VP - AI & Digital Transformation

Vashi Integrated · Retail & E-commerce

Navi Mumbai, India
On-site
Posted today
Consulting & Strategy

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About the job

Experience: 15

Reports to: CEO

Location: Turbhe, Navi Mumbai

Mandate horizon: IPO readiness and the 3-year turnover-doubling trajectory

Why this role exists now

Vashi runs on capable platforms — S/4HANA, Salesforce, e-commerce, Digiserve — and a technology team that keeps them reliable. That foundation is solid and must stay solid. But the next phase of growth will not be won by running systems better. It will be won by making Vashi faster and easier to do business with, by taking time, cost and friction out of how the business actually runs, and by making AI part of everyday work before the market forces the question.

Today, technology leadership is organised around running the estate: infrastructure, platforms and applications. What is missing is one accountable owner for the change agenda — the person who decides which workflows to transform and how, builds the business case, takes AI out of pilots and onto the floor, and proves the result in turnaround time, customer experience and gross profit. This role exists to be that owner.

We are not hiring a platform steward, a programme administrator or an innovation lab. We are hiring a leader who treats every quote, every procurement cycle, every credit decision and every delivery as a process that can be made dramatically faster, cheaper and more trustworthy — and who gets the change adopted.

This is the go-to leader for AI and digital change at Vashi — a builder of velocity and adoption, not a custodian of systems.

The mandate

Velocity: Cut the time Vashi takes to respond to the market — quotation, order processing, credit, procurement, fulfilment — using AI and automation, with results visible in turnaround time (TAT), not slideware.

Customer: Make Vashi measurably easier and faster to buy from, so that customers feel the speed and the LGP relationship deepens. A tool that does not reduce friction or build trust is not serving our purpose.

Productivity & profit: Raise productivity and lower cost-to-serve across the cluster model, with every initiative carrying a business case that Finance validates in gross-profit and working-capital terms.

AI-first: Make AI and data science a standing capability across the company — use-case pipeline, models in production, guardrails, adoption and measurement — raising the Digital Quotient of every function. Pilots and models that never reach the floor do not count.

Make it stick: Lead adoption through the business heads and the technology peer group, so that new ways of working are used every day, not demonstrated once.

What you will own

Transformation portfolio & value

Own a prioritised map of Vashi’s end-to-end workflows — quote-to-cash, procurement,

credit, fulfilment — and a hard target for cycle-time and cost reduction in each, with a

baseline measured before work begins.

Own the business case and benefits tracking for every transformation and AI initiative —

one scoreboard, validated by Finance, covering TAT, customer experience, productivity

and gross profit.

Drive build-vs-buy-vs-partner decisions with frugality: every rupee must earn its keep. No

over-engineering, no vanity pilots, no consultant decks without a deployed result.

AI capability

Stand up the AI operating model: use-case pipeline, data readiness, guardrails and

responsible use (including data-privacy obligations), adoption and measurement across

functions.

Move AI from pilots to production — embedded in how sales, procurement, credit, support

and finance actually work day to day — and retire the pilots that do not earn their place.

Be the in-house authority on what AI can and cannot do for Vashi, separating real

capability from vendor hype for the CEO and the leadership team.

Data science & decision intelligence

Own the data science agenda for the workflows that matter most — for example demand

forecasting and replenishment, quote and margin optimisation, credit-risk scoring,

procurement analytics and customer propensity — chosen by value, not by novelty.

Build a lean data science and ML capability and run models as products: every model has

an owner, the decision it changes, a measured value, and a plan for monitoring, retraining

and retirement.

Define decision-grade data with the business and the peer group. Platforms and

Infrastructure own the data platform and pipelines; this role owns the models, the use

cases and the decisions they improve.

Set model governance — validation, explainability, drift and bias checks, and data-privacy

compliance — so that managers can trust models and auditors can stand behind them.

Customer experience & speed to market

Redesign the customer-facing journeys — quote to order, order status, credit, delivery and

service — so that speed and transparency are built in, not added by effort.

