Reflections on our recent talk at the Brno conference.

We’ve been invited to a conference in Brno to share our view and expertise on the topics of AI automation, AI agents, its securing and pricing and a couple of other points. Topics which we are surrounded by every day now both in our personal lives and in work for our clients.  

Together we unpacked one big question that’s on every leadership agenda right now:

How can a company stay flexible when the AI landscape won’t sit still for even a quarter?

Below is a slightly expanded recap of the talk. Part field notes from the conference floor, part practical guide for teams who want to move beyond AI hype and toward concrete automation wins.

1. Why we called it an “AI" future

AI’s potential is huge, but its trajectory is anything but predictable. The models available today are simultaneously the least powerful and the most expensive they will ever be.  

Presumably next year they’ll be cheaper and better, and the year after that the cycle repeats. That exponential curve demands an equally exponential mindset: absolute flexibility in both strategy and architecture.

2. Meet the agent: your new digital teammate

An AI agent is software that completes tasks a human would normally do. Sometimes hand-in-hand with you, sometimes entirely on its own.

• Key idea: context is oxygen. Just as people need background to work, agents need data.
  
• That data has to be digitally stored and programmatically reachable—ideally via clean APIs or database access.

3. Data first, always

The more structured data you have, the more surface area agents have to create value. Most organisations already own the raw material; it’s just scattered across CRM records, SharePoint folders, data warehouse and Excel files (oh these excel files…). Step one is exposing those islands through APIs so agents can swim between them.

4. Giving agents the right context

We covered two parts:

• Static context - system prompts, tool definitions
• Dynamic context - Interactive Q&A, data fetched on demand

Rule of thumb: send only what the agent needs for the specific request. Nothing more, nothing less.

5. How automation maturity evolves

1. Human only - the whole process is manual.
   
2. Human + Agent - the agent gathers insights, the human still decides.
   
3. Agent-first - 80 - 100 % of the workflow runs autonomously. Humans step in only where judgement or missing data demand it.
   
   

6. A specific, simple example: Leave-request approvals

Stage: Manual (Human only approach)
What really happens: The manager chases down data in multiple UIs, then approves or rejects.

Stage: Assisted (Human + AI agent approach)
What really happens: The manager asks an AI agent for PTO balances, team workload, project timelines. Still clicks Approve.

Stage: Autonomous (AI agent only approach)
What really happens: The AI agent fetches all data, applies the decision rules and finalises the request -> no human in the loop.

7. Other high-impact use cases examples

• New-hire onboarding
• Invoice processing
• Tier-1 tech support
• Essentially anything where clear rules meet reliable data
  

8. Cost & security myths—debunked

• Cost: You no longer need a battalion of engineers or months of dev time. With modern APIs, certain automation is orders of magnitude cheaper than even two years ago.
  
  
• Security: Major LLM providers do not train on data sent via their paid APIs. On-prem models are possible for extreme cases, but for most teams the cloud is safer, faster and more economical (at the moment).
  
  

9. Three take-home guidelines

1. Start small, smart. Map a single bottlenecked process in your company and wrap a lightweight AI agent around it.
   
2. Chase outcomes, not buzzwords. If an AI pilot can’t articulate the business value, pause and rethink.
   
3. Explore now, not later. The AI age is here—and it’s accelerating.
   

10. How we can help

At Moravio we live this every day. Designing, building and shipping AI automations and AI agents that actually ship. Whether you’re still evaluating possibilities or knee-deep in an implementation, we’re happy to jump in.

Thanks for having us, Brno. Great crowd, sharp questions. Onward to the next stage—and the next AI agent.