Why This CEO Thinks 2025 Will Be The Year Of True AI Transformation

The AI transformation timeline: A CEO’s perspective: DataStax CEO Chet Kapoor predicts that while 2024 will be a year of production AI, 2025 will mark the true transformation of business operations through generative AI technologies.

• Kapoor draws parallels between the current state of generative AI and previous tech revolutions like the web, mobile, and cloud, suggesting that the current challenges are a normal part of technological evolution.
• Enterprise leaders are advised to view the current implementation hurdles as stepping stones towards transformative applications in 2025.
• DataStax, as a provider of operational databases for AI applications, is closely involved with how enterprise companies are implementing AI technologies.

The three phases of GenAI adoption: Kapoor outlines a progression that companies typically follow when adopting generative AI technologies.

• Delegate: Companies initially seek 30% efficiency gains or cost-cutting measures, often through tools like GitHub Copilot or internal applications.
• Accelerate: The focus shifts to becoming 30% more effective, not just efficient, by building apps that allow for productivity gains.
• Invent: In this phase, companies begin to reinvent themselves using AI technology, potentially leading to transformative changes in operations and services.

Key areas for successful AI implementation: Kapoor identifies three crucial aspects that companies need to address to implement AI successfully.

• Technology Stack: A new, open-source based architecture is emerging as essential for transparency, meritocracy, and diversity in AI development.
• People: While data scientists remain important, Kapoor emphasizes the need to empower developers, stating that 30 million developers are needed to build AI applications, similar to web development.
• Process: Governance and regulation are becoming increasingly important, with Kapoor advocating for early involvement of regulators while cautioning against stifling innovation.

The importance of open-source solutions: Kapoor strongly advocates for open-source solutions in the GenAI stack, emphasizing their role in future AI developments.

• He asserts that if a problem is not being solved in open source, it may not be worth solving, highlighting the importance of transparency and community-driven innovation for enterprise AI projects.
• This perspective aligns with the view that 2025 will be a transformative year, giving enterprise leaders a narrow window to prepare their organizations for the impending shift.

Current challenges and innovative approaches in generative AI: Experts at a recent event hosted by DataStax discussed the challenges facing generative AI and potential solutions.

• Future improvements in large language models (LLMs) are unlikely to come from simply scaling up the pre-training process.
• Innovative approaches being explored include increasing context windows, implementing a “mixture of experts” approach, and developing agentic AI and industry-specific foundation models.
• OpenAI’s recent release of GPT-01 models with “Chain of Thought” technology represents a significant step towards enhancing the reasoning capabilities of LLMs.

The impact of GenAI on productivity: Despite skepticism from some AI critics, studies continue to demonstrate the technology’s positive impact on productivity.

• Research by Ethan Mollick, a professor at Wharton specializing in AI, shows 20-40% productivity gains for professionals using GenAI.
• Adoption rates for GenAI are reported to be the fastest in history, indicating significant perceived value among users.

Navigating the path to AI transformation: For enterprise leaders grappling with AI implementation, Kapoor’s message combines optimism with realism.

• The current challenges are viewed as necessary steps towards transformative changes in the near future.
• Those who invest in understanding and implementing AI now will be best positioned to lead in their industries as we approach 2025.
• The rapid rate of change in AI technology, terminology, and audience understanding underscores the importance of staying informed and adaptable.

Looking ahead: The baked AI platform: As the AI landscape continues to evolve rapidly, industry leaders anticipate a more stable foundation in the near future.

• Jason McClelland, CMO of DataStax, predicts that the AI platform will be “baked” within the next 6 to 18 months, suggesting a more mature and established technological foundation.
• This perspective aligns with Kapoor’s view of 2025 as a transformative year, emphasizing the narrow window enterprise leaders have to prepare their organizations for the impending shift in AI capabilities and applications.