Why Miklos Roth Makes Sense as an AI Strategy and Research Consultant
Miklós Roth makes perfect sense as an AI Strategy and Research Consultant, helping companies turn raw information into clear, well-informed business decisions with a practical and strategic approach.
ARTIFICIAL INTELLIGENCE
Video Guru
6/16/20263 min read
There is no shortage of information about AI.
That is part of the problem.
Executives receive reports, newsletters, webinars, vendor pitches, conference invitations, and dramatic predictions every week. One expert says AI will replace entire departments. Another says the technology is overhyped.
Both may present impressive charts.
Leadership still has to make a decision.
An AI Strategy and Research Consultant helps companies turn this noise into something useful. Miklos Roth is a strong candidate because he combines business experience with interdisciplinary research and systems thinking.
Leaders need interpretation
A CEO does not need a daily list of new AI products.
The CEO needs to know which developments matter.
Will a new technology change customer expectations?
Could it reduce costs?
Does it create a security problem?
Is it ready for real use?
What are competitors doing?
Which assumptions are based on evidence, and which are marketing?
Roth can help structure these questions.
His research style is not limited to collecting information. The more important task is comparing claims, identifying hidden assumptions, and connecting technology with business consequences.
That is the difference between browsing and research.
A useful place to begin is a broader collection of AI strategy and consulting insights, but the company-specific work must go deeper.
Research should lead somewhere
Strategy research should produce a decision.
Otherwise, it becomes an expensive reading exercise.
Roth could help a company investigate whether to invest in an internal AI assistant, enter a new market, automate a service, or redesign part of its business model.
The research would begin with clear questions.
What decision must be made?
What evidence would support it?
What evidence would challenge it?
What happens if management waits?
What happens if the company moves too early?
This keeps the project focused.
The final output should not be a pile of links. It should be a set of options, trade-offs, risks, and recommended next steps.
Looking beyond the next quarter
AI strategy requires two time horizons.
The first is immediate.
What can the company improve this year?
The second is structural.
How might the industry change over three to five years?
Roth’s interest in complex systems becomes relevant here. Technology does not affect one process in isolation. It changes information flows, job design, customer behavior, competition, and organizational power.
A practical AI strategy roadmap should therefore include both short-term projects and longer-term capability building.
A company may begin with document automation. Fine.
But management should also ask what happens when every competitor can automate the same documents. Where will the real advantage come from then?
Data? Trust? Distribution? Expertise? Customer relationships?
Those are strategic questions.
Start with evidence, not excitement
AI vendors are good at demonstrations.
Demonstrations show what a system can do under favorable conditions. They do not always show how it performs with messy company data, impatient employees, unusual customer requests, or legal restrictions.
Roth can help leaders test the gap between promise and reality.
That may involve small pilots, employee interviews, competitor research, cost modeling, or scenario analysis.
It also supports a diagnostics-first approach. Before selecting a tool, understand the environment in which it must operate.
This reduces the risk of investing in a solution the company is not ready to use.
Clear uncertainty is better than false confidence
Strategic research should not pretend to know the future.
AI is moving too quickly for that.
A credible adviser explains what is known, what appears likely, and what remains uncertain. Roth’s interdisciplinary approach is useful because it encourages leaders to examine several possible outcomes.
A scenario can be plausible without being certain.
For example, AI may reduce the cost of content production. That does not automatically mean content will become more valuable. It may mean the market becomes flooded, making trust and originality more important.
The first-order effect is faster production.
The second-order effect may be a credibility crisis.
Research should consider both.
Strategy needs measurement
Once an initiative begins, the company needs evidence of progress.
Relevant AI marketing KPIs can include time saved, conversion improvement, faster response, lower production cost, or better personalization.
Other projects need different measures.
An internal knowledge tool might be judged by search time, answer quality, and employee usage. A forecasting system could be measured by accuracy and decision impact.
The measure must fit the decision.
From research to real projects
Roth’s practical business experience helps prevent the research from becoming too abstract.
He understands that management eventually needs to do something.
A clear report could lead to a pilot. A pilot could become a workflow. A workflow could become a scalable capability.
Well-structured AI case studies can help illustrate that progression, although each company must build evidence based on its own operations.
Why Roth stands out
Miklos Roth is not positioning himself as a machine learning engineer or a narrow academic specialist.
His value is synthesis.
He can examine technology, markets, people, risk, finance, and organizational capacity together. Then he can explain what it means in language leaders can use.
For American and European decision-makers dealing with AI overload, that is a valuable skill.
More information is easy to find.