AI for Innovation
A high-level, enterprise-grade innovation program that uses AI as a structured innovation amplifier. The program combines formal innovation logic (including TRIZ-style contradiction reasoning) with AI-assisted expansion, explicit traceability, and IP-aware decision-making. The outcome is a repeatable innovation capability that produces novel, non-obvious, and defensible solution directions.
The hours and daily structure below are recommended baselines. The program can be adjusted and modified according to client needs, organizational constraints, delivery format, and workshop intensity preferences.
Who Should Participate
R&D and Engineering Leadership
CTO office, engineering directors, principal engineers, and technical leads who need structured innovation that holds up under feasibility pressure.
Product, Strategy and Portfolio Teams
Roadmap owners who must justify direction, quantify risks, differentiate from competitors, and maintain coherence across stakeholders.
Innovation Units and Corporate Transformation
Teams that must scale innovation beyond one-off success, implement repeatable pipelines, and build governance for AI-assisted work.
IP, Legal, and Technology Strategy
Professionals focused on novelty risk, defensibility, prior-art collision, and creating protection strategies aligned with technology reality.
No coding required. Familiarity with innovation or R&D environments. Willingness to work on real organizational challenges.
Workshop: AI for Innovation (4 Hours) – Full Syllabus
Core Learning Objectives
Use AI as a structured innovation amplifier, reframe innovation problems using formal contradiction analysis, apply innovation methodologies (e.g., TRIZ) logic with AI assistance, generate solution spaces that are novel, non-obvious and defensible, understand how AI tools can be used for IP-related aspects (patentability, prior art, competitors), and design innovation processes that are repeatable inside organizations.
High-Level Structure (as provided)
0. Opening: Innovation Is a System • 1. Problem Framing at Scale • 2. AI-Augmented Innovation Methodologies (TRIZ) • 3. IP, Prior Art, and Innovation Risk • 4. From Ideas to Innovation Systems • 5. Closing: What AI Can and Cannot Do for Innovation.
| Part | Time | Topics (Full alignment with your syllabus) |
|---|---|---|
| 0 | 09:00 – 09:20 | Opening: Innovation Is a System (20 min) – Why most AI-innovation initiatives fail • Innovation ≠ ideation ≠ automation • Innovation as a loop: problem → abstraction → synthesis → validation → protection • Where AI actually fits (and where it does not). |
| 1 | 09:20 – 10:00 |
Problem Framing at Scale (40 min) – Why bad problem framing kills innovation • Engineering vs business vs strategic contradictions •
Moving from symptoms to root contradictions • Translating fuzzy goals into formal problem statements.
AI role: Decomposing messy problem spaces • Identifying hidden constraints • Generating alternative framings. |
| Hands-on | 10:00 – 10:15 | Participants re-frame their challenge into 2–3 fundamentally different problem definitions. |
| 2 | 10:15 – 11:15 |
AI-Augmented Innovation Methodologies (60 min) – TRIZ principles • Contradictions, ideality, and evolution patterns • Why TRIZ works especially well with AI •
Using AI to explore contradiction matrices at scale.
AI role: Expanding solution spaces without losing structure • Exploring non-intuitive principle combinations • Avoiding local optima and “obvious ideas”. Role separation: Human judgment vs AI expansion. |
| Hands-on | 11:15 – 11:25 | Each team generates solution directions using AI-assisted TRIZ logic and documents the reasoning path. |
| 3 | 11:25 – 12:10 |
IP, Prior Art, and Innovation Risk (45 min) – What makes an idea defensible vs merely “creative” • Innovation as entry into an existing solution landscape •
Patents vs trade secrets vs speed-to-market • How AI increases prior-art collision and false novelty confidence • Organizational risks of unstructured AI ideation.
AI role: Conceptual prior-art exploration • Claim-oriented thinking (functions, constraints, variations) • White-space exploration. |
| Hands-on | 12:10 – 12:25 | Evaluate one generated concept for: novelty risk • obviousness indicators • solution-space crowding • appropriate protection strategy. Prepare a decision gate/tree based on technological and IP considerations. |
| 4 | 12:25 – 13:00 | From Ideas to Innovation Systems (35 min) – Turning one-off success into repeatable capability • Designing internal AI-enabled innovation pipelines • Measuring innovation effectiveness beyond “number of ideas”, including Market Overview, Value, Initial Novelty Assessment, Competitive landscape, etc. Teams design a mini innovation system tailored to their organization. |
Each participant leaves with: a reframed innovation challenge • multiple structured solution paths • an IP-aware evaluation lens • a blueprint for an AI-supported innovation workflow.
