Let´s start with three questions you should put on your radar:

1. Ask yourself: "What parts of our workflows could we hand over to an AI that acts – not just one we consult?" That is where agents are heading (1).
2. Challenge your team: "What data are we ignoring because we thought 'AI only works on structured data'?” Unstructured data is the goldmine now (2)!
3. Look at your business: "If everyone has access to the same AI models, what will differentiate winners from laggards?" Hint: It’s going to be infrastructure, culture, data-craft – not just the model headline (3) .

Let´s dive deeper:

We’ve grown comfortable with AI that responds to us (“Hey ChatGPT …”). But the next wave is AI that acts – autonomously, adaptively, as part of workflows. According to recent analysis, “agentic AI” – systems capable of independent action, coordination and decision-making – is being widely anticipated (4). What does this mean in practice?

• Gone are purely reactive bots; here come entities that plan, coordinate, refine.
• For businesses, that implies a shift: AI will not just accelerate our workflows but begin to own pieces of them.
• The catch: trust, oversight and “who owns the action” become huge governance issues. As one expert put it – “you still need humans checking in” even when agents act (5).

Text-only AI is yesterday’s baseline. The bigger shift: systems that understand not just words but sound, vision, motion, environment – and act in the physical world.

• The concept of multimodal AI (combining text, image, audio, video) is rapidly gaining ground (6).
• Likewise, embodied AI means AI with a physical or simulated interface – robots, AR/VR, sensors. IBM’s trend-list flagged “embodied AI and world models” as key (7).
• Agent-as-a-Service will emerge. Rather than paying for tools, businesses will pay for execution. Instead of purchasing software licenses, companies will rent outcomes – work performed by AI agents. (8)
• Real-world impact: imagine factories where AI watches the physical line, adapts the layout, interacts with machines – and not just via a terminal but via sight, sound, motion. Or retail environments where AI interprets #customer gestures, voice, movement and adapts the experience.
• For you as a thought-leader: talk less about “AI writes blog posts” and more about “AI inhabits spaces”.

While many focus on the “front-stage” of AI (chatbots, image-generators), the real game is behind the curtain: how data is organized, how models reason, what chips they run on.

• Mature AI adoption means dealing with unstructured data: text, images, video – not just neat spreadsheets. A survey found 94% of AI/data leaders said generative AI is making unstructured data important again (9).
• There’s a rising emphasis on reasoning models – not just pattern-matching, but logic, abstraction, multi-step thinking. AI systems will be able to synthesize meaning across entire libraries of documents, long-running conversations, and even the full body of everything you’ve read or written, creating a truly continuous intelligence layer. (10)
• Models are becoming the new operating system – an intelligence layer that applications plug into. Instead of interacting with tools directly, users will define high-level goals, much like entering a destination into a navigation app, and AI will autonomously plan and execute the steps required to achieve them. (11)
• Hardware and infrastructure matter: custom silicon, novel architectures, and “foundation model commoditization” are influencing which players succeed (12).
• The takeaway for business-savvy folks: AI isn’t just a “tool you buy”; it’s increasingly a strategic platform – and your advantage may come from infrastructure, data strategy, and model-strategy as much as from clever use-cases.

Here’s one to bookmark:

• By 2026, organizations that treat AI as feature (i.e., add-on-tool) rather than platform (i.e., core to business) will find themselves less competitive (13).
• The future winners will be those who build AI like they once built IT: as foundational, strategic architecture – with data design, governance and model life-cycle embedded across the business.
• This leads to the fact that rigid mastery of old workflows will be less effective than adaptability and curiosity. Talents who are willing to rethink and evolve their expertise will be increasingly valued. (11)
• In other words: the next decade won’t be about “Does our company use AI?” but “Is AI embedded in how we operate?”