So you want to start AI transformation in your company? Read this first ->

We're oversaturating the product space but falling short in the use cases space.

It's inevitable, as that's just how most operate - the idea is born in a more or less vacuum. Usually, it happens when you notice a cool thing that can be reused or reframed to do something else. That's natural, as it's much easier to figure out a feature than a problem, let alone formulate a precise description and a coherent solution.

So, nothing changes here. The more I encourage you to focus on the case when thinking about AI.

Yes, it's a tool and it can be used in many places.

No, it can't be everywhere, not with the current capabilities. It must be put in the right place and against the right problem.

It should be a specific one, yours, unique.

Here are some key thoughts:

• Focus on AI as a functionality within a broader service, e.g. an LLM as a tool to transcript and summarize meetings or a user <-> app interface for SQL query creation.
• Define AI’s role as a core product, feature, or problem-solver while avoiding commoditized and gimmicky applications.
• Assess AI expertise, tools, budget, and business revenue to determine feasibility.
• Establish strong data acquisition, processing, and storage, as quality data is key.
• Solve real business problems, prioritize user value, and stay adaptable.

AI and software - a story as old as time

You might say AI is just a software, and you'd be mostly right. If you perceive it like that, you might have a better chance of doing something useful with it.

What I mean is AI is not a standalone product. It might be a core functionality (ChatGPT or DALLE) or a minor functionality (AI-powered Excel formula generator). Still, there has to be a system where AI operates and communicates with the user.

Let's take a look at some of the most common AI uses:

- Image recognition

- Text-to-speech conversion

- Language translation and learning

- Data analysis (e.g. pattern recognition)

- Text correction and refinement

- Research and content synthesis

Sure, each use case is cool, but without a broader business (and user-related) context, it's useless.

And that's your main goal as a business leader working within an organization - to create a product that addresses a real problem.

Addressing a real problem, aiming for success

It cannot be emphasized enough - pinpoint the use case first.

Start with an honest answer to these three questions:

Is AI the product itself?

If it is, you might be dealing with a horizontal use case. In this case, you should always start with a thorough market analysis.

These types of products require enormous resources to be developed, and there's a chance someone has already dedicated a whole company to build such a tool so you don't have to.

The only proof you need is to consider how many companies just evaporated after OpenAI released their web browser agent or tasks app.

Is it gimmicky or serious?

Don't get me wrong; I don't say that your AI idea is shallow and cannot bring value. I strongly encourage you to think about it critically to make sure a good product will be the outcome.

A gimmicky AI feature won't have much value in the wrong context.

Does it solve a problem specific to your organization (or just a handful of others in the same space)?

We'll go through the problem-solving principle for a while.

There must be a solution-oriented mindset (or perhaps it doesn't, but it's your best shot).

If you're not sure about how the solution you designed fit into the bigger picture, it's not lost yet, as you can validate the idea through a quick system design + PoC development session.

We also do this, and it's called AI Navigator (learn more here -> https://ai.sparkbit.pl/ai-navigator)

Assess your resources and in-house competence

The likelihood of shipping a product after the idea is validated increases greatly with each seasoned team member.

First, each business stakeholder involved (and they should be directly involved) in the development process should know what you build, why you do it, and how it should be done.

Second, there's a broad expertise and domain expertise. In most cases, you should seek broader knowledge and skillset, with most people on the team knowing:

- Some system design and architecture

- Some frontend

- Backend

- AI engineering

- Data handling

- Working with APIs

Versatility is good as it will also highlight specific areas where you'll need expert help.

Third, the budget. It might be a limiting factor, so it's best to prepare. Once again, some of our AI Navigator principles are to equip you with knowledge of how the system will look, the development roadmap, and estimated costs.

Also, a lower budget doesn't rule out development. You have to cut some nice-to-have but non-critical functionalities.

Building AI software, finally

As always, there are two paths - developing AI from scratch or leveraging existing AI platforms. While building your own model might seem attractive, your priority should be solving a real business problem (yes, again).

Choosing the right approach requires understanding key challenges:

• AI advances fast, and a year of development can be outdated overnight by a major tech announcement. Unlike traditional software, AI development is R&D-heavy and time-intensive.
• AI specialists are in high demand, and hiring them might be costly.
• Without reliable data sources upfront, costs can spiral out of control. Even with a data lake, if you’re unsure of the next step, you may not be ready for custom AI development.

If you can’t confidently address these challenges, consider using pre-built AI solutions instead. In the early stages, focus on solving the problem efficiently rather than reinventing the wheel.

Now, here's the general plan to follow:

Step one: Research, research, research

Before building, explore what’s already available. Start with AWS, Azure, and Google Cloud, which offer powerful AI services. Look into companies like OpenAI and Anthropic, as well as open-source models for more control.

Step two: Tools and team

Opt for high-abstraction tools to avoid unnecessary complexity. If you’re not already an expert in a tool, don’t become one while scaling your business. Instead of niche specialists, hire generalists who can implement and manage AI solutions efficiently. Early on, data engineers or software developers with AI experience are more valuable than pure data scientists.

Step three: Data

A great AI model is useless without quality data. Invest in robust data pipelines to ensure a steady flow of clean, well-structured data. Good data engineering is just as important as the model itself, if not more.

Step four: Improve and adapt

AI development is an ongoing process. Regularly refine your system based on user feedback and stay updated on AI advancements to remain competitive.

Final thoughts

With AI, the non-deterministic being entering the operationalization phase, successful deployments will require a mixture of innovation and realism.

Your success depends on clear use cases, strong data management, and smart implementation. Whether you build from scratch or use existing tools, the priority should always be delivering real value. Stay adaptable, keep learning, and continuously refine your approach.

And if you need an AI development partner, we’re here to help!

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If you're looking to start an AI project, you can book a free consultation here: https://calendly.com/kornelk/ai-consultation-intro

Author: Kornel Kania , AI Delivery Consultant at Sparkbit