EI Readiness Advisory
EI Readiness Advisory measures AI readiness across strategy, data, people, governance, value and scale. Built in partnership with the MIT Media Lab, the program puts business, technology and operational leadership in one room and challenges the assumptions behind your current AI plan.
Most consulting confirms the direction you have already chosen. This tests it. You leave knowing which opportunities to fund, what to fix first, and how pilots become enterprise capability.
You Don't Have an AI Shortage. You Have a Prioritisation Problem.
Most enterprises have already spent on AI. Pilots are running, agentic experiments are live, the use-case backlog is long and the transformation ambition is on record. What is missing is the ability to prioritise, govern, scale and prove value.
The hard part was never adopting AI. It is turning AI activity into Engineering Intelligence at scale.
Five Questions That Stall Enterprise AI
Which opportunities deserve the next dollar?
A long backlog is not a portfolio. Without a shared basis for value, feasibility and scalability, prioritisation defaults to whoever argues loudest.
Will teams trust it enough to use it?
Adoption fails on confidence and workflow fit long before it fails on model performance.
Can a pilot become a capability?
A result that cannot be repeated across sites, products and teams is a demonstration, not an asset.
Is the data foundation actually ready?
Pilots survive on curated datasets. Enterprise scale does not.
Is there governance that holds at scale?
Validation, accountability and control that work for one use case rarely work for forty.
Unanswered, these questions do not stop AI spending. They only stop it from compounding value.
A Consulting Engagement, Not an Assessment Survey
EI Readiness Advisory brings business, technology and operational leadership into one room and works through the decisions that precede investment: where value is real, what is blocking it, and what the enterprise must change to scale.
The advisory was developed with Prof. Hossein Rahnama, head of the sAIpien program at the MIT Media Lab, combining research-backed AI readiness principles with LTTS' engineering, manufacturing and digital transformation practice. LTTS is a member of the MIT Media Lab consortium.
What that produces is a framework you can act on, not a maturity score you file away.
Tell Us What You’re Looking For in EI Readiness Advisory
Handpicked for You
Different chairs, different questions
Every organisation starts somewhere different. The questions leadership needs answered depend on where value has to land.
Product Engineering
Where can EI create product advantage?
The questions you leave answered:
- Where does Engineering Intelligence create sustainable product advantage?
- Which opportunities are worth prioritising and scaling?
- Are our engineering processes, teams and data ready?
- How do we build trust, governance and validation into AI-enabled products?
- Which capabilities must we strengthen to realise value faster?
Manufacturing & Operations
Where can EI create operational value?
The questions you leave answered:
- Where does Engineering Intelligence create the greatest operational value?
- Are our plants, processes and data foundations ready for scale?
- Can successful pilots be replicated across sites and functions?
- What is actually preventing wider adoption?
- What is our repeatable path from experimentation to enterprise impact?
What You Walk Away With
Four deliverables, all of them usable in the next budget conversation.
Leadership Alignment
One shared view of priorities, opportunities and readiness across business, technology and operations.
Readiness Insight
Clear visibility into where the organisation is strong, where it is exposed, and what is blocking scale.
A Prioritised Portfolio
A focused set of high-value opportunities tied to business outcomes, and an explicit list of what waits.
An Actionable Roadmap
Practical recommendations, sequence and next-step actions to accelerate adoption.
You will not leave with another AI strategy document. You will leave knowing where to focus, what must change, and how fast you can move.
Where It Has Been Applied
Across enterprise, product engineering and manufacturing environments.
The situation
16 business units were pursuing AI independently, each with different priorities, different maturity and no common view of value. We assessed readiness across business, technology and workforce, built one leadership framework for evaluating opportunities, and facilitated prioritisation across the group.
What changed
One enterprise view of AI investment and roadmap decisions
The situation
A strong executive mandate for AI, but no clarity on where to focus or which opportunities would actually scale. We evaluated readiness across product, data, governance and business dimensions, and challenged the assumptions behind value realisation.
What changed
Leadership aligned on priority areas, ownership and execution paths
The situation
Multiple AI initiatives already running across operations, supply chain and manufacturing, with no shared view of readiness or scale priorities. We assessed six readiness dimensions, identified the barriers to adoption, and built the enterprise roadmap.
What changed
A structured path from experimentation to scalable deployment
An enterprise AI readiness assessment evaluates whether an organisation can move AI from isolated pilots to enterprise-wide capability. It examines strategy, data foundations, workforce capability, governance, value realisation and scalability, then identifies the gaps that block adoption. Unlike a technology audit, its purpose is decision support: establishing which AI opportunities are worth investment and what must change before that investment pays back.
A maturity assessment scores where you are. EI Readiness Advisory decides what you do next. The difference sits in the Challenge stage, where LTTS deliberately tests the assumptions and blind spots leadership has stopped questioning rather than confirming them. It also concludes with a prioritised opportunity portfolio and a roadmap with named owners, not a maturity level. The advisory was developed by LTTS in partnership with Prof. Hossein Rahnama, head of the sAIpien program at the MIT Media Lab.
Four. Leadership alignment, meaning one shared view of priorities, opportunities and readiness across business, technology and operations. Readiness insight covering organisational strengths, exposures and the specific barriers to scale. A prioritised opportunity portfolio tied to business outcomes. And an actionable roadmap of recommendations, sequence and next steps. All four are built to be used directly in the next budget conversation.
A pilot that cannot be repeated across sites, products and teams is a demonstration, not an asset. The assessment identifies exactly what prevents repetition: data foundations that only held for a curated set, governance built for one use case, workflow and trust gaps that suppress adoption, and unclear ownership. The Scale stage then puts those foundations in place, so the next deployment starts from enterprise capability rather than from scratch.
LTTS assesses readiness across six dimensions: strategy, data, people, governance, value and scale. Within each, the advisory examines whether opportunities are prioritised on a shared basis, whether data foundations hold beyond curated pilot datasets, whether teams will trust and adopt AI in their workflows, whether validation and accountability work across many use cases rather than one, and whether results can be repeated across sites, products and teams.
Opportunities are ranked on three criteria applied consistently: business value, technical feasibility and scalability potential. This replaces the common default, where prioritisation follows whoever argues loudest or whichever function moved first. The exercise runs with business, technology and operational leaders in the same room, so the output is a portfolio the enterprise has already agreed on, including an explicit list of what will not be funded yet.
Yes. The advisory runs through two lenses. The product engineering lens addresses where Engineering Intelligence creates sustainable product advantage, whether engineering processes, teams and data are ready, and how to build trust, governance and validation into AI-enabled products. The manufacturing and operations lens addresses where the greatest operational value sits, whether plants, processes and data foundations are ready to scale, and whether pilots can be replicated across sites and functions.
Business, technology and operational leadership together. The advisory produces a single shared view across those three groups, which is only achievable if all three are in the room. Depending on the lens, that typically means product engineering leaders or manufacturing and operations leaders alongside data, IT and transformation ownership.