Why Leaders are Trapped in AI Vendor Hell (And How to Escape)

The 8 Things You Must Do BEFORE You Sign the Contract

Last week, I attended a professional forum focused on AI adoption. What I heard should terrify everyone tasked with bringing AI into their business.

These weren't just failure stories—they were expensive, career-damaging disasters that followed a predictable pattern.

Further, these weren’t small firms fumbling with basic technology. They were established institutions with substantial budgets, experienced teams, and a genuine ambition for AI transformation.

Yet every single one was trapped in what can only be described as vendor hell.

One finance leader put it bluntly: “We’ve done a number of POCs, but we’re not seeing any results. We’ve been running things for months and it’s just not working.”

This isn’t an isolated problem.

According to recent industry analysis, 42% of AI initiatives are now abandoned before reaching production—up from just 17% the year before.

And the primary culprit in this case isn’t poor technology, it’s systematic vendor manipulation that’s holding enterprise AI hostage.

Let’s get into it.

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The Vendor Lock-In Trap

The concept of vendor lock-in obviously isn’t new. However, when it comes to AI, there’s a formula that's become endemic.

Here's how the trap seems to be working.

Step 1: The Pressure Campaign

Leaders face mounting pressure to "have an AI story". I’ve come up against this a lot, you’ll have seen me refer to it as the “we need AI agents in the business” narrative.

Board members and peers ask pointed questions about AI strategy.

Competitors announce AI initiatives.

The pressure to act becomes overwhelming.

Step 2: The Vendor Swarm

Major vendors sense opportunity. They arrive with polished demos, compelling case studies, and promises of quick wins.

The technology looks impressive. The ROI projections seem reasonable.

In many cases the old chestnut of “adoption incentive” (amusingly referred to as “AI” - it seems business vernacular isn’t without its sense of humor) is served up, making it look at the beginning of the process as if there’s more carrot than stick on offer.

Step 3: The Commitment Before Understanding

Here’s where the trap snaps shut. It’s all downhill from here.

As one participant admitted: “We’d already signed the vendor deal… it’s only once we became familiar with the solution that people started asking, how much is this actually going to cost us?”.

Step 4: The Results Desert

Months—or even years—later, the reality becomes clear.

POCs deliver marginal outcomes.

Business processes remain unchanged.

The promised transformation never materialises.

But the contracts are signed, the relationships are cemented, and escape seems impossible.

One finance director summed it up: “We’re so trapped in these vendor relationships we can’t escape. We’re dictated to. We’re told.”

Does this resonate? Leave comment below. 👇

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Why Traditional Approaches Fail

The experiences I heard reveal three fundamental flaws in the current AI adoption model.

Technology-First Thinking

Vendors lead with technology demos instead of business problem analysis. They show what their AI can do—not whether it should be done. This leads to what one participant called “throwing chatbots at problems”—a scattergun approach that rarely hits meaningful targets.

Vendor-Driven Strategy

Instead of developing independent AI strategies aligned with business goals, organisations allow vendors to define the roadmap. This creates dependency rather than capability, leaving teams “powerless to do anything” about their AI direction.

Commitment Before Evaluation

The most damaging pattern is signing vendor contracts before proper evaluation. As one leader admitted: “Unfortunately, that’s just how it’s structured.”

Organisations commit to solutions before understanding their real costs, risks, or suitability.

This backwards approach explains why McKinsey’s research shows that fewer than one in five organisations report significant bottom-line impact from AI initiatives, despite massive investment.

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The Four-Phase Escape Framework

Here's the framework to use to regain control of your AI destiny.

Phase 1: Readiness Assessment

Before evaluating any technology or engaging vendors, you must understand your capability to implement AI successfully. This isn’t about servers or APIs—it’s about operational readiness across six dimensions.

Tip 1: Conduct a Human Infrastructure Audit

Please. Do this first. Your AI ambition is DOA without it.

The biggest predictor of AI success isn't your technology stack—it's your people's readiness for change.

Assess whether your teams have the skills, time, and motivation to adopt new AI-powered processes.

MIT research shows organisational culture and change leadership—not technical capability—are the key barriers to AI success, with 91% of leaders citing change management as a critical hurdle.

Tip 2: Evaluate Your Data Ecosystem Reality

Don’t swallow vendor promises about easy data integration. Assess your actual data quality, accessibility, and governance.

Several leaders I spoke with admitted that regulatory constraints alone made vendor demos irrelevant. I wrote extensively about this here - your geographic base of operations alone can make AI a complete non-starter for you in some cases.

