The AI Era: Why Today Is Your Worst Day with Technology — and the Greatest Opportunity

Why today is the “worst” day with AI

If AI annoys you today, it’s not proof that “it’s just hype”. It’s paradoxically good news: much of what we consider the limit of possibility today will be considered basic a year from now. Not because the future can be predicted, but because the pace of improvement is visible in measurable data (firm adoption, model quality, productivity impact). TopicAI era of leadersis key today.

Fact (adoption):in OECD countries, around 20.2% of firms reported using AI in 2025 (up from 8.7% in 2023), more than doubling adoption in two years.

Fact (competition and quality):in the global AI competition, the performance gap between top models (on selected benchmarks) narrows measurably, while development becomes more and more global and competitive.

Fact (productivity already today):experimental study published inScienceshowed that access to a chatbot in written professional tasks reduced time by ~40% and increased quality by ~18% (in a given task type and setting). That’s not a promise. This is a measurement in a controlled experiment.

And now the most important “reset” for every manager: big language models are not magical thinking beings. They are high performance systems that are trained to predict the next token/word in the text.  This explains two things at once:

• Why they can sound convincing even when they are not right (hallucinations are a real risk in practice).
• Why “technology” itself is still just a tool: without strategy, data and quality control of outputs, you are planning to produce chaos.

AI era of leaders: Technology is not strategy, so start with an automation portfolio

In AI marketing, the biggest trap is this: buy the tool before you know it,what exactly should change in the results of the company.

That is why the “AI portfolio” approach is stronger for the manager than the “AI toy”. It also coincides with the philosophy of the FIRST CLASS HOLDING s.r.o. portal: first specify the goals, then choose the technology; technology is a means, not an end.

Here’s a practical framework that works for both small and large businesses, designed to be100% usable(not academic):

First, define 3 outputs (not 3 tools). For example:

• reduce delivery times by X%,
• accelerate the business cycle by Y days,
• reduce the cost of administration by Z%,
• increase the quality/consistency of outputs (e.g. legal contracts, reporting, customer communication).

Then make a process map according to “leaks”. In reality, most of the value lies in 5 types of leaks:

• rewriting (manual copying between systems),
• search (time lost searching for information),
• approval (waiting times),
• communication (duplicity, unclear assignments),
• quality control (repairs, complaints).

Only then design an AI portfolio in three baskets:

• Quick wins (2-4 weeks):automation without interfering with core systems (summaries, transcriptions, templates, internal FAQ, text proposals, simple workflow).
• Core processes (6–16 weeks):AI linked to data and processes (knowledge base, ticketing, CRM, invoicing, purchasing).
• Frontier experiments (quarterly):agents, multimodal workflow, autonomous decision-making with supervision.

Why does it work? Because you separate “business effect” from “technology” and manage risk.

And one more fact that decides the winners today: adaptability. In a report on the future of work, the World Economic Forum describes resilience, flexibility and agility as key differentiators for growing roles.

Five uses-cases that you can introduce without chaos and that are “managerially” relevant

Below, the use‑cases are selected to cover: strategies, data, costs, speed and growth. I make a distinction with each: what is the proven basis and where is the risk limit.

Virtual “non-executive” team member for decision-making (strategic sparring)Use: hypothesis brainstorming, pre-mortem (“what will go wrong”), scenario comparison, KPI tree proposals, preparation of documents for a meeting.Fact:LLMs can significantly speed up written and analytical tasks in specific types of work (see experiments and productivity reports).  Risk: the model can sound convincing even with mistakes; therefore, human validation before the decision (human‑in‑the‑loop) is mandatory.  Managerial point: you get a “quick mirror” on the quality of your own thinking – but you don’t outsource responsibility.

“Horizontal scanning” instead of Googling: answer‑engine with citationsA classic search gives a list of links. Answer‑engine goes straight to the summary + sources. Example tool: Perplexity (own documentation states that answers include citations to the original sources so they can be verified).  Managerial advantage: in decision-making, ignorance is often more expensive than a tool license. Risk: even the answer-engine may select weaker sources or summarize poorly; discipline the team: “no decision is made without a quote”.

Corporate knowledge base from your documents: less hallucination, more truthThis is most often a “gold mine” for companies, because most of the time is wasted on internal searches: manuals, SOPs, contracts, meeting minutes, onboarding, know-how. Example direction: “I work only from your sources” solutions (eg NotebookLM) – they aim to respond contextually based on embedded materials, including formats such as documents and presentations.Fact (scientific basis):retrieval‑augmented generation (RAG) combines text generation with external memory/resources, increasing the chance that the answer will be anchored in specific documents.  An important limitation: RAG reduces the risk of hallucinations, but does not guarantee zero errors (wrong source = wrong response). Therefore, operational discipline is key.

Law and Administration: Speed, Standardization, Risk ControlFirst the truth: legal texts are not just “text”. They are risks, liabilities and interpretation. Therefore, the management model is correct as follows: AI makes the first version + checklist, the person signs and bears responsibility.Fact:in practice, companies are already reporting time savings in internal legal teams when using Generative AI for routine tasks (drafting/review/research), but at the same time there is a growing emphasis on risk management and quality.  Risk: sensitive data and compliance. In addition, the risk framework of AI regulation applies in the EU (see below).

Audio-visual content transformation: from 90 minutes to 9 minutes without losing directionThe manager does not need more information. He needs faster understanding. That’s why the principle: “convert the resource into a format you can consume” is useful. Example: generating podcast-style summaries from sources in NotebookLM (Google describes this as a feature that creates an audio summary from documents).  Profit: quick decision making, quick onboarding, quick team education.

Additional tip “for the brain” (not for the algorithm): if you need quality of thought, not just speed, a short analog phase (paper/pen) before prompting helps. Research has long shown the advantages of handwriting over keyboarding in certain types of learning and information processing.

