Eighty-eight percent of companies say they use AI, yet 95% of generative AI pilots fail, and 56% of CEOs report zero financial benefit from AI investments, according to AI Business Weekly.

My conversations with data and AI leaders reveal similar disparities between AI usage and AI-driven outcomes. But the people I speak with know the cause: fragmented, outdated, or incomplete context. And they know even the best models are just guessing if that’s all they can access.

Gartner realizes that significance, recently creating an entirely new market category for “AI Context Platforms” valued at $28 billion for 2026 and $78 billion by 2030. And so do other vendors, who are now racing to capture a share of the market even though their solutions don’t measure up.

It’s a very confusing convergence. But as someone who co-founded a contextual AI company two years ago, who has spent nearly every day since then listening to leaders describe their domain-specific context challenges, and who systematically studies every product, tool, and platform claiming to deliver rich business context to AI, I’ve identified seven factors that definitively delineate effective contextual AI from incomplete or ineffectual approaches. 

Below are the criteria I apply, that I encourage others to use to evaluate any kind of contextual AI solution, including ours.

1. Push Past the Phrase “Continuously Learning”

This term gets used loosely, and it hides two fundamentally different architectures. 

In one, the adaptation layer sits outside the base model weights and updates in near real time. In the other, you’re waiting on a retraining cycle every time something in your business changes, which isn’t continuous learning at all.

Ask any vendor:

• Does your adaptation layer sit inside or outside the base model weights?
• Is retraining required to keep content current?
• How long does it take the model to reflect new/changed information, processes, outputs, etc.?
• Can the system detect context drift before users start reporting wrong answers?
• Do corrections made by one user improve the system permanently, for everyone?

If those answers involve a retraining schedule measured in weeks or longer, that’s not the architecture I’d bet my business on. And neither should you.

2. Time the Data-Prep Process, Not the Demo

Most enterprise AI projects burn 6-18 months of data operations to deliver a single working model, with data prep alone eating up 70-80% of the total effort. That timeline’s the part most vendors gloss over because it’s the least impressive thing about their product.

So I recommend buyers ask vendors pitching a contextual AI solution these questions upfront:

• How long will it take to connect all our disconnected data sources?
• How long will it take to transform and validate that data?
• With which data tools/platforms/systems does your AI natively integrate?
• Will your AI access structured records and unstructured documents in a single query?
• Will your AI recognize the same customer, supplier, or product consistently across systems?

Platforms exist now that eliminate the data-prep bottleneck almost entirely by mapping everything into a live knowledge graph during ingestion, which is the whole point of building one.

3. Demand Proof the Knowledge Graph Can Actually Keep Up

Institutional knowledge doesn’t sit still, and most knowledge graphs weren’t built to move at its speed. 

GPU-accelerated graphs are the only ones that can deliver inference fast enough for users to trust the outputs in the moment they’re needed. 

That’s why I want any customer evaluating knowledge graphs to ask:

• Can you explain the type and quantify the amount of manual retraining and intervention your model requires to keep information current?
• Will every output be grounded in verified, persistent memory?
• Will your knowledge graph prioritize more recent versions of knowledge above older versions?
• Will the system understand relationships and dependencies (how a pricing change affects open quotes, then pipeline, then forecast) instead of just understanding isolated facts?
• Will I get the same answer if I ask the same question twice?
• Is the graph building as fast as a vector RAG, and is it actually faster than RAG?

Consistency and speed aren’t nice-to-haves. They’re the difference between a tool people trust and one they quietly stop using.

4. Protect What Your Competitors Shouldn’t Get

Not every model creates an advantage solely for the company paying its bills. 

And building on general-purpose infrastructure without guardrails can hand your competitors the exact edge you built and paid for over decades. 

So before any of your data goes near a training set, get clear answers to these questions:

• Where will the data we want excluded from training actually live?
• How do we guarantee private data we want excluded never finds its way into a training set?
• How long will it take us to train and implement new models?
• Will our model secure data more rigorously than a cloud ERP platform secures financial data?

Any vendor who gets vague here is telling you something important. (BTW, don’t trust enterprise agreements on APIs. They’re black boxes).

5. Don’t Let One Model Touch Everything

Companies unwilling to fund a custom model for every use case often default to a single model for everything. That shortcut has a real cost, and the biggest one is data security. 

The right questions to ask here are:

• Can you apply role-based permissions specifically to each model?
• Does the platform route sensitive data to compliant, sovereign infrastructure?
• Can I deploy this on infrastructure I own?
• Can this model be air-gapped?
• Can the model trace every output back to its sources and reasoning path, in a form a regulator would accept?

6. Model the Token Costs Before They Model You

Nobody yet knows how many tokens a fully agentic business will ultimately require, and agents that consume hundreds of thousands of tokens per task are depleting even generous AI budgets with no ceiling in sight. 

So I push every buyer to get real numbers like these before signing a contextual AI contract:

• What’s a realistic estimate for monthly AI costs once data volumes and users are 10x and 100x what they are today?
• By how much will agent prompts increase our token costs?
• How will my budget be affected if frontier model providers raise token prices to become profitable?
• How much less should our second use case cost compared to our first?
• How will we calculate AI’s total cost of ownership across the entire organization?

7. Be Honest About What You Can Build Yourselves

Building custom, domain-specific AI in-house or on top of general-purpose models like Claude or GPT demands dedicated leadership focus and specialized technical talent that’s extremely hard to find right now, per Axiom‘s compensation data on AI engineering roles. 

So before trying to build contextual AI in house, ask these important questions first:

• How many data scientists and AI/ML/MLOps engineers do we employ with the bandwidth to build and maintain domain-specific AI for us?
• How much budget do we have to hire additional resources to build and maintain contextual AI ourselves?
• How abundant/scarce are the roles we need to hire in the geographies we want?
• What’s our realistic backup plan for delivering domain-specific AI if we can’t find the talent we need?

The Bottom Line

Use these criteria to vet any contextual AI approach, including ours. Frankly, that’s the only pitch I’m interested in winning.

Find out how 4Minds has reimagined AI to be a living, breathing system of adaptive, contextual intelligence that you control. One that sees clearly across fragmented institutional knowledge. Reasons easily across workflows. Learns continuously. Adapts automatically. And deploys how and where you want it. Visit us at www.4minds.ai.