Panel, Conference Presentation
Navigating the New Era: Designing AI strategies for Competitive Edge | RAISE Summit 2024 | Paris
- Infrastructure and power requirements for AI computation are expected to be resolved by the time strategic AI discussions occur.
- The market is shifting from generic AI applications to holistic ecosystems and brand-specific solutions, with foundational models unlikely to displace agnostic companies leveraging them for specific use cases.
- Investment focus is directed toward B2B niche markets developing vertically integrated, scalable enterprise applications that solve entire jobs rather than isolated tasks, while cloud infrastructure is viewed as a challenging sector against dominant players.
- Foundational technology is expected to be purchased from major software editors like Salesforce, Microsoft, or Adobe, leading to a trend where brands build "brain brands" using first- and second-party data.
- A two-year outlook involves tracking global trends, with specific high-impact areas identified in translation, call centers, neuroscience, healthcare (drug discovery), video advertising, and "boring" sectors like logistics and construction.
- Market dynamics predict a highly concentrated AI landscape with rapid winner evolution, prompting a need for B2B companies to verticalize and protect against competitors who may disrupt horizontal functions like ERP automation within five years.
- Quick wins in sales and accounting automation are deemed essential for competitiveness, while strategic use cases at the top of the Maslow pyramid may require longer investment horizons and business model reinvention.
- Data strategy is established as a prerequisite for value, requiring cleaned and properly structured data from the past five years to support AI initiatives.
- Talent and resource constraints are significant, with difficulties in recruiting, a lack of money and workforce in IT and governance, and challenges in scaling upskilling due to the direct correlation between input data quality and AI output.
- Regulatory and legal environments remain undefined and evolving, creating compliance hurdles for startups and necessitating that companies anticipate future rules rather than adhering to current ones.
- Speed of development has accelerated, reducing data training time from 80% to weeks for proofs of concept, yet the core complexities of data curation and model monitoring remain unchanged.
- Human factors include the risk of job displacement for non-users, the need for change management regarding skills and job futures over the next three years, and the necessity to mitigate safety and incorrect output risks to manage PR challenges.
- Efficiency and ecological mindsets are becoming critical as usage shifts from unlimited content generation to specific, high-value applications within the workflow.
- The next six months are anticipated to bring significant redistribution among AI leaders, with particular interest in backend components, chip technology, and multimodal content creation.
- Non-technical teams are expected to gain the ability to build applications due to the prominence of English as a programming language, though many managers currently underestimate the scope of AI impact beyond chat tools.