Full Program: AI for Innovators (5-Day Flagship) – Full Syllabus
Program Intent
Build an end-to-end innovation capability with explicit reasoning traceability and defensibility. The flagship program produces a complete Innovation Dossier and a decision-ready innovation workflow aligned with organizational reality and IP constraints.
Mini-Project (4 Phases)
Phase 1 (Day 1): Problem framing and contradictions • Phase 2 (Day 2): Structured TRIZ solution directions and reasoning paths • Phase 3 (Day 3): IP and risk evaluation • Phase 4 (Day 4): Innovation system blueprint. Day 5 integrates and drives decisions.
| Day | Time | Agenda (Full alignment with your syllabus) |
|---|---|---|
| Day 1 | 09:00 – 09:45 | Opening: Innovation Is a System (45 min) – Why most AI-innovation initiatives fail • Innovation ≠ ideation ≠ automation • Innovation as a closed loop: problem → abstraction → synthesis → validation → protection • Why “more ideas” usually reduces innovation quality • Where AI actually fits – and where it does not. |
| 09:45 – 10:15 | AI Failure Modes (introduced early) – False confidence amplification • Over-abstraction and loss of feasibility • Pattern hallucination • Crowded solution-space illusion. | |
| 10:15 – 11:30 |
Problem Framing at Scale (75 min) – Why bad framing kills innovation downstream • Engineering vs business vs strategic contradictions • Symptoms vs root contradictions •
Translating fuzzy goals into formal problem statements.
AI role: Decomposing complex problem spaces • Surfacing hidden constraints and assumptions • Generating alternative framings without committing to solutions. |
|
| 11:45 – 13:00 |
Hands-On (Mini-Project – Phase 1): Teams define the initial challenge • generate 2–3 different problem framings • identify at least one explicit contradiction per framing.
Deliverable: Problem Framing Section of the Innovation Dossier. |
|
| 14:00 – 14:20 | Reflection & Alignment (20 min) – What changed in how the problem is understood • What assumptions were broken • Which framings feel risky vs promising. | |
| 14:20 – 15:30 | Consolidation block – selecting framings for Day 2 and preparing contradiction statements for TRIZ exploration. | |
| Day 2 | 09:00 – 11:00 | AI-Augmented Innovation Methodologies (120 min) – TRIZ foundations: contradictions, ideality, evolution patterns • Why TRIZ works especially well with AI • Scaling contradiction exploration beyond human limits. |
| 11:00 – 11:30 | Role Separation: Human Judgment vs AI Expansion – Humans define contradictions and direction • AI explores combinatorial principles • AI increases breadth; humans enforce feasibility and intent • Reasoning remains explicit and auditable. | |
| 11:45 – 13:00 | AI Role – Exploring contradiction matrices at scale • Combining non-intuitive principles • Avoiding local optima and obvious solutions. | |
| 14:00 – 15:00 |
Hands-On (Mini-Project – Phase 2): Teams select 1–2 contradictions from Day 1 • use AI-assisted TRIZ to generate multiple solution directions •
explicitly document which principles were applied and why each direction is non-obvious.
Deliverable: Solution Directions + Reasoning Paths section. |
|
| 15:00 – 15:30 | Synthesis Discussion (20 min) – Why some ideas feel “too obvious” • How structure enables creativity rather than limiting it. | |
| Day 3 | 09:00 – 11:00 | IP, Prior Art, and Innovation Risk (120 min) – Defensible innovation vs creative ideas • Innovation as entry into an existing solution landscape • Patents vs trade secrets vs speed-to-market • How AI increases prior-art collision and false novelty confidence • Organizational risks of unstructured AI ideation. |
| 11:00 – 11:25 | AI Role – Conceptual prior-art exploration • Claim-oriented thinking (functions, constraints, variations) • White-space exploration. | |
| 11:25 – 11:45 | Decision Gates Introduction (20 min) – Decision Gates: Kill, Pivot, Advance, Protect. AI informs signals. Humans decide. | |
| 11:45 – 12:15 | AI & Inventorship Risk – AI output ≠ inventorship • Loss of reasoning traceability weakens patents • Prompt logs and decision paths matter • Premature disclosure risks. | |
| 14:00 – 15:15 |
Hands-On (Mini-Project – Phase 3): Teams evaluate one solution direction for novelty risk • obviousness indicators • solution-space crowding • competitive alternatives •
appropriate protection strategy.