Phase 2: Evaluation

Once you understand your readiness, you can evaluate solutions objectively rather than being led by vendor presentations.

Tip 3: Apply the Six Critical Questions Framework

Before any vendor demo, tee up the six questions that expose the gaps between marketing promises and production reality:

• How does your solution handle memory after 500+ interactions and how is it administered and managed over time? The majority of vendors have absolutely no answer to this question.

• How do you detect and prevent hallucinations in production?

• How do we monitor and evaluate the consistency of the system?

• What audit trails exist for decision traceability?

• How do you handle failures gracefully?

• What guardrails can I apply to the system to protect us?

Most vendors cannot answer these questions satisfactorily, revealing the limitations of their solutions and the liability that awaits you.

Tip 4: Demand Proof of Production Performance

Insist on evidence from organisations running in production, not shiny proofs-of-concept. One finance leader told me they wanted “the full end-to-end solution”—not another demo reel.

Where a solution has a great business fit but might not yet have enough production pedigree, the same questions apply, only in a different context - can the vendor prove they have a plan or set of existing procedures to satisfy these points?

Phase 3: Strategic Implementation

Implementation should be driven by business strategy, not vendor capabilities.

Tip 5: Design Low-Risk, High-Impact Pilots

The group kept saying they wanted “low risk, high impact POCs” but couldn’t achieve them because of vendor entanglement. Your pilots should test business value hypotheses—with measurable outcomes inside 90 days.

Tip 6: Maintain Implementation Independence

Avoid vendor-led implementation approaches that create deeper dependency. Develop internal capabilities for AI project management, ensuring you retain control over timelines, priorities, and success criteria.

Phase 4: Performance Measurement

Measurement must focus on business impact, not technology metrics.

Tip 7: Establish Baseline Business Metrics

Before implementing any AI solution, establish clear baselines for the business processes you're trying to improve. This enables objective assessment of AI impact rather than relying on vendor-provided success stories.

Tip 8: Implement Continuous Value Assessment

Set up mechanisms for ongoing measurement. Several leaders admitted they couldn’t do “life cycle cost modelling.” Without it, you’re flying blind.

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The Evidence Against Vendor-Led AI

The problems described in the forum aren’t isolated incidents. Recent research and surveys from the past year show that vendor-led AI initiatives are overwhelmingly failing to deliver value.

MIT Research: 95% of Enterprise AI Pilots Are Failing

A groundbreaking August 2025 report from MIT's NANDA initiative reveals the shocking reality behind enterprise AI adoption. Despite massive investment and vendor promises, 95% of generative AI pilots at companies are failing to deliver meaningful results.

The research, based on 150 interviews with leaders, a survey of 350 employees, and analysis of 300 public AI deployments, found that while executives often blame regulation or model performance, the real issue is "flawed enterprise integration."

Generic vendor tools like ChatGPT excel for individuals because of their flexibility, but they stall in enterprise environments since they don't learn from or adapt to specific business workflows.

Most damaging is the resource misallocation: more than half of generative AI budgets are devoted to sales and marketing tools pushed by vendors, yet MIT found the biggest ROI potential lies in back-office automation that vendors rarely prioritise.

Industry Analysis: AI Project Abandonment Rates Soar 147%

Multiple industry reports confirm a dramatic surge in AI project failures throughout 2024-2025. The percentage of companies abandoning the majority of their AI pilot projects soared to 42% by the end of 2024, up from just 17% the previous year—a staggering 147% increase in failure rates.

This abandonment epidemic is directly linked to vendor-led approaches that prioritise technology demonstrations over business problem analysis.

Companies are discovering that vendor promises of quick wins and easy implementation are fundamentally disconnected from the complex reality of enterprise AI deployment.

The data reveals that organisations following vendor roadmaps consistently struggle with data security, privacy concerns, and escalating costs that were never properly disclosed during the sales process.

ISACA Security Analysis: Vendors "Overpromising and Underdelivering"

A comprehensive July 2025 analysis by ISACA, the global cybersecurity professional association, directly addresses the vendor manipulation tactics destroying enterprise AI initiatives. The report states unequivocally: "In many cases, vendors are overpromising and underdelivering by adding AI features to tools where they are not necessary."

The analysis reveals that vendors are "rapidly embedding AI into an increasing number of products" not to solve business problems, but to "appear technologically advanced and competitive."

This has led to "bloated tools with superficial AI features that contribute more to complexity, cost, and risk than to meaningful outcomes."

Most concerning is the finding that "AI-enabled features are added to products merely for show—lightweight features that offer little functional value are included to feed into market hype or meet investor expectations."

