Executive Advisory

Get your AI Use Case Portfolio and 24-Month AI Roadmap in just 8 weeks.

For executives who own the AI mandate. Moving from scattered AI initiatives to a coherent AI adoption strategy.

50 hours · 32 sessions · 8 weeks · 5 executive deliverables By George Krasadakis, author of Innovation Mode 2.0
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8
Weeks
12
Strategic Use Cases
5
Framed Product Concepts
1
24-Month Roadmap
The Problem

AI Spend Is Accelerating. Often on the Wrong Initiatives.

AI budgets are growing faster than AI clarity. Pilots stall before production. Vendors get selected before use cases are validated. Talent gets hired without a clear roadmap. Most AI initiatives fail not because the models don't work, but because organizations invest before they decide where AI actually creates competitive advantage. That's a strategy problem — not a technology one.

What Most Companies Do

  • Chase vendor demos that promise transformation overnight
  • Run AI pilots that never scale beyond the demo environment
  • Hire data scientists without a clear strategy for what to build
  • Confuse AI automation with AI-powered competitive advantage
  • Present "we're exploring AI" as a strategy to the board

What This Program Delivers

  • An objective assessment of your AI readiness across five dimensions
  • A scored portfolio of use cases prioritized by impact and feasibility
  • Detailed product concepts for your top 3–5 AI opportunities
  • A phased implementation roadmap covering 12–24 months
  • A board-ready executive AI briefing you can present next quarter

The hardest part of adopting AI isn't the technology itself.
It's knowing where to apply it, in what sequence, and how to measure it.

— George Krasadakis

What You Receive

Five Deliverables. Designed for Action.

Every deliverable is shaped by the first published architecture for agentic innovation in a corporate context — covering AI-powered opportunity discovery, AI-assisted validation, and agent-driven realization. The architecture is documented in Innovation Mode 2.0 (Springer, 2026) and built on 25 years of shipping intelligent systems — from data mining platforms in the early 2000s to autonomous AI agents today, including Ainna.ai in production. Together these frameworks form an AI transformation roadmap your team can act on — not theoretical.

01

AI Readiness Assessment

A comprehensive diagnostic across five capability dimensions: data infrastructure, technical talent, process maturity, leadership alignment, and cultural readiness. No generic benchmarks — a diagnosis specific to your operating reality, identifying exactly where the gaps will slow you down.

02

AI Use Case Portfolio

A prioritized portfolio of AI opportunities mapped across your business — from quick wins that build momentum to strategic bets that create competitive moats. Each use case scored on business impact, technical feasibility, data readiness, and implementation complexity. Not a wishlist — a decision-ready framework.

03

AI Product Concepts

For your top 3–5 priority use cases: detailed product concepts including user journeys, technical architecture, build-vs-buy recommendations, integration points, and success metrics. These are the specifications your product and engineering teams need to move from "interesting idea" to "let's build this."

04

AI Innovation Masterplan

A phased AI transformation plan covering 12–24 months: what to pilot first, what to scale, what to defer. Includes technology stack recommendations, vendor evaluation criteria, talent requirements, AI governance framework, and risk mitigation. Each phase has clear milestones and decision gates — sequenced as an AI innovation roadmap your board can authorize and your teams can execute.

05

Executive AI Briefing

A board-ready presentation synthesizing findings, recommendations, and roadmap — designed for communicating AI strategy to leadership, investors, and stakeholders. Includes talking points for the questions your board will ask: ROI timelines, competitive positioning, risk exposure, and talent strategy. Present it next quarter.

Innovation Mode 2.0 book

Every program includes 10 copies of Innovation Mode 2.0 — the complete framework for building AI-powered innovation capabilities. Your leadership team receives the definitive guide to sustaining the AI transformation long after the program concludes.

The 8-Week Program

From AI Ambiguity to AI Clarity. In 8 Weeks.

50 hours of AI strategy advisory delivered across 32 sessions. Every week targets a specific dimension — building toward a complete, actionable AI strategy your team can execute. Remote or hybrid.

1
Week 1

AI Readiness

We assess your AI capabilities across five dimensions: data infrastructure and quality, technical talent and skills gaps, existing AI/ML initiatives, vendor relationships, and organizational readiness for AI-driven change — using the same diagnostic discipline as our Innovation Maturity Index. We benchmark against what's achievable and identify the specific constraints that will shape your strategy.

→ AI Readiness Assessment with capability gaps
2
Week 2

Opportunity Mapping

We map AI opportunities across your entire value chain — customer experience, operations, product development, decision-making, and back-office functions. We interview stakeholders across business units to surface pain points. The goal: identify where AI creates competitive advantage, not just automation.

