What does an AI strategy engagement actually include?

At L+R it's three things, in order. A scored list of candidates, covering LLMs and agentic workflows, classic machine learning, and plain automation where no model is needed at all. A prototype or spike for each survivor, enough to show how it fails. Then the roadmap: model and build-vs-buy recommendations, where human-in-the-loop review and governance sit, and an owner and dependencies for every step. Want to see it on your own shortlist? Ask us.

How do we know which AI use cases are worth pursuing?

Score them before you build them. Value, data readiness, and technical feasibility are three separate questions. Most shortlists fall apart on the second: the use case is worth having, but the data isn't there yet. The roadmap starts with what's valuable and buildable today. Some of the best scorers point inward, at your own team's workflows, and those get built as internal tooling and workflow AI.

Do we need an AI strategy first, or can we start building?

Not always. If the decision's made and the engineering is the hard part, skip the roadmap and go straight to AI and machine learning integration. Strategy earns its keep when the list of candidates is still open and nobody agrees on what comes first.

AI Strategy

AI Strategy Consulting: the roadmap before the build

AI programs tend to stall somewhere between the pilot and the shipped feature. Strategy settles which use cases get funded, which get killed, and what your data can carry.

Digital network lights across a planet-like horizon

About this service

We help product and operations teams decide where AI belongs. We score the candidate use cases, test them against your data and systems, and sequence the roadmap: build it with us or take it in-house.

Look Through The Work That Shapes Our Advice On AI.

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SATYA S:9

Designing and engineering an immersive wellness device and product system from the ground up

Curved wellness pod and standing person in a bright room with city views.

Ironic Reports

Helping wedding planners anticipate microclimate risk with AI-powered intelligence

Outdoor wedding ceremony with a translucent weather forecast overlay.

ONYX

Scaling brand creation with an identity system built for agentic workflows

Neural Magic

Positioning high-performance AI infrastructure with a clearer digital brand system

Glowing digital cubes connect across a dark network grid.

Alexander

Creating a premium storytelling platform for synchronized digital experiences

Illustration of workers sorting sea creatures on a conveyor near icebergs

Gitcoin Grants

Making public-goods funding easier to discover, understand, and support

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Teams We've Advised On AI

Each of these teams worked through its AI decisions with us inside a longer product engagement.

Sectors We've Advised On AI

AI gets judged differently by sector. Regulated ones ask about governance and audit trails first; startups about speed to ship.

What An AI Strategy Engagement Includes

Learn more

L+R scores your use cases with the teams who'd own them, prototypes the survivors, and sequences the roadmap.

Common Questions About AI Strategy Consulting

Scoping, sequencing, and what you walk away with.

At L+R it's three things, in order. A scored list of candidates, covering LLMs and agentic workflows, classic machine learning, and plain automation where no model is needed at all. A prototype or spike for each survivor, enough to show how it fails. Then the roadmap: model and build-vs-buy recommendations, where human-in-the-loop review and governance sit, and an owner and dependencies for every step. Want to see it on your own shortlist? Ask us.
Score them before you build them. Value, data readiness, and technical feasibility are three separate questions. Most shortlists fall apart on the second: the use case is worth having, but the data isn't there yet. The roadmap starts with what's valuable and buildable today. Some of the best scorers point inward, at your own team's workflows, and those get built as internal tooling and workflow AI.
Not always. If the decision's made and the engineering is the hard part, skip the roadmap and go straight to AI and machine learning integration. Strategy earns its keep when the list of candidates is still open and nobody agrees on what comes first.