Author(s): Abdul tayyeb Datarwala
Originally published on Towards AI.
My journey building operational intelligence — and why most AI initiatives quietly die
I’ve built AI-enabled systems that scaled revenue, cut operational cost by multiples, and replaced chaos with clarity.
I’ve also watched brilliant AI initiatives fail — not because the models were bad, but because the system was never designed to carry them.
That contrast is why I write this.
Most AI content today talks about tools, agents, and models. Very little talks about how businesses actually operate once AI is introduced. Even less is written by people who’ve had to live with the consequences when systems break at scale.
This article is my personal perspective —
as a founder who designs business-running operating systems, not AI features.
I’ll share:
- The real problem behind failed AI programs
- Concrete examples from systems I’ve built and seen fail
- What Operational Intelligence actually looks like in practice
- And what’s coming next — whether organizations are ready or not
This is written for serious builders: CTOs, COOs, AI leaders, founders, and operators who are tired of demos that never turn into leverage.
The Pattern I Kept Seeing (and Couldn’t Ignore)
Early in my career, I was excited by AI the same way everyone else was.
I built predictive models. Automation pipelines. Optimization engines. The work was technically sound — and often impressive.
Yet something kept bothering me.
Even when the AI worked, the business didn’t always improve.
In one case, we deployed a highly accurate forecasting model for a mid-market manufacturing client. Leadership loved the charts. Accuracy was north of 90%. Everyone called it a success.
Six months later, the operations team was still making decisions the same way they always had.
Why?
Because the model didn’t live inside the system where decisions were made.
- It produced insight — but had no authority.
- It generated intelligence — but had no operational role.
That was my wake-up call.
I see this everywhere now. A recent MIT Sloan/BCG study found that 55% of organizations were piloting AI in 2023, but most struggled to move from pilot to production. The gap isn’t technical — it’s architectural.
The Core Truth Most AI Teams Miss
Here’s the uncomfortable truth I had to learn the hard way:
AI does not fail because it’s inaccurate.
AI fails because it’s architecturally homeless.
Most organizations bolt AI onto:
- Fragmented workflows
- Conflicting KPIs
- Siloed ownership
- Legacy approval chains
Then they wonder why nothing scales.
This creates what I now call AI Theater:
- Demos without deployment
- Insights without action
- Automation without accountability
I saw this pattern play out at a Series B startup where the CEO kept asking for “AI strategy.” They’d built three different ML models — customer churn prediction, dynamic pricing, inventory optimization — all technically solid. But none of them talked to each other. Sales didn’t trust the churn model. Pricing couldn’t access inventory data. Finance had their own spreadsheets.
The real problem? They’d built AI solutions before they’d built a decision architecture.
Once I saw this pattern, I stopped “building AI solutions.”
I started architecting operating systems.
What I Mean by “Systems That Run the Business”
When I say I design systems that run the business, I mean this literally.
In one transformation I led for a fast-growing B2B SaaS company, the organization was bleeding efficiency:
- Sales, supply chain, quality, finance, and engineering all operated in silos
- Decisions were made in spreadsheets and email threads
- Leadership had dashboards, but no control
- Every strategic initiative required manual coordination across six different tools
AI alone would not have saved this.
So I started with the system.
Step 1: Mapping How Work Actually Flows
Not how leadership thinks it flows.
Not how the org chart says it flows.
How it really flows — where it stalls, loops, or breaks.
I spent two weeks just watching. Sitting in on calls. Reading Slack threads. Following a single deal from lead to close. What I found was brutal: a $50k deal required 47 handoffs across 12 people and four systems, with an average 8-day delay at procurement approval.
That map became the foundation.
This is what Deloitte’s research on agentic AI keeps hammering on — organizations fail because they layer agents onto broken processes. You can’t automate chaos and call it progress.
Step 2: Designing Decision Architecture
Before introducing AI, I forced clarity on:
- Which decisions mattered most
- Who owned them
- What inputs they actually used
Only then did AI enter the picture — as a decision participant, not a sidecar.
For example, in our procurement workflow:
- AI could recommend sourcing decisions within defined cost and risk thresholds
- Humans retained override authority for exceptions
- Every decision path was logged and auditable
- The system would escalate when confidence was below 75%
This single design choice changed adoption overnight.
Why? Because we’d answered the question everyone was silently asking: “Who’s responsible when the AI screws up?”
World Economic Forum’s recent paper on AI agent governance calls this “agent accountability mapping” — and they’re right. Without it, you get finger-pointing, not adoption.
Step 3: Embedding AI Inside Workflows
Instead of asking users to “check the AI tool,” we embedded intelligence directly into:
- CRM workflows (Salesforce automation that suggested next actions)
- Procurement approvals (auto-routing based on risk scores)
- Quality review gates (flagging anomalies in real-time, not in weekly reports)
If AI wasn’t used, it wasn’t because people resisted change — it was because the system allowed them to bypass it. We closed that gap intentionally.
I learned this the hard way. In a previous role, we built an amazing AI assistant that sat… in a separate tab. Usage dropped to 8% within three months. Turns out people won’t context-switch for “nice to have.” They’ll only adopt what’s unavoidable.
Step 4: Building Control Loops (Not Just Automation)
One lesson I learned the hard way at a fintech client:
The faster a system acts, the more dangerous it becomes without guardrails.
Every AI-enabled system I design now includes:
- Drift monitoring (we caught a model degrading 40% before it hit production)
- Exception escalation (anything outside confidence thresholds routes to humans)
- Kill switches (literally big red buttons in Slack channels)
- Human-in-the-loop checkpoints (certain decisions always require approval)
These aren’t compliance theater.