→ Comprehensive AI opportunity map across business functions
3
Week 3

Strategic Prioritization

We score and prioritize identified opportunities: business impact, technical feasibility, data readiness, time to value, and strategic alignment. We separate quick wins from long-term bets and identify the sequencing that builds capability while delivering results. You'll know exactly which use cases to pursue — and which to defer.

→ Prioritized AI Use Case Portfolio with scoring rationale
4
Week 4

AI Product Concepts

For your top 3–5 priority use cases, we develop detailed product concepts: user journeys, technical architecture sketches, data requirements, integration points, build-vs-buy analysis, and success metrics. These aren't vague ideas — they're specifications your teams can act on.

→ Detailed AI Product Concepts for priority use cases
5
Week 5

Technology & Vendor Strategy

Technology stack, vendor selection, and build-vs-buy decisions. Evaluation criteria for AI platforms, guidance on foundation models vs. custom training, cloud strategy, compute requirements, and data architecture. We help you make the infrastructure decisions that determine whether your AI initiatives scale or stall.

→ Technology strategy and vendor evaluation framework
6
Week 6

AI Governance & Risk

An AI governance framework appropriate to your organization: policies for data usage, model validation, bias monitoring, and human oversight. Regulatory considerations, IP protection, and risk management. This isn't about slowing you down — it's about building AI capabilities that scale without creating liabilities.

→ AI Governance Framework and risk mitigation plan
7–8
Weeks 7–8 · Synthesis Phase

Implementation Roadmap & Executive Briefing

We synthesize all findings into a comprehensive AI Implementation Roadmap and a board-ready Executive AI Briefing. Your roadmap covers 12–24 months — what to pilot first, what to scale, what to defer — with clear milestones, resource requirements, and decision gates at every phase.

Final deliverables presented to leadership: AI Readiness Assessment, Prioritized Use Case Portfolio, AI Product Concepts, Implementation Roadmap, Executive AI Briefing, and 10 copies of Innovation Mode 2.0.

→ Complete AI strategy package
The Implementation Path

Your Roadmap: From Foundation to AI-Powered in Four Phases

The AI Implementation Roadmap sequences your AI maturity transformation across 12–24 months. Each phase builds on the previous — quick wins first, strategic bets second, autonomous capabilities last.

Months 1–3
Foundation
Quick wins, data infrastructure, core team formation. Example: data audit, 2–3 use cases shipped (document AI for contracts, customer service triage).
Months 4–9
Build
Priority use case development, pilot deployment, learning cycles. Example: top-3 use cases moving from concept to working pilot with measurable KPIs.
Months 10–18
Scale
Production rollout, capability expansion, organizational embedding. Example: cross-functional AI team established, first AI products in customer-facing production.
Month 18+
Evolve
Advanced use cases, autonomous systems, continuous optimization. Example: agentic workflows, AI-native product capabilities, internal AI platform.
Your Advisor

AI Strategy from an AI Inventor — Not a Consultant

This program is led by an AI inventor who has been building intelligent systems for 25 years — from data mining platforms in the late 1990s to autonomous AI agents today. Not a team of junior analysts learning on your dime. The person who holds the patents, wrote the book, and built the tools.

George Krasadakis — AI inventor, holder of 20+ AI patents

George Krasadakis

Author of Innovation Mode 2.0 (Springer, 2026). Sole inventor of 20+ patents in AI-powered ideation, voice-driven brainstorming agents, and intelligent negotiation systems — filed 2016–2018, years before generative AI validated every thesis. His patent portfolio reads like a 2025 AI startup pitch deck, except he filed it a decade ago.

Senior AI and innovation leadership at Microsoft, Accenture, GSK, and ResMed. Founded five technology ventures — from Datamine's early machine learning systems to Ainna.ai, the AI product opportunity framer that proves AI-powered innovation works in production. MSc in Computational Statistics (University of Bath). 500+ Google Scholar citations. Follow George on LinkedIn →

20+
AI Patents
25
Years in AI
80+
Tech Projects
5×
Founder
500+
Citations
Who This Program Serves

For Executives Who Own the AI Mandate

This program is commissioned by the executive who owns the AI agenda — typically the CEO, CAIO, CTO, CDO, or VP-level sponsor with budget authority. They convene a core team of 4–6 stakeholders for the engagement: heads of product or technology, business unit leaders, and someone from data/analytics. What matters is leadership commitment to act on the findings.

CEOs, COOs & Board-Level Leaders

You need to present an AI strategy next quarter. This program gives you the assessment, the portfolio, and the board-ready briefing to do it with confidence.