Deliverable: IP & Risk Evaluation section. |
|
| 15:15 – 15:30 | Wrap-up: preparing Day 4 validation and system design inputs. | |
| Day 4 | 09:00 – 10:00 |
Early Validation & Reality Checks (60 min) – Technical feasibility assumptions • Organizational feasibility (capabilities, inertia, integration friction) •
Market pull vs solution push • What must be proven vs assumed.
AI is used to surface risk, not validate truth. |
| 10:15 – 11:30 | From Ideas to Innovation Systems (75 min) – Turning one-off success into repeatable capability • Designing AI-enabled innovation pipelines • Where AI is allowed vs restricted • Governance, traceability, escalation • Measuring innovation effectiveness: Market overview • Value hypothesis • Novelty assessment • Competitive landscape. | |
| 11:45 – 13:00 |
Hands-On (Mini-Project – Phase 4): Teams design a mini innovation system for their organization • define inputs • decision gates • outputs • metrics.
Deliverable: Innovation System Blueprint section. |
|
| 14:00 – 15:00 | Governance Guidance Included – Data exposure awareness • Prompt logging for IP-sensitive work • Separation of exploratory vs confidential work. | |
| 15:00 – 15:30 | Integration prep for Day 5 – aligning dossier and presentation expectations. | |
| Day 5 | 09:00 – 10:00 | Integration: From Idea to Decision (60 min) – Reviewing the full Innovation Dossier • Stress-testing assumptions • Kill / Pivot / Advance decisions • Why killing ideas early is success, not failure. |
| 10:15 – 11:45 | Final Team Presentations (90 min) – Each team presents: problem framing • key contradiction • solution direction • IP & risk assessment • decision recommendation. | |
| 11:45 – 12:15 | Peer + facilitator feedback – focused on structure • defensibility • clarity of reasoning. | |
| 14:00 – 14:30 | Closing: What AI Can and Cannot Do for Innovation (30 min) – Common organizational failure modes • Where human judgment remains irreplaceable • How to scale without losing originality • Roadmap for implementation. | |
| 14:30 – 15:30 | Final Deliverables wrap-up – Completed Innovation Dossier • defensible innovation concept • decision rationale (kill/pivot/advance) • blueprint for AI-supported workflow. |
Each participant leaves with: a completed Innovation Dossier • a defensible innovation concept • clear decision rationale (kill/pivot/advance) • a blueprint for an AI-supported innovation workflow.
Tools Used (Hands-On, Vendor-Agnostic)
Data exposure awareness • prompt logging for IP-sensitive work • separation of exploratory vs confidential work.
Instructors
AI researcher, entrepreneur and lecturer focused on structured reasoning and system-level innovation frameworks. lecturer at the Technion - Israel Institute of Technology and Academic Director at Google–Reichman Tech School. Founder of ArtificialGate, delivering enterprise AI programs that translate cutting-edge AI capabilities into repeatable organizational workflows.
VP of Intellectual Property and IP strategist with nearly 20 years of experience leading IP portfolio development and cross-functional IP strategy in complex technology organizations. Former Chair of the Israel Patent Attorneys Association (IPAA), leading professional initiatives and collaboration with regulators. Lecturer and trainer across academic courses and management programs, speaking on IP strategy, innovation and AI. Early adopter and hands-on practitioner of cutting-edge AI tools, translating emerging platforms into practical, repeatable workflows. Education: MSc Bio-Medical Engineering (Tel-Aviv University) • MA Patent Law (University of Haifa) • Global MBA Student (Big Data & AI Track), Reichman University.
