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Turning AI investment into measurable business value
Over two decades of enterprise AI leadership, focused on one outcome. We deliver solutions to real business problems, with governance and scalability built in from day one.
AI consultancy at AcuteAI has three parts. They work on their own or together, and each one connects to a studio of about 60 engineers that can then design, build and ship what the plan calls for.
AI Consultancy
Strategy & Assessment
Define your AI strategy, assess readiness and data maturity, then prioritize use cases by ROI, risk and feasibility.
What it covers
- Define an AI strategy tied to business goals.
- Assess readiness, including the maturity of your data.
- Prioritize use cases by ROI, risk and feasibility so the first projects are the right ones.
Questions it answers
- Where can AI create value in our business, and where can't it?
- Is our data ready for the use cases we have in mind?
- Which use case should come first, and why?
AI Consultancy
AI Roadmap
Sequence quick wins and long-term initiatives into a phased plan with clear milestones, owners and KPIs.
What it covers
- Sequence quick wins and long-term initiatives into one phased plan.
- Set clear milestones so progress can be seen and checked.
- Name an owner and KPIs for each initiative.
Questions it answers
- What do we do in the first 90 days, and what comes later?
- Who is accountable for each initiative?
- How will we know it is working?
AI Consultancy
Responsible AI
Embed governance, risk controls and ethical guardrails so AI stays transparent, compliant and trustworthy at scale.
What it covers
- Embed governance so decisions about AI have clear owners.
- Put risk controls and ethical guardrails in place early, not after launch.
- Keep AI transparent and compliant as it scales.
Questions it answers
- What rules and controls should apply to this AI system?
- How do we show that it is transparent and compliant?
- What happens when something goes wrong?
A public reference for responsible AI: the NIST AI Risk Management Framework
The US National Institute of Standards and Technology published version 1.0 of its AI Risk Management Framework in January 2023. It is voluntary guidance, not a law or a certification, and it is widely used as a common language for managing AI risk. It organizes the work into four functions:
- Govern. Set accountability, policy and risk tolerance.
- Map. Document each AI system, its context and what could go wrong.
- Measure. Decide how you will tell whether a system is working or drifting.
- Manage. Plan the response when it isn't, including incident handling and the ability to pause the system.
Source: Kognitos, NIST AI Risk Management Framework explained. Listed here as general background. It does not mean AcuteAI projects are certified against it.
How the three parts answer why AI projects fail
Gartner cites three reasons agentic AI projects get canceled. Each one has a matching part of the consultancy.
| Why projects get canceled | What goes wrong | Where we start |
|---|---|---|
| Escalating costs | Production costs far exceed the pilot budget. | Strategy & Assessment to prioritize by ROI, risk and feasibility, and the AI Roadmap to phase the spend. |
| Unclear business value | Returns aren't tied to a measurable business outcome. | AI Roadmap to set milestones, owners and KPIs from the start. |
| Inadequate risk controls | Security and governance arrive too late. | Responsible AI to embed governance and guardrails from day one. |
Plan your AI work before you build it.
Start with an assessment, a roadmap or a governance review.