CTOs, CDOs & Heads of Technology

You own the technical roadmap. This program provides the architecture decisions, vendor framework, and build-vs-buy analysis your team needs to execute.

Heads of Product, R&D & Innovation

You decide what gets built. This program delivers detailed AI product concepts — user journeys, technical architecture, integration points — for your highest-impact opportunities.

Chief AI Officers & VP AI/Data

You own the AI mandate. This program gives you an objective external readiness assessment, a scored use case portfolio, and a published methodology to reference — a Chief AI Officer-as-a-Service framework that compresses your Year 1 framework-building into 8 weeks, freeing you to focus on execution.

Endorsements

On Innovation Mode — The Book Behind the Methodology

The frameworks deployed in this program are drawn from Innovation Mode 2.0 (Springer, 2026). Here's what innovation leaders are saying about the book.

"Not just a book, but a blueprint for what it really takes to build innovative companies in today's world — companies that don't just talk about innovation, but live it in their culture, in their systems, and in their everyday decisions."
— Alex Adamopoulos | CEO, Emergn
"An excellent overview and reference point about people, culture, and capabilities as the key pillars of innovation and practical ways to discover, validate, and realize opportunities on the road to innovation."
— Dr. Mathew Hughes | Professor of Innovation, University of Leicester
"Very practical and inspirational for executives, leaders, middle-level managers, and teams willing to transform their vision to actual execution. Must-read and must-use."
— Achilleas Stergioulis | Director, INTRASOFT International
"A must-read for innovation managers, top-level corporate executives, and entrepreneurs!"
— A. Tzoumas | CTO, SciFY.org
Frequently Asked

Questions from Prospective Clients

How do you prioritize AI use cases for enterprise investment?

AI use cases are prioritized by scoring each opportunity across four orthogonal dimensions: (1) business impact — projected revenue uplift, cost reduction, or competitive advantage; (2) technical feasibility — model maturity, data availability, and integration complexity; (3) data readiness — whether the proprietary data needed actually exists at the required quality and volume; (4) strategic fit — alignment with corporate strategy, competitive positioning, and risk appetite. The output is an AI use case portfolio mapped across short-term wins (3-6 months payback), medium-horizon investments (12-18 months), and long-term capability bets (24+ months) — typically 8 to 12 prioritized use cases for an enterprise. The most common prioritization error is starting from technology ("what can the model do?") rather than business ("what problem are we solving?"), which produces impressive demos that never reach production. The AI Strategy Advisory program delivers this scored portfolio in Week 3, built on the methodology documented in Innovation Mode 2.0 (Springer, 2026).

Why do most enterprise AI pilots fail to scale to production?

Most enterprise AI pilots fail to scale because they are designed as technology demonstrations rather than as operational infrastructure — five recurring failure modes account for the majority. (1) No production data path: pilots use clean curated datasets that don't exist in the production environment. (2) No integration architecture: the pilot model has no clear path into the systems business users actually work in. (3) Missing governance: data privacy, model auditability, and decision-rights are treated as afterthoughts and surface as blockers at production gate. (4) Unclear ownership: the pilot succeeds in a lab but no one owns the production deployment, the maintenance, or the success metrics. (5) Wrong success metric: pilots are evaluated on model accuracy rather than business outcome, so even technically successful pilots can't justify production investment. The fix is treating AI as operational infrastructure from day one — designed for production, not as proof-of-concept. Innovation Mode 2.0 (Springer, 2026) documents the four-layer architecture (use case portfolio, agentic systems, governance, phased roadmap) that reduces this failure rate, and the AI Strategy Advisory program installs it across an 8-week engagement.

What does effective AI governance look like in a corporate environment?

Effective corporate AI governance is built on five integrated controls operating across the AI lifecycle, not as a bolt-on compliance function. (1) Use case approval — every AI initiative passes through a documented gate covering business case, risk classification, and decision-rights ownership before development begins. (2) Data and model lineage — provenance tracking for training data, model versions, and outputs so any AI decision can be traced and audited. (3) Bias and fairness assessment — structured testing for disparate impact across protected attributes, with documented mitigations before production deployment. (4) Production monitoring — real-time tracking of model drift, hallucination rates, prediction accuracy, and decision confidence, with escalation paths when thresholds are breached. (5) Regulatory alignment — explicit mapping to applicable frameworks (EU AI Act, NIST AI RMF, sector-specific regulations) with audit-ready documentation. The most common AI governance failure is treating it as a legal review rather than as operational infrastructure — by the time a model reaches the legal team, the architectural decisions that determined governability are already locked in. The AI Strategy Advisory program embeds governance from Week 1 of the AI Innovation Masterplan, built on the methodology documented in Innovation Mode 2.0 (Springer, 2026).

