Navigating the New Era: Designing AI strategies for Competitive Edge | RAISE Summit 2024 | Paris

Panel Composition & Expertise

• Julie Hardy (BrandTech/Partner): Leads a Paris-based firm serving 80% of top global advertisers; acquired Pencil.ai (Gen-AI content solution) a year ago to enable brand-specific marketing automation.
• Emmanuel Vivier (Ebb Institute): Co-founder based in Paris; trains 10,000 executives annually across 120 brands in retail, energy, and industry; tracks global trends for a two-year horizon.
• Nicole (Merentix Fund/CEO): Former economist turned AI entrepreneur; manages a Berlin-based venture fund and the "AI Campus" (AI-focused co-working space); focuses on B2B niche markets where domain experts and tech experts co-found.
• Bogdan (Roosh Circle): Co-founder of a European tech community for entrepreneurs; emphasizes "domain knowledge + prompt engineering" as the new programming language for building scalable AI startups.

Strategic Opportunities & Value Unlocking

• Application Layer Focus: Nicole and Bogdan identify the application layer, specifically B2B verticals (synthetic biology, manufacturing, healthcare), as the primary value opportunity over infrastructure.
• The "Dual PhD" Model: Successful ventures combine deep domain expertise (e.g., medical, legal) with technical skills to solve specific, high-value problems rather than building generic tools.
• Market Shift from "Off-the-Shelf" to "Brain Brands": Enterprises have evolved from testing generic LLMs to building proprietary "brain brands" trained on first-party data to ensure brand safety and specific consumer insights.
• Agnosticism as a Moat: Experts advise avoiding reliance on a single LLM provider to maintain freedom, mitigate concentration risk, and adapt to rapidly evolving model landscapes.
• Outcome-Centric Applications: Significant defensibility is created in sectors where outcomes are critical (e.g., legal, medical) rather than "sliver" tasks, requiring the automation of entire workflows, not just isolated tasks.

Implementation Roadmap & Organizational Readiness

• Data Prerequisite: Data strategy and cleanliness are identified as the foundational "bottom layer" (Maslow pyramid) required before advanced AI use cases can succeed.
• Quick Wins vs. Long-Term Moats: Corporates should target short-term wins in standard functions (sales, accounting) while reserving long-term investment for core differentiated business models that reinvent value propositions.
• Human-Centric Change Management: Success requires massive upskilling of non-technical staff, proactive legal/IP leadership, and managing the fear of job displacement through education rather than replacement.
• Speed of Deployment: The timeline for MVP/POC has compressed from months/years to weeks, though data curation and model monitoring remain complex, high-effort tasks.

Barriers to Adoption

• Data & Regulation: Messy data, lack of clear IP guidelines, and evolving compliance regulations are cited as primary obstacles preventing startups and enterprises from scaling.
• Talent Shortage: Difficulty in recruiting AI/IT talent and the high cost of skilled human capital remain significant bottlenecks.
• Decision-Maker Literacy: A gap exists between technical teams and executive leadership, where decision-makers often underestimate the speed and impact of the technology.
• Risk Management: Organizations are shifting from exploratory "play" modes to structured, safe, and legally compliant deployments to protect brand reputation and data sovereignty.

Forward-Looking Trends (Next 6-24 Months)

• Multimodality: The explosion of video generation (beyond static images) is expected to revolutionize advertising, entertainment, and content creation.
• Vertical Specialization: A shift toward specialized engines for specific industries (legal, medical, logistics) where the AI "speaks" the industry's native language.
• Hardware & Infrastructure: Continued focus on "cheap" chips and backend infrastructure, alongside a recognition that no single player will dominate all LLM demand.
• Human-AI Collaboration: The future is defined not by AI replacing humans, but by humans who utilize AI replacing those who do not; success depends on integrating AI into collective workflows and prompt libraries.