They’re what make leadership trust the system.
Bain’s 2025 Tech Report is blunt about this: companies that skip governance infrastructure see 3x higher failure rates in agentic AI deployments. The smart money is building guardrails first, speed second.
What Operational Intelligence Looks Like in Practice
Operational Intelligence is not a dashboard.
It’s the ability for an organization to:
- Sense reality in near real-time
- Decide with clarity
- Act with confidence
- Learn continuously
In systems I’ve built, this meant:
- Real-time visibility into bottlenecks (not weekly reports that were outdated before they hit inboxes)
- AI that didn’t just predict — but triggered action (automatic PO generation when inventory hit reorder points, not alerts)
- Feedback loops where outcomes retrained behavior (every closed deal improved lead scoring)
The result?
- Faster decisions (procurement cycle time dropped from 12 days to 3)
- Fewer firefights (operations team stopped working weekends)
- Compounding ROI over time (systems got smarter with every transaction)
Not because the AI was smarter — but because the system was coherent.
Where Most AI Architectures Collapse
I’ve seen AI programs with massive budgets collapse at scale.
Not due to bugs — but due to:
- Incentive misalignment (sales didn’t want AI flagging their “sure thing” deals as risky)
- Fear of automated accountability (managers worried about being measured by their team’s AI-augmented output)
- Unclear ownership when AI decisions go wrong (legal, product, and ops all pointed fingers)
This is why I say:
Scaling AI is an organizational problem disguised as a technical one.
If you don’t design for people, power, and process, your architecture will fail — no matter how advanced your models are.
McKinsey’s research on “agentic organizations” found that governance becomes the bottleneck to AI adoption, not compute or models. The winners are redesigning org charts around outcomes, not hierarchies.
I saw this up close at a healthcare startup. They’d built an AI triage system that was legitimately impressive — better than most doctors at initial diagnosis. It failed in six months. Not because it was wrong, but because doctors wouldn’t trust a system they couldn’t interrogate, and legal wouldn’t approve a system without clear liability boundaries.
We rebuilt it with full decision provenance — every recommendation showed its reasoning chain. Adoption went from 12% to 87% in two months. Same AI. Different architecture.
What’s Coming Next (From the Front Lines)
Based on what I’m seeing now — and what leaders like Anshuman Singh (Aays) and Raj Babu (Agilisium) are posting about on LinkedIn — here’s what’s coming fast.
1. AI Becomes Digital Labor
Not copilots.
Not chatbots.
Role-defined digital operators with:
- Job descriptions (“Customer Onboarding Agent,” not “chatbot”)
- Authority boundaries (can approve up to $5k, must escalate above)
- Performance metrics (measured on outcome quality, not just speed)
Organizations that don’t architect for this will drown in unmanaged agents.
I’m already building these systems. One client has 14 AI “employees” — each with a defined role, reporting structure, and performance dashboard. HR literally tracks them alongside human headcount.
Gartner predicts 15% of daily work decisions will be autonomous by 2028. That’s not a future state — that’s 36 months away.
2. Agent Governance Will Become Non-Negotiable
As agents multiply, governance becomes the bottleneck.
Expect:
- Agent registries (centralized tracking of every agent, its permissions, and its actions)
- Permission systems (OAuth for AI agents — who can do what, where, when)
- Action audit trails (complete decision provenance, not black boxes)
This is already happening quietly in advanced organizations. IBM and Deloitte are both publishing frameworks. The World Economic Forum just released their “AI Agents in Action” governance paper — it’s a blueprint for what’s about to become table stakes.
The EU AI Act is forcing this conversation whether companies are ready or not. High-risk AI systems require real-time explainability and behavioral observability. US regulations are coming.
3. Operational Intelligence Replaces “AI Strategy”
Executives are tired of buzzwords.
The winning question is becoming:
“Show me how intelligence flows through our operations.”
Not slides.
Systems.
I’ve had three board meetings in the last quarter where investors stopped asking about “AI roadmap” and started asking about “decision latency” and “agent utilization rates.” The conversation is shifting from potential to performance.
4. System Architects Will Matter More Than Ever
Models will commoditize.
The rare skill will be knowing:
- Where AI belongs
- Where it doesn’t
- And how to make it survivable in the real world
That’s systems architecture.
You’re already seeing this in hiring patterns. Companies are posting roles for “AI Systems Architect,” “Agent Operations Manager,” and “AI Governance Lead” — positions that didn’t exist 18 months ago.
The founders and CTOs winning right now aren’t the ones with the best models. They’re the ones who know how to embed intelligence into operations without breaking trust.
The Lesson I Had to Learn (and You Might Too)
If I could distill everything I’ve learned into one line, it would be this:
AI success is not about being clever.
It’s about being structurally sound.
The organizations that win won’t have the flashiest demos.
They’ll have the calmest operations.
They’ll have systems that:
- Make decisions visible (full audit trails, no black boxes)
- Make accountability explicit (clear ownership at every decision point)
- And make intelligence operational (AI that lives in workflows, not dashboards)
That’s what I build.
That’s what Operational Intelligence really is.
And as AI accelerates, it’s the only approach that lasts.
If this resonated, you’re probably already feeling the gap:
Between AI promise and operational reality.
That gap isn’t solved by another tool.
It’s solved by architecture.
I’m building the next generation of operational intelligence systems for companies that are done with AI theater. If you’re a founder, CTO, or COO tired of pilots that never scale, let’s talk about what real AI architecture looks like in your business.
Published via Towards AI