The report concludes that rather than driving innovation, AI is increasingly being used to meet market expectations rather than operational needs.

These three independent sources—from MIT's academic research, industry failure rate analysis, and cybersecurity professional assessment—paint a consistent picture of systematic vendor manipulation that's holding enterprise AI hostage.

The evidence is clear: vendor-led AI approaches are not just failing to deliver value, they're actively damaging organisations' ability to develop genuine AI capabilities.

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Once You’ve Escaped Alcatraz, What’s Next?

The professionals I spoke with represent dozens of organisations worldwide.

They want strategic AI that delivers real value.

Instead, they feel powerless inside vendor-controlled relationships.

Breaking free requires courage to challenge the status quo. It means refusing vendor-led roadmaps, demanding production evidence, and treating AI as business transformation—not a tech implementation.

The Strategic AI Decision Matrix: Your Vendor-Agnostic Compass

When vendors are pressuring you for that "AI story," you need an independent way to evaluate opportunities without their agenda driving your decisions.

The matrix below gives you exactly that - a clear way to categorise AI project based on two critical factors that matter to your business, not their sales targets.

How to Use This Matrix to Reclaim Control

Every AI project proposal - whether from vendors or internal teams - gets plotted on these two dimensions:

1. Business Impact (will this actually move the needle?)

2. Implementation Difficulty (can we realistically execute this?)

When that next vendor walks in with their shiny demo, don't get distracted by the technology. Ask yourself: "Where does this actually sit on the matrix?"

Quick Wins (Green): Your Vendor Kryptonite

These projects are your secret weapon against vendor manipulation.

High impact, easy to implement - things like document summarisation, email response automation, or meeting transcription. When vendors push complex, expensive solutions, counter with: "Before we discuss your platform, let's prove AI value with these simpler wins first."

Most vendors hate this approach because Quick Wins can often be achieved without their enterprise platforms. But that's exactly why they work - they give you leverage and prove internal capability before you commit to bigger investments.

Strategic Bets (Orange): Where Vendors Want to Start (And Why You Shouldn't)

This is vendor territory - customer service automation, enterprise workflow systems, complex data analysis platforms. High impact, but genuinely difficult to implement.

Vendors love leading with these because they justify large contracts and long-term relationships.

Your response: "We'll consider Strategic Bets after we've proven ourselves with Quick Wins and built internal AI capabilities."

Safe Pilots (Blue): Your Learning Laboratory

Internal chatbots, content formatting tools, knowledge base queries - low stakes projects perfect for building skills without vendor dependency. Use these to develop your team's AI literacy and implementation capabilities.

When vendors dismiss these as "too small," you know they're more interested in their revenue than your success.

Avoid Zone (Red): The Vendor Trap

Complex creative tasks, high-stakes decision making that requires expert domain knowledge, poorly-defined processes with regulatory constraints.

They're exactly the type that end up in that 42% abandonment rate we discussed earlier without the right folks at the helm.

The video below is an excellent frame for understanding the complexity in building Red Zone systems. If vendors pitch projects like this without the level of expertise demonstrated in this presentation, run.

Breaking the Vendor Sales Cycle

Armed with this matrix, your vendor conversations change completely.

Instead of letting them drive the agenda with technology demos, you control the conversation:

• "Show me how your solution delivers Quick Wins first."

• "Prove this won't end up in the Avoid Zone."

• "What evidence do you have of Strategic Bets actually scaling in production?"

The matrix forces vendors to justify business value rather than showcase technical features. Most importantly, it gives you a vendor-agnostic way to evaluate every AI opportunity against your actual business needs, not their quarterly targets.

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Four Key Takeaways

1. Vendor Lock-In Is Systematic, Not Accidental

The patterns described reveals deliberate strategies designed to create dependency before demonstrating value. Recognising this helps you avoid the trap.

2. Strategy Must Precede Technology Selection

Organisations that develop independent AI strategies before engaging vendors achieve significantly better outcomes than those who allow vendors to define their AI direction.

3. Business Readiness Trumps Technical Capability

The biggest predictor of AI success isn't your technology infrastructure—it's your organisation's readiness for the business process changes that AI requires.

4. Independence Is Your Competitive Advantage

While your competitors remain trapped in vendor-led strategies, building independent AI capability becomes your competitive moat. Vendors fear organisations that know how to evaluate, implement, and measure AI success without them. You want to be on this side the equation because this is how you get the best terms!

Ultimately, don't let your organisation become another cautionary tale in the vendor lock-in epidemic. Take control of your AI strategy before the vendors take control of you.

Until next time,

Chris

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