Category: AI

AI adoption in companies often stalls before the first experiment has even begun. The discussion starts with risks, systems and strategies, even though a far more useful question would be much simpler: what is one real problem we could solve better today?

Some time ago, one company decided it was time to enter the age of AI.

In practice, this meant that the management team began debating whether AI was safe to use.

What could it actually do?

Could it read an incoming purchase invoice from a scanning service?

Could it identify the reference number, amount and due date?

What if it read one digit incorrectly?

The questions were entirely valid. In financial administration, a system that is almost right is not much use.

But there was also something very human about the discussion.

Before AI had processed a single invoice, it was already being assessed as though it were applying for the role of chief financial officer.

“A computer will never match a human”

One employee following the discussion laughed at the whole idea.

“Artificial intelligence? Exactly. Artificial stupidity. A computer will never match a human.”

From their own perspective, they had evidence to support the claim.

Over the years, a highly advanced information management system had developed around their workstation.

There were an estimated 78 kilograms of printed paper on the desk: stacks, binders, plastic sleeves and loose documents wedged between them.

The remaining 42 kilograms had been arranged on the floor around the chair in stacks of precisely calculated height.

The semicircular archive had been designed ergonomically.

When the user swivelled their chair to the correct angle, they could usually reach the desired stack of papers in under a minute.

The system was completely independent of an internet connection.

It had no licence fees.

It required no software updates.

From a cybersecurity perspective, it was difficult to hack remotely.

And no AI had yet managed to move the wrong stack of papers by accident.

On closer inspection, it was therefore a remarkably reliable solution.

Its only weaknesses were search, backups, information sharing, version control, fire safety, and the fact that the entire user interface was stored inside one person’s head.

This is not a question of humans versus machines

When discussing AI, the debate is easily framed in the wrong way.

Can a computer match a human?

Can AI do the same job as an experienced employee?

Which one is better?

In most companies, this is not the first question that needs to be answered.

A far more useful question would be:

In which task is a person currently spending time on something a machine could do quickly – and that a person could verify even faster?

Reading a purchase invoice reference number is a good example.

The task does not require AI to have deep business understanding, ethical judgement or decades of experience.

It needs to recognise a sequence of numbers.

And because recognition can fail, the result is checked or validated against existing rules.

AI does not need to be perfect.

It needs to be good enough at the specific stage of work where it is used, and the process must identify when a human needs to step in.

This is a far more realistic starting point than asking whether AI can replace a person.

Meanwhile, elsewhere

While one room is debating whether AI can safely be trusted to read a single reference number, another working environment has already been transformed completely.

When building a website, a person takes a screenshot after almost every step.

AI examines the image and identifies:

• which change worked
• which element moved to the wrong place
• which setting should be changed next
• which browser tab is already open
• which tool will probably be needed in the next step

The image shows that the web hosting control panel is open in the third browser tab from the left.

It will be needed next to embed a custom-built booking module into the website.

The person does not first need to explain the entire working environment again.

AI reads the situation from the image and continues from where the work actually stands.

This is no longer just about producing text.

It is about interpreting the situation, anticipating the next step and continuously supporting a human-led project.

AI can also help prepare for human situations

The most visible uses of technology often involve documents, images, calculations and code.

A less visible but equally interesting application is preparing for communication and interaction.

When people work together for long enough, recurring patterns begin to emerge in the way they communicate.

One person wants a direct answer.

Another needs background and reasoning.

A third reacts strongly if something is presented as too final.

A fourth will not respond to a long message at all, but will answer three clear questions.

Based on previous communication, AI can help structure these differences and shape a message to suit the recipient better.

Not to manipulate the person, but to reduce situations where even a good idea fails because it was presented in the wrong way.

The same applies to preparing for meetings.

Before an important discussion, it is possible to consider:

• the most likely objections
• where the participants’ objectives differ
• which questions are likely to be asked
• the three different ways the discussion might develop
• how to respond in each situation

AI cannot predict human behaviour with certainty.

It helps people prepare for more than one possible course of events.

In leadership, that is a significant difference.

A poorly prepared person reacts to what happens in the meeting.

A well-prepared person has already considered what might happen.

This is also where boundaries are needed

Using AI in communication and personnel matters requires judgement.

Not all material should be entered into just any service.

Personal data, confidential messages, contracts and trade secrets require suitable business tools, access controls and clear rules.

That is not a reason to avoid examining the subject altogether.

It is a reason to design the use properly.

Companies do not ban email simply because confidential information can be sent to the wrong recipient.

Instead, they create instructions, permissions and responsibilities.

AI requires the same adult approach.

No naive enthusiasm.

But also no blanket ban on everything new, just in case.

A grand AI strategy can be an excellent way to do nothing

In many companies, AI adoption begins with exceptional ambition.

They decide to prepare:

• an AI strategy for the entire group
• a comprehensive risk assessment
• a complete technology architecture
• a training programme for the entire workforce
• a list of approved use cases
• a multi-year implementation roadmap

All of these may become necessary later.

But if the first practical experiment is postponed by six months, strategy work can become a sophisticated mechanism for delay.

AI is discussed extensively.

New boxes appear on presentation slides.

A steering group is established.

One member of the management team attends a seminar.

And the same employee continues copying information from one system to another every morning.

The company does not first need a perfect description of everything AI might someday do.

It needs one controlled experience of what AI can do now.

Start with a task whose result a person can verify

A good first experiment is small, repetitive and sufficiently safe.