What does a Chief AI Officer (CAIO) actually do?

A Chief AI Officer (CAIO) owns the operational responsibility for translating AI ambition into AI outcomes — distinct from the CTO (who owns broader technology infrastructure), the CDO (who owns data assets), and the CINO (who owns innovation strategy more generally). The CAIO's actual remit covers six interconnected responsibilities: (1) maintaining the AI strategy and use case portfolio aligned to corporate strategy; (2) running AI portfolio governance with stage-gated investment across short-term, medium-term, and capability-building horizons; (3) building and scaling AI capabilities — talent, infrastructure, MLOps, model governance — across the operating organization; (4) operating AI-powered innovation pipelines including opportunity discovery, validation, and agentic deployment; (5) measuring and reporting AI performance to the board across capability, pipeline, and business impact metrics; (6) managing AI risk — bias, regulatory compliance, model drift, and reputational exposure. The role typically reports to the CEO with dotted-line accountability to the board. New CAIOs in the first 90 days commonly need a published methodology to anchor the role; Innovation Mode 2.0 (Springer, 2026) covers the AI integration architecture and the agentic innovation framework specifically for this purpose.

How do you measure ROI on enterprise AI investments?

AI ROI should be measured across four sequential metric tiers, mapped to the AI investment lifecycle rather than treated as a single dashboard number. (1) Capability metrics — AI readiness score, data infrastructure maturity, AI talent coverage, MLOps maturity — answer "are we building the foundation?". (2) Pipeline metrics — AI use cases identified, validated, in production; concept-to-production cycle time; pilot-to-scale conversion rate — answer "is the engine running?". (3) Output metrics — number of production AI systems, decisions automated, predictions made, agentic workflows deployed — answer "are we producing?". (4) Business impact metrics — revenue from AI-enabled products, operational cost reduction, customer experience lift, market share in AI-augmented categories — answer "does it matter to the business?". Most enterprise AI programs over-report Tier 3 (vanity output metrics — "we deployed 47 models") while ignoring Tier 1 (capability) and Tier 4 (business impact), which is why boards lose confidence in AI spend. The Executive AI Briefing delivered in Week 8 of the AI Strategy Advisory program installs all four tiers as a board-ready measurement framework, built on the methodology documented in Innovation Mode 2.0 (Springer, 2026).

What is agentic AI and how is it changing corporate innovation?

Agentic AI refers to AI systems that perform multi-step work autonomously — making decisions, calling tools, executing actions, and producing outcomes — rather than responding reactively to single prompts. In a corporate innovation context, agentic AI is replacing three traditionally manual functions: opportunity discovery (autonomous scanning of markets, patents, customer signals, and academic research to surface ranked opportunities); validation (simulated AI panels stress-testing concepts for feasibility, market fit, and risk before significant investment); and realization (agent-driven execution of validated concepts through prototyping, integration, and deployment). The shift from prompt-based assistants to autonomous agents is the most significant change in enterprise AI since the launch of ChatGPT — and the architectural decisions made today determine whether the organization participates in it as a builder or as a buyer of someone else's agents. The first published architecture for agentic innovation in a corporate context is documented in Innovation Mode 2.0 (Springer, 2026), and the AI Strategy Advisory program is led by an AI inventor with 20+ AI/ML patents filed before ChatGPT existed, architect of Ainna.ai — an autonomous AI innovation agent.

What are the signs my organization needs an AI strategy advisory engagement?

Common signals: a board demanding an AI strategy without clear direction; multiple AI pilots running in different business units without coordination; vendors pushing AI initiatives without a clear business case; competitors making AI moves you can't yet evaluate; a CTO or CDO asking for prioritization frameworks; or the realization that "we're exploring AI" is no longer an acceptable answer to "what's our AI strategy?" If three or more apply, you'll benefit from the structured assessment and prioritization the program delivers.

How is this different from AI consulting from the big firms?

You work directly with an AI inventor who builds AI systems — not partners who delegate to junior analysts learning on your budget. The advice comes from 20+ AI patents, 25 years of hands-on implementation, and an active AI product (Ainna.ai) in production. The deliverables are designed for action: product concepts your teams can build, not slide decks that sit on a shelf.

Does this work for non-tech companies or organizations already running AI initiatives?

Yes to both. Enterprise AI strategy is no longer a tech-company concern — pharma, manufacturing, financial services, logistics, retail, every industry has AI opportunities, and the advisor's career spans telecom, banking, retail, life sciences, and digital health. For organizations already running AI initiatives, the program assesses what's working, what isn't, and how to course-correct — many of the most valuable engagements are with companies whose pilots haven't scaled, where understanding why is more valuable than starting from scratch.

