Leadership issues drive 84% of AI failures. When success metrics are defined up front, success rates jump from 12% to 54%. Ownership is the variable, not the model.
RAND
We bring what's actually working at the frontier. Patterns from Claude and OpenAI's own deployments. Playbooks refined inside MBB and the Big Four. What leading enterprises are quietly putting into production.
Most AI advisors pull from one of those worlds. We sit at the intersection: deep implementation fluency, workflow literacy across regulated industries, and a live view across the firms setting the pace.
Leadership issues drive 84% of AI failures. When success metrics are defined up front, success rates jump from 12% to 54%. Ownership is the variable, not the model.
RANDAI is now the hardest skill in the world to hire for. 72% of employers cannot find the people they need, and AI salaries pay 67% more than traditional software roles.
ManpowerGroupEmployee confidence in their company's AI strategy fell from 47% to 31% in a single year. Structured upskilling delivers 3 to 4x faster adoption and 67% higher ROI than self-directed learning.
DataCamp83% of GenAI pilots never reach production. Strategy houses hand over a roadmap, engineering shops hand over a pilot, and the workflow redesign that actually moves the numbers is left unowned.
MIT / FortuneExperience building with
We anchor in the highest-value workflows inside each function, then layer industry expertise on top so the redesign lands in the language and constraints of the work. Function depth is the engine; industry fluency is what makes the engine relevant.
FP&A modelling, close cycles, controls and audit-grade trail. Where structured data already lives and ROI is easiest to anchor.
Sourcing cycles, supplier qualification, contract negotiation, and category spend analysis where clause history and supplier performance data compound.
Pipeline qualification, deal-desk operations, account research, and the long tail of seller admin that quietly eats selling time.
Campaign production, content operations, brief-to-asset cycles, and attribution work that compounds when the inputs are clean.
Talent operations, performance cycles, internal mobility, and policy work where structure beats sentiment.
Contract review, matter intake, regulatory disclosure, and the privilege-aware document work that sits inside in-house counsel.
We do not scale an engagement until a real workflow is landing. The first step is a conversation; nothing further commits without proof.
We sit down to see how your organisation uses AI today and compare it to what we're seeing at peer firms in your industry. Real survey data, plus what we hear from operators in the field.
Stakeholder interviews and process surveys give us a ranked list of candidate workflows, each scored on business impact, data readiness, and risk. We then redesign the top candidate at the activity level with the people who run the work today, and where it warrants software, our engineering squad ships it. The first redesign has to deliver, capturing data, risk, and control requirements at every step.
With proof in hand, we scale across the organisation. Role-based curricula, hands-on labs, and a coaching cadence keep capability alive long after the first workflow ships, while a portfolio of redesigns moves into delivery. AI fatigue is real. Novelty alone doesn't drive returns.
If your team has clear bottlenecks, repetitive analytical work, or data sitting unused in systems no one queries, you have enough surface area to start. The first conversation is about which workflow is worth redesigning and which ones should be left alone.
Most stalled programs share two failure modes. The use case was picked because it was visible, not because it was high-value. And the prototype was never adopted into the daily run of the work. We pick fewer use cases, score each against a real economic case, and stay until the redesigned workflow is the workflow.
Yes. Most of the workflows we redesign run on the data you already have, in the systems you already own. We design around current constraints and own the technical lift so your team stays focused on the function it was hired for.
Less than most engagements. We need access to the people who do the work, an honest read of what the workflow looks like today, and a decision-maker who can sign off when the redesign is ready. From there we do the build.
Working pilots in weeks, not quarters. We compress the early loop so the team can see the redesigned workflow running on real inputs before any broader rollout.
We stay through adoption. That means training the people who will use the workflow, monitoring how it behaves on live data, and tuning the system until it runs cleanly without us in the room.
We are workflow specialists first, AI specialists second. We start by understanding what the work actually looks like, then propose where AI earns its place. We are tool-agnostic and industry-agnostic, and we measure ourselves against whether the redesigned workflow holds up six months in.
Thirty minutes on understanding your immediate pain points and how AI can fit in your process and where it does not. If there is a reason to keep going, we will talk about next steps.