AI Readiness and Value Assessment for an Industrial Manufacturing Giant in the USA

Summary:

From 64 scattered automation ideas hovering around in a manufacturing company in the USA to a board-approved, $2M-a-year AI roadmap in 8 weeks with DataToBiz’s pod of 6 senior experts.

Our Client:

• A leading industrial manufacturer and distributor headquartered in the US Midwest.
• With an annual revenue of roughly $1.4 billion and a workforce of 4,800 employees, the company operates across 12 plants and 4 distribution centers in the US.

Problem Statement:

• Lack of Agenda: After major acquisitions, the firm ran two ERP systems, three planning tools, and a patchwork of spreadsheets. Internal departments launched AI pilots on their own, while the board asked the stakeholders one simple question they could not answer: “Where will AI actually pay off for us, and what will it cost?”
• Crucial Ask: They required an independent, evidence-based view of an AI readiness system for their enterprise that ends in decisions around: who to fund first, what to fix first, and what to stop.

Our Solution:

After the final discussion with their team, DataToBiz proposed a five-gated phase plan where the complete maturity assessment was compressed into 8 weeks.

Weekly plan: Every phase in DataToBiz’s methodology ended with their decision. Assessing, Prioritising, and Architecting were combined into one fixed-fee, total 8-week assessment.

Development phases: This phase-wise process gave our leadership a funded roadmap rather than just a maturity score. Delivery and scale are then funded via the roadmap itself, allowing the client to move to execution faster.

The pod: Principal AI consultant, transformation consultant, business analyst, enterprise data architect, AI solution architect and value consultant. One senior-led team from strategy through to architecture.

What we Discovered:

Ambition was ahead of the foundations: Overall AI maturity scored 22.2 out of 5 against a peer median of 2.7 for US industrial or manufacturing companies of similar size that came to us. The gap was not ideas: use-case value already scored above peers. The gap was data, governance and the operating model needed to turn ideas into production.

Data readiness heatmap, by domain

Executive findings:

• Procurement, AP and finance data were ready enough to act on now. Product master and plant data were not, and that shaped the whole roadmap: back-office quick wins first, a master-data fix in parallel, and the big plant-floor prizes once the data could support them.
• The money was leaking in the back office. Off-contract spend and manual invoice matching were the largest near-term value pools, and the data was already usable.
• The big prizes needed a data fix first. Demand forecasting and predictive maintenance could not scale without one product master and connected plant data.
• Risk was already present. Unmanaged GenAI use made governance a week-one action, not a year-two nice-to-have.

Discussed Priorities:

Every use case earned its place on a transparent 2×2. Weights were agreed with the steering group before any idea was scored, which took politics out of the room. Every use case was scored on the same five criteria, sized, and placed on the value/feasibility grid.

Ideas Chart

The prioritised portfolio in a Glimpse:

Foundation and Technical Architecture:

One cloud-agnostic blueprint for every use case on the roadmap

Rather than a new stack per pilot, the team designed a single target state on the client’s existing cloud tenancy, with reference patterns for GenAI, RAG and agents. Each key choice was captured as an architecture decision record so the board could see what was decided and why.

Responsible AI: controls proportional to risk

A policy and a three-tier risk model gave the stakeholders a fast lane for low-risk use cases and clear controls for the rest.

Target operating model:

An AI Centre of Excellence owns standards, the shared platform, intake and value tracking. Each business unit keeps a named owner per use case.

A RACI sets decision rights, and a quarterly funding and intake cycle run by the CoE decides what enters the portfolio next.

Roadmap & Business Case:

The plant’s technical team and dedicated pod from DataToBiz together executed a 24-month plan in three waves, with a decision gate every six months.

Each wave captured near-term value while building the foundations for the next. The biggest value pools were addressed only once the underlying data was fit for purpose, with each six-month gate determining what moved forward in the following wave.

• Decision gate (G1–G4): value review, then release of the next tranche of funding.

CFO-ready business case in a glimpse:

Value tracking process:

1. Baseline KPIs: Measured in the assessment and signed off by finance.
2. Business-case targets: Benefit, TCO, and a named owner for every use case.
3. Benefit tracking: Monthly, on a shared value dashboard.
4. Realised-value review: At each gate, before the next wave is funded.

Business Impact and Outcomes

What started as process to determine readiness score for AI implementation. The entire project actually resulted in ten decision-ready outputs.

10 Decision Outputs in a Glimpse:

Delivery Process, Post Decisions:

Why did the solution work?

For the manufacturing firm, the approach worked because:

• Every phase ended with a decision gate that the client signed off on, keeping the engagement focused on decisions rather than a score.
• The evaluation weights were agreed upfront, and transparent scoring made the resulting portfolio defensible to the board and CFO.
• The same architects who designed the roadmap can carry it into delivery, working alongside the manufacturing team as priorities move from planning to implementation.

*Client name and identifying details withheld. Engagement figures are representative and rounded; value estimates are modelled at the assessment stage.