If 2023 was the year everyone wanted to be an AI company, 2024 was the year they discovered that wishing doesn’t make it so. Turns out, adding “AI-powered” to your pitch deck doesn’t automatically add zeros to your ROI. At this month’s GenAI Productionize 2.0 conference, hosted by AI evaluation platform Galileo, industry leaders shared a striking insight: a clear majority of AI projects never reach production. This isn’t just a statistic – it’s revealing how organizations are discovering the real shape of AI’s business value.
The conference, bringing together speakers from companies like HP, Databricks, and Twilio, offered a window into the state of enterprise AI implementation. While Galileo, fresh off their $45M Series B funding round, focuses on AI evaluation and observability, the discussions revealed broader patterns about the challenges and opportunities in AI implementation.
The Implementation Reality
The most successful organizations aren’t chasing the bleeding edge – they’re building practical solutions to well-defined problems. At the conference, regulated industries emerged as surprising leaders in AI implementation. Their secret? They started with clear constraints rather than open-ended possibilities.
What’s working isn’t what the headlines predicted. Instead of custom solutions promising to transform entire organizations, successful implementations focus on specific, measurable improvements to existing workflows. The real innovation isn’t in the technology – it’s in the approach.
The economics tell their own story. During one notable session, an enterprise leader shared their experience with operational costs, highlighting how LLM expenses can significantly impact project budgets – a sobering reminder that your cutting-edge AI project might cut through data like butter, but it could also eat through your budget like a teenager through a pantry. Organizations finding success are those that understood this cost reality early and planned accordingly. They’re not trying to build AI-first solutions – they’re building business solutions that thoughtfully incorporate AI where it matters most.
The Evolution of AI Development
Conference speakers noted a significant shift in who’s building these solutions. The profile of AI developers is evolving beyond traditional AI/ML specialists. Just like how everyone eventually learned to make a website, we’re seeing AI development democratize faster than you can say “prompt engineering certification.” Teams are increasingly composed of generalists with broader business context, reflecting a shift toward practical implementation over pure research.
This evolution makes sense when you look at what’s actually working. The most successful projects aren’t trying to push the boundaries of AI capability – they’re focused on solving specific business problems reliably and efficiently. The key skills aren’t in advanced AI algorithms but in understanding business processes and user needs.
What’s Actually Working
While Silicon Valley was promising AI would revolutionize everything from coffee brewing to cosmic exploration, regulated industries quietly became the adults in the room. Turns out, the secret to AI success is less “move fast and break things” and more “move deliberately and document everything.”
These practical leaders are finding success by starting with clear constraints:
- Defined use cases with measurable outcomes
- Structured approaches to data and governance
- Clear ROI requirements from the start
- Focus on company-specific data and knowledge
Looking Forward to 2025
As we look toward 2025, conference speakers highlighted several key shifts:
- ROI will become the primary driver, with increased focus on well-defined use cases
- Simpler, purpose-built models will replace one-size-fits-all approaches
- Organizations will need dedicated GenAI expertise, but in a more practical, business-focused way
- The gap between POC and production will narrow as implementation patterns mature
- Value will increasingly come from private, organization-specific data
- Trust and governance will become central to implementation success
- AI systems will require new approaches to evaluation as they become more sophisticated
For business leaders, this means adjusting both expectations and approaches:
- Start with clear, specific problems rather than broad transformational goals
- Plan for operational costs, including ongoing LLM expenses
- Invest in collecting and organizing company-specific data
- Develop clear governance and evaluation frameworks
- Build teams around business understanding rather than pure technical expertise
The Path Forward
The reality check of 2024’s implementation challenges isn’t a setback – it’s a clarification. The path to successful AI implementation is becoming clearer – and ironically, it looks a lot like regular old good business sense. Who would have thought that solving real problems would be more valuable than chasing digital squirrels?
Organizations that succeed with AI in 2025 won’t be those with the most advanced technology or the biggest AI budgets. They’ll be the ones that understand AI is just another tool in the business toolkit – powerful when applied thoughtfully, but not a magic solution to every challenge.
The question isn’t “How can we use AI?” but rather “Where do our current workflows show the clearest opportunities for enhancement?” This shift in thinking – from technology-first to business-first – is what separates successful implementations from stalled experiments.
Based on insights from the GenAI Productionize 2.0 conference, October 2024.
Ron Whitman is founder of Global Gum and a veteran technology consultant with over two decades of experience navigating platform relationships. He writes about the human patterns behind technology and business.
This article was written with assistance from artificial intelligence tools, following Global Gum’s editorial standards and fact-checking processes.