From 18 Months to 18 Days: Inside Turinton’s Enterprise AI Playbook

India’s enterprise AI journey is hitting a hard reality check. Despite aggressive investments in data platforms, consultants, and pilots, nearly 90 per cent of AI initiatives fail to scale beyond proof-of-concept. What should take weeks routinely stretches into 12–18 months, leaving enterprises with dashboards but no decisions, and models but no material outcomes.

For manufacturing and CPG firms, the cost of delay is tangible, unplanned downtime, compliance exposure, inventory mismatches, and regional sales volatility, often translating into crores of lost value. The problem, as several operators now admit, is not lack of data or ambition, but the gap between AI insight and operational action.

Turinton is positioning itself squarely in this gap.

Founded in 2023 by Vikrant Labde, Nikhil Ambekar, and Vikas Kumar Singh, the company is building what it calls a business-first AI platform, one designed to shorten adoption cycles by working with existing enterprise systems rather than replacing them. Its platform, Insights AI, sits on top of operational data across supply chain, manufacturing, and customer systems, aiming to move organisations from reactive firefighting to decision-led foresight.

Early deployments across manufacturing and CPG indicate a focus on narrow, high-impact use cases, quality checks, compliance validation, predictive maintenance, and sales anomaly detection, where outcomes can be measured in weeks rather than quarters.

While speaking to CiOL, Turinton’s Nikhil Ambekar explains what actually makes AI work:  According to Nikhil Ambekar, Co-founder & CEO, Turinton AI, the failure of enterprise AI is rarely about model sophistication.

“Most deployments fail because organisations expect technology to compensate for unclear ownership, slow workflows, and poor data discipline,” he says. “AI doesn’t break because it’s inaccurate. It breaks because no one is ready to act on it.”

Ambekar argues that AI adoption must be evaluated through CFO-aligned metrics, downtime reduction, working capital improvement, audit readiness, and not model accuracy scores. He also stresses that speed comes from architectural choices and organisational readiness, not exaggerated claims of instant transformation.

Interview Excerpts

You claim enterprise AI adoption can shrink from 18 months to 18 days; can you walk us through one complete deployment example with clear milestones and the specific business outcome achieved?

That's a fair challenge. When we say rapid deployment, we're talking about specific types of use cases where the value is concentrated and the organisational alignment is already there.

Let me walk you through a fibre optic manufacturer. They had a specific problem: proof testing was completely manual. Raw PDF documents with varying formats, structured comparison against proof schedules, and manual sentencing reports. It was eating up time and introducing human error.

In week one, we ingested their proof result documents and proof schedule templates into our knowledge graph. In week two, we built the extraction and comparison logic. By week three, we had a working prototype generating sentencing reports. In weeks four through twelve, we validated outputs against their audit requirements, trained the team, and hardened the system for production.

The result was a 70 per cent reduction in manual review time, standardised sentencing across batches, improved audit readiness with downloadable reports, and enhanced traceability across the workflow. Three months total.

Now, is that 18 days? No. That's honest. What is true is that they went from months of consultants analysing their process to production-grade AI delivering business value in twelve weeks. Most AI deployments take eighteen months. We compress that significantly when the problem is well-defined, the data is accessible, and the team is ready to implement.

The "18 days" framing applies to very specific scenarios where we're automating a single, well-defined decision within an already instrumented process. A quality control gate. A compliance check. A routing optimisation. Not enterprise-wide transformation. But even in those cases, the speed comes from our architecture, not magic.

How do you define and measure ROI for enterprise AI? Which financial KPIs matter most, and what is the typical payback period you see in manufacturing and CPG?

Stop measuring model accuracy. Measure what the CFO cares about: cost reduction, quality improvement, faster decision-making, and working capital improvement.

In manufacturing, the KPIs are straightforward. Reduction in unplanned downtime translates directly to production revenue. A fibre optic manufacturer deployed predictive maintenance and saw a 35 per cent reduction in unplanned downtime, a 22 per cent improvement in OEE, and saved 1.4 million dollars annually in maintenance and scrap costs. That's measurable.

In CPG, we had a pharmaceutical packaging manufacturer dealing with defect rates in blister packaging. They were losing product to micro-seal failures that affected drug shelf life. We deployed computer vision and real-time anomaly detection. The result was a 67 per cent reduction in defects, $2.8 million saved in rework costs, a 40 per cent reduction in batch release time, and passed FDA compliance audits with zero deviations.

Payback in manufacturing typically ranges from three to six months. If you focus on real business problems, the financial case becomes obvious within a quarter.

Tell me about a deployment where the platform didn't deliver expected value. What went wrong, and what did you learn from that experience?

The most instructive situation was a predictive maintenance deployment where the model was solid, but the maintenance teams couldn't act fast enough.

The system predicted bearing failures three days out. Maintenance scheduling took five days. The intelligence was there. The human workflow wasn't.

What we learnt is that deployment success isn't about having the best algorithm. It's about redefining workflows so insights actually get acted on. We now validate organisational readiness and data quality before deploying AI. That’s non-negotiable.

When you say the platform “plugs on top” of existing systems, what integrations are truly out-of-the-box?

We work with existing ERP, MES, logistics, and supply chain systems without requiring data migration or heavy ETL.

Where complexity arises is in dark data, legacy systems, spreadsheets, and manual workarounds. The bigger challenge is organisational, not technical. Integrating the platform is easier than integrating teams around decision ownership.

What guarantees do you provide around data governance, explainability, and security?

We work with data where it lives. Nothing is centralised or moved. Our architecture supports real-time audit trails and explainable recommendations.

A pharmaceutical packaging manufacturer achieved FDA 21 CFR Part 11 compliance using our system and passed audits with zero deviations. Security-wise, we operate entirely within customer infrastructure and align with required frameworks like NIST and ISO 27001.

If AI becomes a core business utility by 2026, what must enterprise leaders change now?

The winners won’t reorganise around technology. They’ll reorganise around decisions.

KPIs must shift to decision velocity, decision quality, and outcome realisation. Governance should enable speed, not slow it down. The companies that succeed will be the ones that trained teams to act fast and created accountability for outcomes.