Building an Engineering AI Strategy (That Actually Works)

MIT NANDA released a shocking finding: 95% of enterprise AI deployments fail. Despite this failure rate, there’s still an existential fear in the market that if companies don’t adopt AI (and quickly), then they’ll risk missing company performance targets or go out of business entirely. 

In a recent survey of 250 engineering leaders, those sentiments were split roughly 50/50. 95% of engineering leaders believe that if their team does not fully adopt AI in the next 12-24 months they’ll either: miss company performance targets (48%) or get put out of business by competitors (47%). This isn’t a matter of if your engineering organization should deploy a full AI strategy, but when. And that when should be very soon.

The problem is this gap. Engineering leaders (along with every other enterprise business leader) know AI is a necessity. Yet, they also have no blueprint for a successful AI deployment. Because nearly every one fails.

But not every deployment fails.

What can engineering leaders – desperate for their teams to adopt AI – learn from the 95% who fail and the 5% who succeed in deploying AI?

Why most enterprise AI efforts fail^{Why 95% fail}

Despite heavy investment, most organizations hit the same blockers. Here are the four most prominent reasons enterprise AI deployments fail:

1. They invest in static, prompt-only tools. 

The primary blocker isn’t models, data, or legal. It’s that most systems don’t learn and adapt from use. Tools, like out-of-the-box LLMs or chatbots require re‑prompting every time. And so they fail to accumulate context. Then, when it comes to a larger deployment, the initiative never makes it out of a pilot.

This lack of information retention forces SMEs to re-teach the system each task, increasing hidden labor and eroding trust. When most deployments behave like popular LLMs (think ChatGPT and Copilot) it’s easy to see why only ~5% of task‑specific tools reach production.

2. Building instead of buying workflow‑integrated systems.

The MIT study emphasizes that external partnerships reach deployment ~2x as often as internal builds (~67% vs. ~33%). Why does this happen? Vendors focused on a narrow workflow arrive with pre‑learned patterns and integrations, reducing configuration burden and “time to value.” 

Internal teams tend to over‑generalize and stall in customization debt. Not to mention, they often lack workflow specific skillsets in the build-stage dooming the project form the start.

Interestingly, enterprises lead in pilot volume but lag in scale‑up. A sign that in‑house pilots also overfit to demos and underperform in messy operations.

3. Relying on "central labs" instead of empowering end users/managers.

Successful deployments decentralize implementation to managers and power users, then hold leaders accountable for outcomes. "Central labs" slow down discovery and miss workflow nuance due to not being close enough to the pain. 

Frontline “prosumers” already use AI personally, proving fit and creating credible bottoms‑up demand that executive programs can formalize. So, organizations should source problems from budget holders on the floor, and treat deployment as co‑evolution with vendors rather than a centrally planned rollout.

4. Chasing easily quantifiable ROI (sales/marketing) vs. high transformational ROI (engineering/operations).

Roughly 50–70% of AI budgets flow to Sales & Marketing. Because metrics in these departments are visible and easily attributable, not because ROI is highest. 

The MIT NANDA study documents faster payback in back‑office and operations functions, like BPO reduction and agency spend cuts. This is even without headcount changes. This offers some evidence that engineering and operations can drive clearer cash savings.

When we look at these together, the underlying failures are clear. The AI deployments that fail tend to take the easy way over the hard way. Next, we’ll explore what successful AI deployments do differently.

The blueprint for a successful AI deployment ^{How 5% succeed}

The 5% do something different. Let’s define what exactly that is.

1. Start with high-value, low-disruption workflows: Crawl, walk, run approach

Winning teams land small, visible wins in narrow workflows (voice routing, drawing checks, document reviews) before touching core processes. This avoids the “pilot graveyard.” Tools with low setup burden earn trust fast, while heavy internal builds stall. Mid-market leaders averaged ~90 days to rollout vs. ~9 months for enterprises.

Start where success is easiest and most visible as defined by core business end users (i.e. You are an engineering company that manufactures physical products. Start with engineering). Prove quick wins, then expand deliberately.

2. Invest in AI that learns and adapts

Executives prioritize learning from feedback (66%) and retaining context (63%) over benchmarks. Treat your data “memory” as infrastructure. Meaning preferences, standards, and edit history should compound into a knowledge model, not vanish between sessions. Systems that learn reduce manual re‑entry, error rates, and change‑management friction.

Static tools fail because they repeat mistakes. Winners demand systems that get smarter with every cycle.

3. Build external partnerships and workflow integrations

Partnering with vendors already embedded in workflows doubles success. Hold them accountable to operational KPIs (cycle time, rework, warranty claims), not model scores. Known vendors and referrals carry more weight than demos. Enterprises are locking in learning‑capable systems now—creating switching costs for laggards.

Buy, don’t build. Success rates double with workflow‑ready partners. Move early to capture compounding advantages, like roadmap prioritization and strategic partnerships.

4. Benchmark on operational outcomes, not model scores

Buyers care less about model benchmarks and more about rework, warranty claims, or time‑to‑market improvements. Vendors tying success to ops KPIs scale more successfully.

