure to get much more than they give.

What you actually get

The forward deployed engineer model isn’t new. The consulting industry has been doing some version of it for decades. What makes this different is the scale and the direct financial incentive behind it.

These engineers work for the cloud provider. They’re not your employees. They’re not independent consultants. They’re technically excellent professionals who are being paid to solve your immediate problems while simultaneously building relationships and architectures that favor their employer’s ecosystem. Think about it from their perspective. Those forward engineers are evaluated on whether customers succeed with their employer’s platform. They’re rewarded when enterprises adopt more services from that platform. Their career advancement depends on making AWS, Google Cloud, or Microsoft Azure the obvious choice for all of your technical decisions.

This isn’t a criticism of the individual engineers. Many of them are genuinely talented and genuinely want to help. But they’re operating within a system that rewards specific outcomes, and those outcomes align with the vendor’s financial interests, not necessarily yours.

The problem no one talks about

Here’s what I see happening at enterprises right now. A company decides they need help deploying AI. A cloud provider offers to embed engineers at no additional cost. Those engineers work alongside internal teams, make architectural recommendations, and help build out systems. Six months later, the company has a production AI system running on a single cloud platform, built by people with deep expertise in that specific platform.

The problem? Nobody evaluated whether that platform was actually the best choice for the business. Nobody looked at alternatives. Nobody asked whether a multicloud architecture or best-of-breed approach might deliver better results at lower cost.

The engineers embedded in these programs are not going to recommend that you split your workloads across providers. They’re not going to suggest you use open source tools where they make sense. They’re not going to point you toward a competitor when their employer’s solution will work well enough. That’s not how these programs are designed to function. What you’re getting is optimized architecture for a single cloud brand, not optimized architecture for your business.

The financial reality will hit

The bills are going to come due, and they’re going to be painful. I’ve watched this pattern play out before. When enterprises lock into a single cloud provider through these embedded engineering programs, they often discover two or three years later that they’re paying premiums that their more independent-thinking competitors avoided.

The reasons are straightforward. When you’re architecting systems around a single platform, you naturally fall into usage patterns that favor that platform’s pricing structures. You use their managed databases instead of portable alternatives. You adopt their AI services instead of evaluating third-party options. You build workflows that only work within their ecosystem. And when it comes time to renegotiate or benchmark against alternatives, you find that migrating would cost more than accepting whatever pricing they offer.

I’ve spent the past decade helping companies untangle from these situations. I’ve seen organizations with cloud bills 15 to 20 times higher than they should be, unable to migrate because their entire AI infrastructure is built on proprietary services that only work on one platform. The forward deployed engineer programs are accelerating this problem. They’re making it easier to get into these situations and harder to get out.

Think before you commit

Before you accept one of these programs, consider these three recommendations.

First, require independent architecture oversight from day one. Hire or engage architects who work for your company, not for your cloud provider. They should evaluate every recommendation made by embedded engineers against business requirements and compare options across providers. This isn’t about being suspicious of the engineers. It’s about ensuring that decisions are made with your interests in mind.

Second, demand a clear exit strategy before you begin. Ask the cloud provider to document which proprietary services you’re using, what migration paths exist, and what the cost would be to move to an alternative platform. If they can’t provide that information, or if the migration costs seem impossibly high, that’s a sign that you’re building technical debt that will be very expensive to service later.

Third, benchmark your costs continuously. Set up internal processes to compare your cloud spending against industry benchmarks and against what your competitors might be paying for similar workloads. Don’t wait until your contract renewal to discover that you’re paying premium prices. Monitor expenses from the beginning, and be willing to challenge your cloud provider if you’re not getting value that justifies the cost.

The bottom line

The forward deployed engineers are solving real problems. Enterprises genuinely struggle with AI deployment, and having experienced engineers available to help is valuable. I’m not suggesting these programs are fundamentally bad. However, they’re being marketed as neutral partnerships when they’re actually strategic sales programs designed to lock enterprises into specific platforms. The helpful engineers showing up at your office are building dependencies that will be very difficult to break. The “free” technical assistance is being funded by margins on services you’ll be buying for years.

Go in with your eyes open. Use these programs but add your own independent oversight. Build architectures that you could leave if you needed to. And don’t let the immediate satisfaction of having problems solved today blind you to the financial consequences that will arrive tomorrow.