The Davos Problem: Why 2026 Must Be the Year AI Stops Talking and Starts Delivering
- Nikolaos Lampropoulos
- Jan 22
- 5 min read
This week, as global elites gather in Davos to discuss the future of AI and technology, there's a familiar pattern playing out: a lot of talk, a lot of predictions, and very little accountability for actual delivery. It's the perfect metaphor for what's been wrong with AI adoption in agencies and businesses over the past two years.
The parallel is striking. Just as Davos brings together the 1% to theorize about problems faced by the 99% or challenges they experience from a comfortable distance, the AI discourse in our industry has been dominated by voices with no skin in the game. Tech spokespeople, influencers, and self-proclaimed thought leaders have spent months evangelizing "amazing new tools" without ever having to build, implement, or support a single practical solution.
2026 has to be different. This has to be the year AI works and delivers value, or we risk losing credibility entirely.
The Hype Phase Is Over
We're past the novelty stage. Everyone is familiar with AI now. We've all experimented with ChatGPT, tested image generators, and sat through countless demos of tools promising to "revolutionize" our workflows. The hype cycle has run its course.
What comes next requires something fundamentally different: methodical design, disciplined development, and ruthless focus on measurable outcomes. No more innovation theater. No more proof-of-concepts that sit in folders gathering digital dust. No more celebrating pilots that never see production.
The agencies and businesses that will win in 2026 are those that stop listening to opinions and start demanding results.
What "AI That Works" Actually Looks Like
When I say AI needs to work, I mean it needs to deliver tangible, undeniable value to the bottom line. Here's what that looks like in practice:
Operational Excellence, Not Innovation Theater
Agency teams should be able to use AI in ways that demonstrably reduce costs, automate repetitive processes, and free their people to do higher-value work. This means actual time savings that can be measured in hours reclaimed per week, overhead reductions that show up in quarterly financials, and productivity gains that translate to either increased capacity or decreased headcount needs.
Data That Actually Gets Used
The ability to consolidate data from multiple disparate sources into a single source of truth isn't aspirational anymore—it's table stakes. But it only matters if that consolidated data enables real-time querying, generates insights that lead to actions, and surfaces operational bottlenecks before they become crises. Pretty dashboards that nobody looks at don't count.
Solutions That Scale Beyond the Pilot
Real AI value comes from solutions that improve project delivery, enhance employee engagement, and enable the creation of new AI-driven products and services for clients. These aren't one-off experiments, they're systematic capabilities that become part of how the business operates.
The Skin-in-the-Game Problem
Here's where the Davos comparison really lands: opinions are cheap, especially when they come from people who don't have to deliver on them.
The AI discourse has been polluted by voices that have zero accountability. They can talk all day about the potential of new models, the promise of emerging tools, and the theoretical applications of AI, but when it comes to actual solution delivery, they disappear. They don't have to deal with:
Legacy systems that won't integrate
Data quality issues that make AI outputs worthless
Change management challenges when teams resist new workflows
Budget constraints that make enterprise solutions unaffordable
Support and maintenance costs that exceed the initial implementation
The gap between demo performance and production reality
It's easy to be an AI optimist when you're not the one who has to make it work, support it, or stake your professional reputation on its success.
Marrying Great Talent with Great Technology
2026 needs to be the year we figure out how to marry great talent with great technology, and that marriage requires more than just buying tools and hoping people use them.
It requires:
Strategic clarity about what problems you're actually trying to solve
Technical honesty about what's feasible with current capabilities
Organizational readiness to change workflows and processes
Measurement discipline to track real outcomes, not vanity metrics
Leadership commitment to invest in capabilities, not just tools
Most importantly, it requires shedding everything that doesn't serve a purpose. Every tool that creates more work than it eliminates. Every initiative that's about optics rather than outcomes. Every voice that offers opinions without responsibility for results.
The Accountability Gap
The Davos elite discuss global challenges from private jets and five-star hotels. They propose solutions for problems they don't personally experience. There's no feedback loop, no accountability, no consequence for being wrong.
The AI hype machine has operated the same way. Consultants and commentators have promoted tools and approaches without having to live with the consequences of their recommendations. They move on to the next trend while agencies are left managing the technical debt and broken promises.
This has to end.
What Changes in 2026
If 2026 is going to be different, we need to fundamentally change whose voices we listen to and what criteria we use to evaluate AI solutions:
1. Demand Proof Points Over Promises
Stop accepting "this could potentially" and start requiring "this has demonstrably." Case studies with real numbers. Implementations that have survived beyond the pilot phase. Solutions that work in production environments with real-world constraints.
2. Prioritize Integration Over Innovation
The most valuable AI isn't the most cutting-edge, it's the most integrated. Solutions that fit into existing workflows, connect to existing systems, and enhance current capabilities will deliver more value than standalone "revolutionary" tools that require building entirely new processes around them.
3. Focus on Sustainability Over Novelty
AI solutions need to be maintainable, supportable, and economically sustainable. The total cost of ownership matters more than the initial price tag. The ability to operate without constant vendor support matters more than bleeding-edge features.
4. Listen to Practitioners Over Pundits
The people who deserve your attention are those who have actually built, deployed, and supported AI solutions in production environments similar to yours. Not the ones with the biggest platforms or the most confident predictions, the ones with scar tissue and lessons learned.
The Bottom Line
We don't need another year of AI potential. We don't need more Davos-style discussions about what AI might do. We don't need additional opinions from people who will never have to make any of this actually work.
We need AI that delivers undeniable value. We need solutions that survive contact with reality. We need voices that have skin in the game.
2026 has to be the year we stop talking and start delivering, or we risk proving the skeptics right.
The choice is ours. Choose methodical execution over endless exploration. Choose measured outcomes over bold predictions. Choose accountability over opinions.
The hype phase is over. The delivery phase begins now.
What AI solutions have actually delivered measurable value in your organization? What separated the ones that worked from the ones that didn't? The industry needs more honest conversations about real results and fewer predictions about potential.