Keep the focus on cycle‑time, quality, and cost outcomes that matter to leadership.

5. Partner through early‑stage failures (Co‑evolution)

Enterprises that treated adoption as iterative co‑evolution, not one‑time rollout, were more successful. Accepting bumps but iterating together built durable advantage.

Expect early bumps, but commit to iterate with your vendor—this is how durable advantage is built.

6. Source AI initiatives from frontline managers

Bottom‑up sourcing plus executive sponsorship correlated with higher adoption. Central‑lab‑only programs missed context and stalled.

Let those closest to the work source problems and solutions. Central labs can sponsor, but managers must drive adoption.

In the “Why most enterprise AI deployments fail” section, we summarized with: taking the easy way when it comes to AI is a recipe for failure. Each of the steps outlined in this success section detail what it means to do things the hard way. It’s hard to go out and find an AI partner who knows what they’re doing and agrees to a mutual partnership. It’s hard to research the workflows and processes troubling your front-line managers. It’s hard to invest in a crawl, walk, run approach. 

But this is what the companies who deploy AI successfully do. They take the hard way backed by an AI strategy tied to the business vision. It’s not just a guide, it’s the only way to succeed.

How engineering leaders can build an AI strategy that actually works^{Applying it to Engineering}

Maybe you read through the success list and you already have some ideas. Maybe you still have no clue where to start. We got you. Our team talks with engineering leaders every day. Here’s how you can apply the principles of a successful enterprise AI deployment to engineering. 

Start with high-value, low-disruption workflows

For mechanical engineering teams, these are workflows where AI can remove bottlenecks without touching core design authority

• Drawing checks and standards compliance → Automating drawing checks for tasks like: GD&T validation, revision numbering, or formatting against internal standards is a great place to start.
• Design reviews and documentation → Automatically generating BOM comparisons or flagging drawing inconsistencies before release.
• Supplier communication → Drafting RFQs or summarizing supplier feedback, cutting down manual email loops.

These workflows are low-lift because they sit adjacent to design, not inside the CAD software itself. Yet applying AI will save hours of engineering time, reduce rework and motivate engineers to overcome adoption hurdles.

Invest in AI that learns and adapts

In mechanical engineering, static copilots fail because they forget context between sessions. What works instead:

• Systems that retain engineering standards → Once a company teaches the system its tolerance stack-up rules or preferred fastener libraries, that knowledge gets applied forever.
• Memory of decisions across reviews → AI that recalls how similar design changes were handled in the past reduces redundant discussions.
• Feedback-driven improvement → Tools that learn from redlines or review outcome compound into a knowledge model instead of vanishing into a chat log.

Here are some AI tools that already promise much of these capabilities today.

Benchmark operational outcomes, not model scores

The most persuasive part of any AI strategy is when you can tie it to metrics that matter. These aren’t vanity benchmarks (like “accuracy %” of a model) but operational KPIs that executives already track and AI can realistically move. These are examples of KPIs engineering leaders can start monitoring today and improve as AI initiatives progress. 

• Design review cycle time
• Supplier RFQ turnaround time
• ECO processing time
• Rework rate (due to documentation errors)
• Warranty claim reduction or Field failures linked to design
• Engineer hours, pre-design release
• Engineering throughput
• Product development cycle time (concept → release)

Buy, don’t build with trusted vendors and long-term partnerships

This is easier said than done. There’s a lot of noise in the AI space right now, especially for hardware engineering which is notoriously nuanced. However, there are signs of AI tool vendors that prove a potential for long-term partnership.

1. They show, don’t just tell. The right vendor should show the product working in many use cases and in many ways. Ideally, they would do this publicly. 
2. They’re solving a real problem for complex engineering. We saw a lot of AI tools fail when context and nuance hit the product. CAD generation is a great example. Lots of tools claiming to do text-to-CAD in seconds. But when asked to handle multi-component assemblies, the model broke. And vendors had no answer. The right vendor should already show they understand the complexities of large-scale assemblies and nuanced engineering.
3. They already have customers like you. The right vendor can tell you who they’re working with today to build real-world AI models.
4. They talk about AI in an intelligent and well-informed way. The right vendor will talk about their product, of course, but they should also publicly discuss the broader nuances of AI. Topics like how to handle data security, IP and why some AI approaches are better than others. This shows you that the vendor has considered AI from many angles and has put the right infrastructure in place to be a trusted partner.
5. They acknowledge what their AI can and can’t do… yet. The right vendor acknowledges the limitations of their AI and will work with you to define the right deployment strategy. AI is evolving fast, so don’t discount AI tools who don’t offer everything you need out-of-the-box. Often a capability is coming very soon, and as a trusted partner, the vendor can prioritize your needs above others.

This is a difficult time for leaders who know they need to develop and deploy an AI strategy sooner rather than later. But, it’s also a time where finding the right partner backed by your AI business strategy can set you lightyears ahead of the competition. 

Ready to start with AI drawing and design reviews? Or, want to chat about what an AI strategy looks like for your engineering team? Let’s talk.