AI as an Operational Advantage: Koren Aisenstadt on Why Hype Is Not a Technology Strategy
On June 17, the Baltic Business Club hosted a business visit to the Amazon / AWS office in Tallinn, focused on the practical application of artificial intelligence in business. One of the featured speakers was Koren Eisenstadt, founder of Kortaliya and former Vice President at Pipedrive, who presented “Hype Is Not a Strategy: AI for Operational Advantage.”
His central message was straightforward: AI is not a strategy in itself. It creates value only when it is tied to real operational challenges, backed by solid technology expertise, and implemented with discipline.
Why “How Can We Use GPT?” Is the Wrong Starting Point
Successful companies are built on deep expertise. They understand their customers, markets, operations, risks, and how work actually gets done. They have established responsibilities, standards, operating rhythms, and years of accumulated management judgment.
That is what makes these businesses successful – not because they experimented with a few tools, but because they developed a deep understanding of their industry and learned how to run their operations professionally.
Yet when the conversation shifts to AI, automation, or internal software, that logic often disappears. Suddenly, the assumption becomes that adopting the most visible AI tool is enough.
“Can GPT help us become more efficient?”
It’s a perfectly reasonable question, but it already frames the problem incorrectly.
Technology, including software engineering, automation, data systems, and artificial intelligence – is a professional discipline, just like finance, manufacturing, logistics, or sales. It has its own methodologies, architectures, implementation practices, quality standards, and accumulated experience.
Without that expertise, it is difficult to determine where AI is genuinely the right solution, where automation would be more appropriate, where system integration is required, where custom software is necessary, or where the underlying business process itself needs to change first.
A better question is: Where are we losing time, quality, visibility, or operational capacity, and which technology solutions can solve those problems?
Answering that question requires more than business expertise.
It also requires technology expertise: an understanding of how modern software systems, automation, data platforms, and AI can be applied in real business environments, and how to translate operational problems into the right technological solutions.
The Companies in the “Middle”
This challenge is particularly relevant for established small and mid-sized businesses in traditional industries.
We’re not talking about technology companies. Nor are we talking about solo entrepreneurs with limited budgets, or large enterprises with CIOs, software architects, engineering teams, data specialists, and mature technology governance.
We’re talking about businesses that already operate at a serious scale. They have customers, employees, revenue, obligations, established processes, and operational complexity. They are large enough for better technology to create significant value, but often not yet large enough to have in-house expertise in software development, automation, data engineering, and AI.
This creates an important gap.
The business has become complex. Technology can clearly improve operations. But there is often no one inside the organization with the expertise to determine which technologies are needed, where they should be applied, in what sequence, and how they can be implemented without creating additional complexity.
Not Every Business Problem Is an AI Problem
Once you recognize that gap, the conversation changes.
The question is no longer “Can AI do this?”
Instead, it becomes: What problem are we solving, and what type of technology is best suited to solve it?
Sometimes AI is exactly the right answer – for example, when people spend significant time reading, summarizing, classifying, comparing, or explaining unstructured information. In these cases, AI can eliminate repetitive manual work while giving employees a powerful productivity tool.
But if employees spend their day copying information between systems, that usually isn’t an AI problem. It’s an integration or automation problem.
If managers lack visibility into operational status, the issue may be reporting, workflow design, or data quality.
- If an entire process depends on one employee, the problem is often not the absence of AI – it is the absence of documented processes, clear ownership, and effective knowledge transfer.
This is why professional technology adoption starts with classifying the problem:
- Is it a process issue?
- A data issue?
- An integration challenge?
- An automation opportunity?
- A user interface problem?
- A reporting gap?
- An AI use case?
- A custom software requirement?
Or simply an organizational responsibility issue?
Some of the greatest value comes not from adopting the newest technology, but from applying the right technology to the right part of the business.
What Companies Typically Try Without This Expertise
Most leadership teams already understand where their operational pain points are.
They know which processes are slow, where data is copied manually, which reports take too long to produce, where mistakes happen, and where management lacks visibility. Often, they already have good ideas about what should improve.
The challenge comes one step later: How do you translate business knowledge into the right technology solution?
In practice, three common approaches emerge:
1. The Hype-Driven Approach
“AI is everywhere – show us how it can help our business.”
This isn’t necessarily the wrong request. It usually reflects a genuine business need: processes are too manual, too slow, or too dependent on specific individuals.
The problem is that the company doesn’t yet know where AI genuinely creates value, where it doesn’t, or what is required for a successful implementation.
2. Developing Internal AI Champions
Another common approach is: “Train our best people, and they’ll introduce AI across the company.”
This can be extremely effective.
Internal champions already understand the business, its processes, and countless operational details that external consultants would need months to learn – and may never fully understand.
With proper training, these employees can quickly validate ideas, build lightweight prototypes around their own workflows, and identify practical use cases from inside the organization.
However, learning AI tools is only the beginning.
Developing real technology expertise takes time, hands-on experience, and professional guidance. A mature understanding of software architecture, implementation quality, and system design doesn’t emerge after a single workshop.
Internal champions still need coaching to understand where experimentation is appropriate – and where professional engineering, architecture, governance, and production-grade implementation become essential.
3. Outsourcing Implementation
The third option is hiring software developers, an agency, or an external technology partner to build the solution.
This can also be the right decision.
However, someone inside the business still needs to define the problem professionally, select the appropriate solution, establish clear requirements, understand the architecture, and ensure that every technical decision supports the broader business process.
External partners can build integrations, automation workflows, reports, software modules, and AI solutions.
But responsibility for the overall technology landscape should remain with the company itself.
The business – not the vendor – must own the understanding of the problem, priorities, roadmap, requirements, architectural direction, and decisions about how systems should work together.
Technology Implementation Is a Professional Discipline
Just as successful businesses don’t build their operations on good intentions alone, technology implementation shouldn’t rely on enthusiasm and random experimentation.
Effective implementation follows a disciplined process:
- First, the business problem must be understood in context.
- Next, the current process must be documented clearly enough to improve.
Only then can the right solution be selected – whether that’s AI, automation, system integration, commercial software, reporting, custom development, or process redesign.
After that come requirements, architectural decisions, prioritization, implementation planning, quality assurance, and user adoption.
A system that people don’t trust or don’t use – creates no value.
This is more than project management.
It is the practical discipline of translating business reality into technology that actually works.
Operational Advantage
When technology is applied this way, the benefits become tangible:
- Manual work decreases.
- Management gains better visibility.
- Errors are identified earlier.
- Planning becomes more accurate.
- Customer communication becomes more consistent.
- Employees spend less time moving information between systems.
Perhaps most importantly, the business becomes less dependent on individual “heroes” who carry critical knowledge in their heads.
That is what operational advantage really means.
Not AI for AI’s sake. Not software for software’s sake. Not digital transformation as a slogan. But the thoughtful use of AI, automation, data, software, and technology to make the business fundamentally more effective.
The Key Takeaway
AI often starts the conversation, but the real opportunity is much broader.
For established small and mid-sized businesses, the most important question is not which AI tool to try next. The real question is how to combine deep business expertise with equally strong technology expertise.
The companies that gain the greatest advantage from AI will not necessarily be the ones experimenting with the most tools. They will be the companies that understand where technology truly belongs within their operating model, which business problems it should solve, and who is professionally responsible for making it happen.
That is how AI curiosity becomes an operational advantage.
This article summarizes the core insights from the Baltic Business Club meeting. The content is for informational purposes and reflects the practical frameworks shared by the invited experts and club members.