Should we run this program if we already have a Chief AI Officer?

Many high-impact engagements are commissioned by incoming or sitting Chief AI Officers. The reason: a CAIO typically spends their first 6–12 months building readiness assessments, use case portfolios, and roadmaps from scratch to establish credibility with the board. This program compresses that work into 8 weeks — giving the CAIO an objective external assessment and a published methodology to reference, so Year 1 focuses on execution, not framework-building. For organizations preparing to hire a CAIO, the program is also a precursor to that hire — the incoming executive arrives to a ready strategy rather than a blank page.

Is this specifically about generative AI, or AI more broadly?

Both. The program covers the full AI portfolio: generative AI for content, conversation, and reasoning; traditional ML for prediction, classification, and optimization; computer vision and document AI; agentic systems for workflow automation; and AI-powered decision systems. Most organizations need a mix — generative AI is high-visibility and creates urgency, but traditional ML often delivers more reliable ROI in operational contexts. The use case portfolio surfaces both categories so you invest where the business case is strongest, not just where the hype is loudest.

What ROI should we expect from an AI strategy advisory engagement?

An 8-week strategy program delivers planning value, not direct revenue impact — AI ROI compounds over multi-year horizons. What it does deliver is decisions that prevent waste: avoiding the wrong use cases (the most common AI failure mode), correctly sequencing investments so pilots actually reach production, and making informed build-vs-buy choices that compound over time. Organizations with a clear AI strategy typically reduce time-to-pilot by 30–50% compared to ad-hoc approaches and avoid the 60–80% spend that goes to AI initiatives without strategic grounding. We do not promise specific revenue figures because they depend on factors outside the program's scope.

How much does an AI strategy advisory engagement cost?

AI strategy advisory engagements are typically priced bespoke based on organizational scale, AI ambition, and the depth of use case portfolio analysis. Standard engagements for mid-market organizations typically start in the low six-figure range; enterprise programs scale up from there. The AI Strategy Advisory program includes 50 hours of advisory across 8 weeks, five executive deliverables (AI Readiness Assessment, AI Use Case Portfolio, AI Product Concepts, AI Innovation Masterplan, Executive AI Briefing), and 10 copies of Innovation Mode 2.0 (Springer, 2026). A tailored proposal is provided within 48 hours of application.

Does this cover AI governance and regulatory considerations?

Yes — Week 6 is dedicated to AI Governance & Risk. The deliverable includes data usage policies, model validation protocols, bias monitoring frameworks, human oversight standards, and regulatory considerations relevant to your jurisdiction (EU AI Act, sector-specific regulations like HIPAA for healthcare or PCI for finance). The framework is designed to enable AI deployment, not slow it — building in compliance and ethics review upfront is faster and cheaper than retrofitting them after a public incident.

How does this relate to the Corporate Innovation Advisory?

The Corporate Innovation Advisory builds organizational innovation capability — systems, culture, and processes for systematic opportunity discovery. The AI Strategy Advisory focuses specifically on AI adoption: where to invest, what to build, how to implement. They're complementary — some clients commission both as a sequenced program.

What's the time commitment for our team?

AI strategy advisory engagements typically require 4–6 hours per week from a cross-functional core team — usually data and engineering leadership, business unit sponsors for the use cases under evaluation, and AI/ML talent if already in place. The structure includes data infrastructure audits, AI use case validation interviews with business stakeholders, technical feasibility reviews with engineering, and executive alignment sessions with the C-suite. The 50 hours of advisory across 8 weeks is the program's commitment; the team's involvement is intensive but manageable alongside normal responsibilities, structured around the AI Innovation Masterplan delivery.

Who implements the strategy after the engagement ends?

Implementation is typically led by your internal teams or delivery partners. The deliverables — particularly the AI Product Concepts and AI Innovation Masterplan — are designed to hand off cleanly to product, engineering, and data teams without requiring further interpretation. Quarterly check-ins and extended advisory engagements are available for organizations wanting ongoing strategic support through the implementation phase, but they're optional — no pressure, no lock-in. Many clients run the 8-week program, execute internally for 6–9 months, and return for a refresh when the roadmap reaches its next inflection point.

Why do most AI pilots fail to scale?

Predictable reasons: wrong use case selection, data problems discovered late, lack of integration planning, missing change management, and no clear path from pilot to production. A proper AI strategy — which is what this program delivers — prevents these failures upfront by addressing them before you write a single line of code.
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50 hours · 32 sessions · 8 weeks · Remote or hybrid · Led by the holder of 20+ AI/ML patents

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