How to do it safely: hallucinations, regulations, reputation and the “deepfake world”

The biggest AI management mistake isn’t technical. It is procedural: to let the output out without control, without rules and without a risk owner.

Fact (risk management):The National Institute of Standards and Technology’s AI RMF framework describes the need to manage the risks of AI systems and directly highlights typical sources of exposure (including hallucinations, robustness, security, impacts).

A practical principle that saves a reputation: “AI is a talented intern, not a responsible manager.” This means:

• every external output (customer, media, contract, finance) must have human validation,
• internal outputs can be “faster”, but always with a clear indication of sources and uncertainty,
• measure error rate as consistently as speed.

Fact (regulations in the EU):The European Union has an effective AI Act that establishes risk levels and an implementation timeline (eg bans on certain practices from February 2025; GPAI rules from August 2025; full application from August 2026; selected high‑risk rules with a longer transition).  From the manager’s point of view, it is especially important:

• whether your use-case falls under “high-risk” (e.g. tools in HR/employee management) and what are your responsibilities,
• transparency in generative content (marking, identifiability),
• requirements for human supervision, documentation, data quality, cyber security.

Fact (information risks):The World Economic Forum repeatedly lists disinformation/misinformation as one of the most serious near-term risks in its global risk assessments.  For the company, this means: reputation will increasingly depend on whether you can prove the origin of the content and whether you have internal publishing rules.

Fact (content provenance):The Coalition for Content Provenance and Authenticity promotes an open technical standard for proving the origin and modification of digital content (so-called Content Credentials).  Executive translation: in the deepfake world, the companies that will be able to say “this is original, this is modified, this is AI” will win – and they will know itdocument, not just claim.

Fact (voice and cloning):voice synthesis tools are already usable by businesses, but at the same time they represent a reputational and legal risk (fraud, imitation). This is also why the platforms have strict rules of use (e.g. prohibition of political impersonation or illegal activities) and publish security frameworks.  Example platform: ElevenLabs.

And an important “caution” point that companies often forget: AI is not just an IT project. It is a project to change people’s behavior. The portal firstclass.sk also describes that readiness, low-risk pilots with success criteria, open architecture and change management are important when adopting AI.

Vision: from assistants to agents and why the market will change before you can approve the budget

Today, many companies use AI as an “assistant”: answer, summarize, suggest. This is useful, but it is only the first league.

Next up is the “agentic” model: the system has a goal, uses tools, performs actions. And here everything is changing: internal workflow, customer journey, purchasing, reporting, even how companies “negotiate” with each other.

Fact (definitions):Anthropic describes the difference between “workflows” (prescribed procedures) and more autonomous “agents” (systems capable of working longer independently with tools).  Similarly, the difference between an assistant and an agent is also explained by large technology companies: the assistant is typically reactive, the agent more autonomous and goal-oriented.

Scenario (probable trend, not certainty):in many companies, within 12-24 months, the model “AI as a role in the org structure” will be expanded – not as an app. Something like “process agent” for orders, “sales agent” for qualifying leads, “finance agent” for checking invoices. Why? Because the pressure on productivity and the adoption of AI are growing across economies, and it will spill over from pilots to operations by sector.

What follows from this for the manager already today:

• Your digital world must be “machine-readable” (quality data, clear processes, API/integration readiness).
• Your content and offer must be explainable: not only for people, but also for their “AI search” (answer‑engines and company agents will compare suppliers through sources and facts).
• The biggest competitive advantage will not be “who has the better model”, but “who has the better processes and data”.

Three questions that will save you months and dramatically increase the chance that the AI ​​will work

Below are three questions that – if you have a clear answer to them – turn AI from a marketing topic to an execution advantage. I also provide answers in the form of applicable frameworks (not theory).

Q: If there were only three things I could automate that would directly improve EBITDA or cashflow, what would they be and why?Answer (framework): start with a “money map”, not a tool map.

• Collect 90 days of data (orders → delivery → invoicing → complaints).
• Identify where the greatest combination occurs: (a) volume, (b) repeatability, (c) error rate, (d) time loss.
• Choose 3 processes with the highest “return on attention”.

Why it works: Generative AI typically delivers measurable gains first in tasks that are text-based, repetitive, and time-consuming (summaries, communications, documents, customer support) – consistent with experiments and productivity impact reports.

Question: What data is the “fuel” for our decision-making – and what quality is it realistically?Answer (framework): do a “data readiness audit” in 5 layers: owner, resource, quality, access, security.

• Owner: who is responsible for definition and quality (not who has the password).
• Source: where the truth arises (CRM, ERP, e-shop, production).
• Quality: duplicates, missing fields, inconsistent names, out of date.
• Access: who needs what and why (minimum rights).
• Security and compliance: personal data, sensitive data, logging, internal rules.

Why it’s key: Both regulations and risk management frameworks emphasize data quality, documentation, oversight and security as the foundation of trusted AI.

Question: Where do we need 99.9% accuracy (and legal defensible) and where is 80% speed enough?Answer (framework): divide use-cases according to risk and set work modes:

• “High-stakes” (law, HR decisions, finance, security): AI generates a proposal, a human verifies, everything is logged, resources are traceable.
• “Medium” (internal analyses, reporting, preparation): AI accelerates, but the decision is human and must be explained.
• “Low” (text drafts, internal summaries): speed is OK, but AI output labeling and pre-publishing checks still apply.

Why: The EU regulatory framework explicitly deals with risk levels and requirements for oversight and transparency.

If you want to do it quickly and without dead ends, it typically pays to have a partner who knows how to combine strategy, processes, data, security and real implementation into one managed package – exactly in the spirit of “first the goal, then the technology” and “first safely, then quickly”.