In Davos this year, the conversation about artificial intelligence shifted — and that shift matters for every business, big and small.
At the World Economic Forum’s 56th annual meeting in Davos, Switzerland, global leaders didn’t just talk about AI as a distant future tool — they debated how it’s shaping jobs, competitive advantage, and strategy right now. The discussions revealed a more nuanced, realistic view of AI’s role in transformation — one that SMBs should take seriously as they plan for 2026.
AI Isn’t Just Hype — But It Isn’t Plug-and-Play, Either
At Davos, executives from major firms framed AI as both an opportunity and a challenge:
Leaders from companies like IBM, Cisco, and BNY highlighted how AI can cut onboarding times and boost productivity — but only when it’s integrated with real operational clarity.
More than half of companies reportedly aren’t seeing measurable benefits from their AI investments, largely because they lack the foundational groundwork. That observation came from PwC’s global chairman at Davos, who framed this as a leadership challenge rather than a technology failure.
The pattern is clear: AI isn’t magic — and it doesn’t automatically create value by virtue of being adopted.
For small and mid-size businesses, this means the focus shouldn’t be on “getting AI,” but on making AI work within the context of your business systems and people. It must be ENGINEERED AND WEAVED into your overall strategy.
Lesson for SMBs: Invest in Value-Focused AI, Not Buzz
Here’s how SMBs can apply what the global conversation at the 56th World Economic Forum revealed:
1) Start With Your Core Operational Systems
Leaders at the forum emphasized that AI value only appears when underlying systems and data are clean, integrated, and usable — something SMBs can do before competitors with legacy drag catch up.
SMB application:
Prioritize unifying key tools (i.e. order entry, HRIS, CRM, accounting software, project management software)
Ensure data feeds are real-time where possible (this is the power of employing humans committed to keeping data honest, accurate, and as close to real-time as possible -- every department in an organization relies on key data. Give your person constantly truing up the numbers at your organization a pat on the back because without them, your producers / service providers / business development reps would be hamstrung)
Use simple dashboards to track performance. It needs to work but it also needs to be able to be translatable to anyone in the organization to understand. Especially if you're using it as a tool to drive people's alignment.
AI is only as good as the ground it runs on.
2) Connect AI to People and Decision Workflows
At Davos, some of the most persuasive remarks about AI weren’t about technology — they were about people. A report from the World Economic Forum’s own coverage highlighted that adoption grows fastest when leaders model how they use AI and when employees see it solve real work problems.
SMB application:
Train teams on practical use cases, not just tool features (Who's spearheading "practicalizing" the technology of AI at your organization? It's one thing to know how to write a prompt. It's another to build a framework using multi-prompts that produces an orchestrated set of automated behaviors to augment your ability to execute your specific roles and responsibilities. Who's skilling your people up in this regard?)
Spotlight AI wins internally to build momentum (Whether we say we love it or hate it, we respond, adapt, and act accordingly to the recognition and the discipline we receive)
Pair humans and machines in ways that enhance roles rather than replace them
This is how to go from friction-based thinking to leverage-based and integrated approach thinking.
3) Treat AI as a Strategic, Not Tactical, Play
The Davos dialogue also underscored something often missed: executives are increasingly being judged on measurable AI ROI, not just experimentation. When AI is not tied to outcomes, it becomes “noise." This is and WAS always business reality -- we need to be good at quantifying our impact. It's just a good skill to have and/or develop and something I love helping professionals do.
SMB application:
Define clear business outcomes before you invest (e.g., >10% reduction in lead time, >15% improvement in customer response times)
Pilot only where there’s a real performance gap
Scale incrementally — not wildly
AI should be a tool to solve a problem, not an ego-stroke of being cutting-edge and forward thinking.
Making a Strategic Shift: From Buzz to Business Value
The common thread from global leaders’ remarks this week is that AI’s future will be defined by execution, not hype. Even among Fortune-level firms, measurable returns from AI are uneven because foundations were rushed or organizational alignment was weak.
Small and mid-size businesses can leapfrog this risk by:
Strengthening the infrastructure before pursuing flashy features
Emphasizing outcomes over optics
Designing adoption paths that are people-centric and operations-aligned
In other words:
AI success will come to the businesses that invest in clarity and human context first.
That’s a 2026 leadership insight worth internalizing — whether you operate with 10 people or 1,000.