Make speed a system, not an effort: faster quotation, lower TAT and fewer manual

handoffs across the cluster model.

Working through the technology peer group

Set the transformation and AI direction. The Heads of Infrastructure, Platforms and

Applications own run, reliability, security (including the live MDR / cybersecurity-audit

decisions) and platform health of the S/4HANA, Salesforce, e-commerce and Digiserve

estate. Agree one shared roadmap and clear decision rights so that change lands on stable

ground.

Give peers a bigger game: AI-ready data, integrations and platforms that they build and

own, while this role stays accountable for the business outcome.

Leadership & governance

Build a lean, senior core team (product, AI, automation and analytics) and a network of

digital champions in the business — drawing first on capability Vashi already has, and

adding only the few hires the agenda needs.

Hold vendors and partners to outcomes, not effort.

Translate technology into the language of the board, the P&L and the equity story — and

back into priorities the floor can execute.

What success looks like in the first 12 months

A clear, costed transformation and AI roadmap tied to TAT, customer experience and gross

profit — built with the technology peer group and agreed with the CEO inside the first 90

days, with a baseline and a target for every priority workflow.

At least two core workflows measurably faster and cheaper, with the change adopted and

sticking, not just demonstrated.

AI in live production use in more than one function, with adoption and impact that can be

measured and that Finance trusts.

At least one data science model — for example demand, pricing, credit or replenishment —

in live use and changing a real decision, with value measured in margin, working capital or

TAT.

At least one customer-facing journey visibly faster or simpler, with evidence from

customers or the LGP network.

A working model with the Heads of Infrastructure, Platforms and Applications in which

decision rights are clear and disagreements are resolved without escalation.

AI guardrails and responsible-use rules approved and operating, so that adoption never

creates a risk the board or auditors have to explain.

Who we are looking for

The right person is a hands-on, business-first transformation leader who has taken AI and digital

change into the daily work of a real, physical, margin-sensitive business — not a programme

manager who governs other people’s delivery, and not a pure technologist or data scientist with

no feel for how a quote or a credit decision is actually made.

Track record: Has personally driven workflow, cycle-time and customer-experience

transformation in a customer-facing, product-based, service-led business — and can point

to time taken out and money saved.

AI and data native: Has shipped AI and data science models into production and changed

how work is done, with real users and measured adoption. Hands-on enough to tell a

working capability from a demo, and a vendor claim from a result.

Commercial: Speaks gross profit, working capital, cost-to-serve, ROI and IPO readiness

fluently. Technology is a means to a commercial end, never the end itself.

Frugal: Frugal by instinct — builds for return, not for headcount or prestige. Has proved

value with a lean team before asking to scale.

Influencer and multiplier: Gets change adopted through peers and business heads who

do not report to them, and makes the people around them more capable — not a hero who

hoards the work.

Instills trust: High ownership; does what they say; surfaces bad news early. This is

Vashi’s most important competency.

Profile specifications

The background we expect, so that candidates and recruiters can screen quickly:

Specification What we expect

Level & experience Vice President (L5). 15+ years of experience, including at least 5

years leading AI, data science or digital transformation with

measured business outcomes.

Industry A customer-facing, product-based, service-led business — one that

sells products and wins on speed, availability and service quality

(for example B2B distribution, industrial and engineering products,

automotive and aftermarket, or retail and e-commerce). Puresoftware,

IT-services and back-office IT backgrounds are not a fit

on their own.

Organisation size Has worked in an organization with ₹ 6000 Cr – 8000 Cr+ annual

turnover and 3,000+ people, operating across multiple locations

or regions. Larger groups are welcome where the candidate has

worked lean.

Budget managed Has owned — not just influenced — an annual technology, digital

or AI budget in crores, accountable for return, vendor negotiation

and build-vs-buy decisions.

Team managed Has led a multi-disciplinary team of roughly 20 – 25 people (direct

and indirect, across data science, engineering, product and

analytics). At Vashi the core team will start lean.

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