Every enterprise AI project has the same failure point — and it’s not the model.
I’ve now seen this pattern repeat across industries, from industrial automation to financial services to gaming and semiconductor design. The model performs well in the lab, the pilot looks promising, and the business case is approved. Then everything slows down — not because the model stops working, but because the data foundation underneath wasn’t built for the kind of reasoning modern AI requires.
TL;DR: Most AI systems don’t fail because of the model — they fail because the data architecture wasn’t designed for multimodal, context-driven AI. If your graph data, vector embeddings, documents, events, and transactional records all live in separate systems, no amount of orchestration can make AI accurate, explainable, or scalable. You just get more pipelines, more duplication, more latency, and a widening dependency chain.
Forrester’s new report calls this out clearly: multimodel data platforms are becoming “the missing layer in the AI stack.” They unify graph, vector, document, search, and relational data in one engine — so AI can retrieve and reason with context, instead of guessing from partial signals. Companies like NVIDIA, Siemens, Synopsys, Cloud Imperium Gaming, and Articul8 are already making this architectural shift.
Indranil Bandyopadhyay is a Principal Analyst at Forrester who advises enterprises on data and AI platforms, architecture, and strategy. Before Forrester, he spent 12 years as a CIO leading award-winning digital transformation initiatives. He says it best in his LinkedIn post.
“The rise of agentic AI is forcing enterprises to confront a hard truth: traditional data architectures weren’t built for this moment.”
— Indranil Bandyopadhyay, Principal Analyst, Forrester
The question is no longer whether enterprises move to this model — only when, and whether they do it before the cost of maintaining the old approach becomes unsustainable.
The Wrong Assumption Most Teams Still Hold About AI
There’s still a belief that if we improve the model — a better LLM, more GPU, more fine-tuning — the outcomes will improve. But the bottleneck has shifted. Once a model reaches baseline performance, accuracy, transparency, and scalability are no longer model problems — they are data architecture problems.
In all of my conversations with leaders over the last 6 months, the story is the same:
“AI doesn’t fail because of the model. It fails because the data underneath it wasn’t designed for context.”
If the system can’t access relationships, meaning, history, and dependencies behind the data, it can only pattern-match — not reason.
That’s the gap most enterprises are running into now.
Why the Old “Analytics Stack” Can’t Support AI in Production
The last decade of data architecture was built for analytics: dashboards, reporting, KPI visibility. That world assumed structured data, batch updates, and humans interpreting results.
AI is different. AI needs:
- Real-time access
- Multiple data models in one query
- Ability to reason across relationships, not rows
- Context, not just content
Which is why the stack is breaking in the same way everywhere:
Old Pattern:
New Pattern:
“The bottleneck isn’t compute — it’s context.”
You can scale GPUs. You can’t scale pipelines forever.
“Stitching together relational databases, warehouses, and lakes might have worked for yesterday’s analytics, but it’s a brittle foundation for AI systems that demand real-time, multimodal context.”
— Indranil Bandyopadhyay, Forrester
What Forrester Is Seeing in the Market
Forrester’s new report, Multimodel Data Platform: The Missing Layer in Your AI Stack, highlights a clear architectural shift: enterprises are moving away from polyglot persistence and toward multimodel data platforms (MMDPs) — single systems that unify graph, vector, document, key-value, and relational data natively.
Not as a convenience. As a requirement for AI that is accurate, explainable, and scalable.
Why Context Matters More Than Compute
A model can generate an answer without understanding anything. But an enterprise can’t act on that answer unless it’s:
- Correct
- Traceable
- Explainable
- Aligned to business rules and constraints
That only happens when the AI system has access to the full picture — not just a slice of it.
“Without context, AI can generate output — but not advantage.”
And enterprises aren’t investing in AI for output. They’re investing for outcomes.
Named Examples of the Shift Already Underway
This isn’t theoretical or “coming soon.” It’s already happening:
- NVIDIA uses a multimodel foundation to power GPU-accelerated context-aware GraphRAG and agentic AI use cases.
- Siemens brings product, sensor, and knowledge data together in one platform instead of a chain of disconnected systems.
- Synopsys uses multimodel data to support engineering, security, and IP graph intelligence.
- Cloud Imperium Gaming unifies game telemetry, player graph data, and real-time context to enable dynamic in-world behavior.
- Articul8 (AI-native) uses a unified data layer so its developers can focus on building agent and co-pilot capabilities, not pipelines and glue code.
Different industries. Same pattern:
Once AI is expected to operate, not just demo, the data architecture has to change.
How to Know If Your Data Architecture Is AI-Ready
Ask 3 questions:
- Can your AI system query across data models without stitching different pipelines and duplicating data?
- Can you explain “why” the system produced an answer, not just what the answer was?
- Can you scale AI without adding more databases, pipelines, or indexing layers?
If the answer to any of those is no, the model isn’t the problem — the data foundation is.
My Point of View
The next decade of AI advantage won’t come from bigger models, more GPUs, or more fine-tuning. It will come from data architectures that can deliver context at scale — context the model can trust, the business can verify, and the customer can rely on.
“AI maturity is now defined by data architecture maturity.”
And the teams that adapt early won’t just ship AI faster — they’ll ship AI that lasts.
Read the Forrester Report
If you’re evaluating whether your current data stack is built for production AI — or whether it will slow you down in the next phase — Forrester’s report is worth the read.
