AI Innovation Consulting

Engineers solve problems, consultants sell their services. AI consulting bridges the gap — helping organizations build prediction models that compound.

The Opportunity

78% of organizations are adopting AI, but most are stuck in pilot purgatory. The gap isn't technology — it's tribal imbalance and missing systems.

What organizations say: "We need AI strategy." What they mean: "We have pilots everywhere and nothing is shipping."

Four-Layer Playbook

AI consulting has four layers: business alignment, readiness, use cases, and delivery/governance.

Layer 1: Business Context & Alignment

Anchor every discussion in business value, not technology.

Area	Key Questions
Objectives	What 2-3 business metrics should AI move first?
Scope	Which units/processes are in-scope in the next 90 days?
Sponsor	Who signs off, who blocks, who operates day-to-day?
Constraints	What data/regs/brand rules can't be violated?
Success	What proves this was worth it within a quarter?

The real question: "What would make this engagement a no-brainer in 12-18 months?"

Layer 2: AI Readiness Diagnostic

Run this in a 2-4 week diagnostic phase.

Dimension	What to Assess
Strategy & Leadership	Named AI owner? Documented vision linked to budget? Clear principles on where AI will/won't be used?
Data Foundation	Accessible, permissioned data? Governance policies? Sensitivity classification?
Technology & Architecture	Approved platforms (Copilot, Workspace, etc.)? API/integration patterns? MLOps capability?
Governance & Risk	AI risk policy? High-risk review process? Output monitoring for bias/hallucinations?
People & Skills	Internal AI training? Champions in teams? Capacity in IT/data without derailing ops?

Layer 3: Use Case Prioritization

Once readiness is clear, design and prioritize use cases.

Discovery questions per function:

• Where is there high manual load, delays, backlogs, or error rates visible in metrics today?
• What patterns are repeatable across clients (to build reusable "blueprints")?

Prioritization criteria:

Criterion	Question
Business Impact	Quantifiable upside in time, cost, revenue, or risk reduction?
Feasibility	Data availability, technical complexity, dependencies, security constraints?
Time-to-Value	Can we show a result inside 4-12 weeks with a scoped pilot?
Adoption Likelihood	Clear owner, motivated team, workflow people want to improve?

Use case one-pager (per candidate):

• Problem statement and current baseline metric
• Users, systems touched, in-/out-of-scope boundaries
• Data sources and sensitivity class
• Proposed AI pattern (assistant, classifier, summariser, generator, recommender, agent)
• Risks, guardrails, and human-in-the-loop checkpoints

Layer 4: Delivery & Governance

Engagement structure:

Phase	Duration	Purpose
Phase 0 - Diagnostic	2-4 weeks	Readiness checklist, discover/prioritize use cases, strategy + roadmap
Phase 1 - Pilots	6-12 weeks	Implement 1-3 high-ROI use cases with clear metrics and governance
Phase 2 - Scale	Ongoing	Expand successful pilots, build reusable agents, train teams, formalize operating model

Pilot delivery checklist:

• Design: Detailed flow, UX, success metrics, policies, acceptance criteria
• Build: Use existing platforms first, then custom agents where needed
• Govern: Risk review, data protection, logging, monitoring, feedback loop, rollback plan
• Enable: Docs, playbooks, training, internal "AI worker manual" for operators
• Review: KPI dashboard, qualitative feedback, go/no-go decision for scale

The Three Tribes

AI transformations fail when tribes are unbalanced.

Tribe	Question They Ask	What They Provide
Explorers	"What if we tried...?"	Discover options, frontier awareness
Automators	"How do we operationalize this?"	Scale validated ideas, integrate systems
Validators	"How do we ensure quality and safety?"	Standards, compliance, trust

The failure pattern: Explorers generate pilots → Automators can't operationalize → Validators block everything → Pilot purgatory.

The solution: Tight fives that pass through all three tribes before shipping.

Pricing Model

Tier	Deliverable	Price Range
Diagnostic	Readiness baseline + prioritized use case roadmap	$5K-15K
Pilot	1-3 implemented use cases with metrics and governance	$15K-50K
Managed Service	Ongoing AI worker operations + optimization	$3K-10K/month

Growth Strategy

1. Focus on customer problems first — solve real problems, not push technology
2. Provide free value — podcasts, speaking, content that generates inbound
3. Leverage partner networks — build relationships with platforms that need implementation help
4. Strategic speaking — choose events where target audience is present
5. Paid discovery offers — tiered packages that qualify leads and demonstrate value
6. Qualify early — timing, budget, decision authority upfront
7. Differentiate through execution — in AI hype, proven delivery sets you apart
8. Event strategy — organize around larger conferences ("event hijacking")

Checklists

Problem-Solving

1. Define the Problem:
   • Clearly articulate the problem statement
   • Validate with data and client input
2. Develop Hypotheses:
   • Formulate initial hypotheses based on available data
   • Test with additional data and analysis
3. Structure the Problem:
   • Break down using issue trees
   • Ensure analysis is MECE
4. Analyze Data:
   • Collect relevant data to test hypotheses
   • Focus on most impactful data points
5. Propose Solutions:
   • Target root causes
   • Validate with data and client feedback

Client Engagement

1. Pre-Wiring:
   • Discuss preliminary findings with stakeholders
   • Align expectations and gather feedback
2. Challenge Assumptions:
   • Ask probing questions to understand real issues
   • Validate client assumptions with data
3. Communicate Clearly:
   • Present findings concisely
   • Use the elevator test (30 seconds)
4. Follow-Up:
   • Schedule implementation discussions
   • Provide ongoing support

Providers to Study

• invisible
• veratai

Context

• Cost Centres
• Protocols
• Solopreneur Model