Digital Transformation Advisory
Every digital transformation we've seen fail in the last five years has failed for the same reason: the AI tool was bought before the data was clean, or the automation was built before the process was understood. We start somewhere different.
What we hear from operators
The problems we solve
The roadmap exists. Nothing is getting built.
Most organisations have a digital transformation strategy document — produced by a big four firm, blessed by the board, filed somewhere on SharePoint. It identifies the right priorities. It doesn't explain how to sequence them, what the first 90-day deliverable is, or who owns what. Two years later, the strategy is still the strategy. A few pilots have happened. The transformation hasn't.
AI was deployed before the data was ready
Generative AI tools, predictive models, and automated decision systems all share the same dependency: clean, connected, current data. When organisations deploy AI on top of fragmented, unreliable data, they get AI that produces confidently wrong outputs. The failure of the AI gets attributed to the technology. It's a data problem. The AI just made it visible.
Technology is being selected before the problem is defined
Vendor demos create technology pull. A Databricks demo impresses the CDO. A Salesforce pitch impresses the CCO. Technology decisions get made based on capability demonstrations, not on a clear definition of the problem being solved, the alternative options evaluated, or the total cost of ownership over three years. The result is a technology portfolio that doesn't fit together and doesn't match the organisation's maturity.
Who this is for
Who digital transformation advisory is built for
The roles that feel the problem first — and what we build for each of them.
CEO / General Manager
The problem
You have a strategy document from a big-four firm, blessed by the board and filed on SharePoint. Two years on, a few pilots have happened and the transformation has not.
What we build
A phased roadmap where the first phase produces a live deliverable — a Power BI dashboard or a Power Platform automation — inside 90 days.
First working output in 6 weeks
COO / Operations Director
The problem
AI was deployed on fragmented, unreliable data and produced confidently wrong outputs — and the technology took the blame for what was really a data problem.
What we build
A data foundation on Microsoft Fabric built before the AI and automation layer, so the intelligence sits on data it can trust.
Data foundation first, AI second
CIO
The problem
Technology is being selected from vendor demos rather than a defined problem, leaving a portfolio that does not fit together or match the organisation's maturity.
What we build
Requirements-led selection across Microsoft Fabric, Power Platform and Azure OpenAI, documented against defined needs with three-year TCO.
Technology decisions requirements-driven, not demo-driven
CFO / Financial Controller
The problem
Technology decisions are made without a three-year total cost of ownership or a business case the board can defend after the money is spent.
What we build
A TCO-modelled roadmap with alternatives evaluated, and Power BI to track delivery against the plan each quarter.
Every technology decision defensible on TCO
Measurable outcomes
What changes after implementation
Specific shifts from delivered digital transformation advisory work — the before, and the after.
From strategy to first live deliverable: 90 days or less
Every engagement produces something tangible within the first quarter. A working dashboard, an automated process, a data foundation — not a further planning document.
Technology decisions: demo-driven → requirements-driven with TCO analysis
Technology selection documented against defined requirements. Total cost of ownership modelled over 3 years. Alternatives evaluated. The decision is defensible to the board and to the team that has to live with it.
Roadmap adoption: shelf document → actively managed quarterly review
The roadmap is reviewed quarterly against delivery, adjusted for what has changed, and remains a live working document rather than a historical record of good intentions.
By market
Digital Transformation — market-specific pages
Each page below covers what digital transformation advisory looks like specifically in that market — the local ERP landscape, compliance context, and the operational patterns we actually see there.
Singapore & Malaysia
United Kingdom
North America
By industry
Digital Transformation — industry-specific pages
How digital transformation advisory applies to the specific systems, metrics, and operational challenges of each vertical.
Manufacturing
Most manufacturing plants we walk into have four or five systems that don't talk to each other: SAP or Oracle for production orders, a separate MES for floor execution, a quality system that's often standalone, and spreadsheets filling every gap in between.
Explore →
EPC (Engineering, Procurement & Construction)
EPC data environments are uniquely complex: long project timelines, multi-currency and multi-jurisdiction reporting, complex contractual structures (lump sum, reimbursable, target cost), and a fundamental tension between the project management system (Primavera, MS Project) and the cost management system (Oracle, SAP PS).
Explore →
Technology stack
Common questions
Digital Transformation — frequently asked
We've done a digital transformation before and it didn't deliver. Why would this be different?
Most digital transformation failures have one of three root causes: the data foundation wasn't built before the AI/automation was deployed; the technology was selected before the problem was defined; or the organisation didn't have a named owner with budget authority and accountability. We address all three explicitly — maturity assessment to establish the foundation, requirements-led technology selection, and a governance model with a named transformation owner before the engagement begins.
We're a mid-market company, not an enterprise. Is this relevant to us?
Digital transformation in mid-market manufacturing and FMCG is where most of the real opportunity sits. Enterprise-scale organisations have armies of IT staff and consultants. Mid-market companies have a 10-person IT team, a legacy ERP, and a board that has just approved a digital budget for the first time. The playbook is different — faster decisions, less governance overhead, higher tolerance for pragmatic solutions. That's the environment we're designed for.
How do you handle change management? Technology is only part of it.
Change management in data and analytics transformations is almost always about trust. Operations managers who've been running on their own data — their own Excel, their own reports — need to see the new system produce a number they recognise before they trust it. We design the rollout to create early wins with the people who are most sceptical, because if they adopt it, everyone else follows.
What's the Fractional Data Consultant model and is it relevant here?
The Fractional Data Consultant model is the engagement structure where Amit embeds as your senior data leader — setting the strategy, owning the roadmap, managing the vendors, and acting as the CDO you need but aren't ready to hire full-time. It's relevant if you've identified a data transformation need but don't have a senior data leader internally to own it. It's typically a 2–3 day per week commitment, structured to match your pace of change.
Related FAQs
Go deeper on the questions buyers ask
Start with a conversation, not a proposal
First call is 30 minutes with Amit. We ask about your systems, your team, and your most pressing operational problem. You get a clear view of where the gap is and what closing it looks like. No slides. No pitch deck. No obligation to proceed.