Why Miklos Roth Works Best as a Business-Focused AI Solutions Consultant
Miklós Roth works best as a Business-Focused AI Solutions Consultant, delivering practical AI solutions with strong business insight and clear technical boundaries for real organizational results.
ARTIFICIAL INTELLIGENCE
Video Guru
6/16/20263 min read
“AI Solutions Consultant” can mean almost anything.
In one company, it means a senior machine learning engineer. In another, it means someone who speaks with clients, understands the business problem, evaluates options, and coordinates the technical team.
Miklos Roth fits the second version.
That is not a limitation.
For many companies, it is exactly what they need.
The company does not always know what to ask for
A leadership team may say, “We need an AI assistant.”
That sounds clear.
It is not.
Who will use it?
What information should it access?
What should it be allowed to do?
How accurate must it be?
What happens when it gives a wrong answer?
A business-focused consultant turns the vague request into a real project.
Roth can help management define the purpose before developers start building. This is central to effective AI marketing consulting.
A solution should be designed around the result.
Not around the excitement of using AI.
Solve the right problem
A technically impressive system can solve an unimportant problem.
That happens more often than leaders expect.
Perhaps the company automates a task that takes one employee fifteen minutes a week. Meanwhile, the sales team spends ten hours preparing every proposal.
The first project looks modern.
The second opportunity creates value.
Roth can help companies prioritize AI use cases based on business impact, feasibility, risk, and time to value.
This prevents the loudest department from automatically receiving the largest budget.
Translating between people
Executives, employees, developers, and vendors often speak different languages.
The executive describes a vision.
The employee describes a daily frustration.
The developer asks for requirements.
The vendor demonstrates features.
Someone needs to connect the conversation.
Roth can do that.
His background in business, marketing, communication, and strategy helps him explain the commercial goal to technical partners. He can also explain technical limits to management without creating unnecessary confusion.
This translation role is easy to underestimate.
It often decides whether a project succeeds.
Vendor-neutral thinking
Vendors naturally recommend their own products.
That is their job.
The company needs someone representing its side.
Roth’s vendor-agnostic approach means the question is not, “How can we use this platform?”
It is, “What is the simplest reliable solution to this problem?”
The answer may be an existing AI service. It may be a custom workflow. It may be a private system. It may be standard automation with no generative AI at all.
A broader AI strategy service should leave room for all of these options.
The best solution is the one that fits the business.
Governance must be part of the design
A solution is not complete when it works in a demo.
It also needs rules.
What data enters the system?
Where is it stored?
Who can access it?
Who reviews the output?
How are mistakes reported?
This is why AI governance should be included while the solution is being designed.
Adding it later is harder.
This is especially important in Europe, but US companies also face customer trust, confidentiality, security, contractual, and reputational concerns.
Roth can help identify these issues early and bring in specialist legal or technical support where needed.
Clear boundaries make him more credible
Roth should not claim to be the senior engineer personally building every complex machine learning system.
That would weaken the positioning.
His strength is the business layer.
He identifies the opportunity. Defines the requirements. Aligns stakeholders. Evaluates options. Coordinates specialists. Supports adoption.
A specialist engineering team can then handle architecture, integrations, security testing, and deployment.
This is how many successful consulting engagements work.
Nobody needs to pretend that one person does everything.
A practical project structure
Roth could begin with interviews and process mapping.
Next, he would write a clear problem statement. Then he would compare possible solutions, risks, costs, and expected value.
A pilot would follow.
The pilot should be narrow enough to test properly. Employees would use it in real work. Errors and adoption issues would be recorded.
Only then would the company decide whether to scale.
A useful AI consulting FAQ can answer common early questions, but the real engagement must be tailored to the organization.
Why Roth is a strong fit
Many AI projects fail before coding begins.
The goal is unclear. The wrong stakeholders are involved. The company has unrealistic expectations. The workflow does not match the real work.
Roth’s job would be to prevent these mistakes.
He brings commercial judgment, communication skills, international experience, and an understanding of AI adoption.
For a business that needs help deciding what to build, buy, or automate, this is highly valuable.
The company may already have technical talent.
What it often lacks is someone who can make sure that talent is solving the right problem.