START
Choose the first AI investment worth funding.
For organizations under board pressure to begin, but without a defensible priority, control path or 90-day plan.
WHAT'S THE AI PROBLEM?
By The Numbers: AI Failure
01 · Pilot outcomes
95%
of generative AI pilots deliver no P&L impact.
MIT. Spend converts into activity and demos rather than measurable earnings.
02 · Project failure rate
80%+
of AI projects fail — twice the rate of standard IT.
RAND Corporation. The gap is execution and decision discipline, not model capability.
03 · Data readiness
85%
of AI models fail on poor or missing data.
Gartner. The binding constraint sits upstream of the model, in access, quality and coverage.
04 · Wasted spend
26%
of AI spend produces no measurable return.
At $1M per month — the reality for one in five organizations — that is $260,000 lost every month.
05 · Strategy vacuum
15%
of employees see a clear AI strategy.
Gallup. Adoption stalls where direction, ownership and priorities are never communicated.
06 · Talent gap
55%
of leaders doubt they can staff AI roles.
Microsoft and LinkedIn. Up to 70% of employees need upskilling, yet 74% of organizations expect to run AI agents by 2027.
THESE ARE NOT TECHNOLOGY FAILURES. They are failures of decision, evidence, data readiness, ownership and adoption — each one diagnosable before the next dollar is committed.
Why leaders call us now.
01 · Board pressure
“The board wants a defensible AI answer.”
Leadership needs a fact-based strategy, investment logic, and accountable next step.
02 · Budget or renewal
“We are paying for AI, but cannot show the value.”
A license, vendor, or budget decision is approaching.
04 · Fragmented portfolio
“Every function is buying and building separately.”
Tools, use cases, data practices, and controls are duplicative or inconsistent.
05 · Uncontrolled use
“Employees are already using AI outside approved controls.”
Leadership lacks an inventory, ownership model, or proportionate guardrails.
03 · Stalled pilot
“The demo worked; the operating workflow did not.”
Quality, integration, ownership, adoption, or economics remain unresolved.
06 · Scaling problem
“One team has a win; we cannot repeat it.”
A promising use case exists, but the organization cannot govern or scale it.
AIRA Audit
The AIRA Process
From AI questions to an actionable plan in four weeks.
WEEK 1
Align
Frame the Business Decision
We establish what matters before evaluating technology.
- Define the business problems and opportunities
- Establish scope, objectives and success measures
- Identify decision-makers and accountable owners
- Inventory existing AI initiatives, tools and experimentation
- Align on the decisions AIRA needs to inform
Deliverable · AIRA Assessment Charter
WEEK 2
Assess
Understand Your Current State
We examine how the business runs and what AI requires.
- Review priority workflows and processes
- Assess relevant data, systems and technology
- Evaluate existing AI tools, pilots and usage
- Identify organizational, data and capability gaps
- Develop an evidence-based map of potential AI opportunities
Deliverable · Readiness & Opportunity Map
WEEK 3
Evaluate
Determine What Is Worth Pursuing
Initiatives are weighed on value, feasibility, readiness and risk.
- Evaluate expected economics and business impact
- Assess data and technical feasibility
- Identify implementation, governance and operational risks
- Classify each initiative: Proceed, Prepare, Repair, Stop
Deliverable · AI Opportunity Portfolio
WEEK 4
Mobilize
Turn the Findings Into Action
We convert the assessment into a prioritized, executable plan.
- Finalize leadership recommendations
- Prioritize initiatives based on value, feasibility and readiness
- Define ownership, controls and success metrics
- Identify immediate capability and data requirements
- Establish near-, medium- and longer-term priorities
- Build a practical 30/60/90-day action plan
Deliverable · Executive AI Roadmap
What you receive.
Illustrative
Executive decision page
The one-page answer leadership can use.
Illustrative
AI portfolio table
What to fund, repair, scale, hold or stop.
Illustrative
Use-case decision card
The evidence and assumptions behind each recommendation.
Illustrative
Evidence-test summary
A technical verdict tied to pre-agreed thresholds.
Illustrative
Risk & remediation register
The conditions required before launch or scale.
Illustrative
90-day roadmap
Named owners, milestones, dependencies and decision gates.