It could be, for example:

• extracting data from purchase invoices
• creating the first draft of meeting minutes
• summarising a long report
• grouping customer feedback
• comparing information in an offer against requirements
• finding an internal instruction within a large collection of documents
• preparing for a sales meeting
• creating the first version of website copy
• prototyping a simple tool

In a good pilot:

1. the task occurs often enough
2. it clearly consumes time
3. the source material is available
4. the result can be checked
5. an error will not cause uncontrolled damage
6. the benefit can be measured with a simple metric

The first experiment does not need to prove that AI will transform the entire company.

It only needs to answer one question:

Is this worth continuing?

Measure ordinary things

The success of an AI pilot does not need to be measured with a complex AI index.

You can ask:

• how much time was saved
• whether manual work decreased
• whether information became easier to find
• whether people had more time for evaluation
• whether fewer errors occurred
• whether the work genuinely became easier
• whether the result was good enough for continued use

If processing a purchase invoice used to take three minutes and now takes one, the benefit can be calculated.

If preparing for an important meeting previously produced one plan and now produces three realistic scenarios, the benefit is visible in decision-making.

If a small technical website change previously required an external developer and can now be completed under supervision in fifteen minutes, the impact is visible in speed and cost.

Not all value needs to be described in futuristic terms.

Sometimes the best business benefit of AI is simply that ordinary work runs a little better every day.

Experimenting does not mean losing control

Corporate caution is understandable.

Mistakes happen.

Information can leak.

AI can produce convincing-sounding nonsense.

But people also misread reference numbers, save files in the wrong folders, forget meeting decisions and make choices based on incomplete information.

The goal is not to replace human imperfection with a perfect machine.

No such machine exists.

The goal is to build a process in which the strengths of people and technology complement one another.

AI processes large volumes of information quickly, identifies patterns and generates alternatives.

A person understands the situation, assesses the consequences, makes the decision and takes responsibility.

Perhaps the greatest risk is not an incorrect reference number

Companies should consider what happens if AI reads one piece of information incorrectly.

But an equally important question is:

What happens if we never learn to use it at all?

In that case, a competitor processes the same information faster.

Another company builds a service while your own organisation is still discussing it in a working group.

One employee multiplies their productivity while another continues searching through piles of paper.

No technology removes the value of good work, experience or judgement.

But history offers very few examples where systematically resisting a more effective tool became a long-term competitive advantage.

Start with the problem

The first AI project does not need to be impressive.

It does not need to transform the entire organisation.

It does not even need to be visible to the customer.

Choose one real problem.

Define the scope of the task.

Define what success means.

Protect the data.

Keep a person involved in checking the results.

Experiment.

Measure.

Then decide what should be done next with what you have learned.

A company does not enter the age of AI when the letters AI are added to a management presentation.

It enters the age of AI when the first real problem is solved in a new way.

AI has become part of all kinds of situations: from counting aquarium fish to designing a hi-fi system, from calibrating a pH meter to building a company website. The biggest change is not any single trick, but the way I now approach problems.

AI did not enter my life as one great revolution.

It first arrived through small questions.

Could you compare these options?

What does this measurement result mean?

How should this be built?

Can you identify this device from the picture?

Then the questions began to change.

I was no longer asking only for information. I began asking AI to interpret images, assess videos, understand technical systems, design solutions and build them with me.

At some point, I realised that ChatGPT was no longer merely a more versatile search engine for me.

It had become a thinking partner.

The same tool, completely different problems

When people talk about AI, the discussion easily focuses on big themes.

We talk about disappearing jobs, automation, major IT projects and innovations that transform entire industries.

These are important topics, but based on my own experience, the impact of AI appears somewhere entirely different, at least at first.

It appears in small moments.

In situations where I would previously have searched for a manual, browsed discussion forums, called someone more knowledgeable or postponed the whole matter.

Now I can take a picture, describe the problem and start working it out immediately.

A pH meter in the morning

One morning, I had an aquarium pH meter in front of me.

It had recently been reading slightly too high, so I had cleaned the sensor, kept it in storage solution and finally placed it in a pH 7.0 calibration solution.

The device still needed to be calibrated.

The problem was small but practical: where was the adjustment made?

I took a picture of the meter and sent it to ChatGPT.

From the image, the device was identified as a Milwaukee pH51 meter. A second image revealed two adjustment screws and markings that referred to the pH 7 and pH 4 calibration points.

I could continue without searching for manuals or comparing different model versions.

AI did not, of course, calibrate the meter for me. I prepared the device, made the adjustment and checked the result.

But AI quickly brought me to a point where I knew what I was doing.

Moments like these have changed the way I solve problems.

Previously, my first question might have been:

Where can I find the manual for this?

Now it is often:

What can we infer from this image?

Counting aquarium fish from a video

Breeding aquarium fish presents problems that may not have a ready-made manual.

One of them is estimating the number of fry.

A single image can provide a rough idea, but a video reveals more.

Using video, we have estimated the number of fry, examined their movement and feeding behaviour, and followed the development of their colouring.

AI’s estimate in a situation like this is not an absolute truth. Counting moving fish always involves uncertainty.

Even so, it can provide a much better starting point than a quick visual guess.

More importantly, the video can be examined from several perspectives:

• approximately how many fry are in the tank
• whether they are distributed evenly
• whether they appear to be feeding actively
• whether there are visible differences in size
• how their colouring and fins are developing
• when the available space is beginning to become too small

AI does not replace the breeder’s experience.

It helps direct attention to the right things.

A hi-fi system is more than a pile of equipment

Another completely different application is hi-fi.

I have spent a long time designing and building different loudspeaker solutions, room acoustics and electronic room correction. In projects like these, an individual question is almost always connected to a larger system.

Loudspeaker placement is affected by factors such as:

• the dimensions of the room
• the listening position
• reflections from the walls and ceiling
• the directivity of the drivers
• crossover frequencies
• delays
• the characteristics of the amplifiers
• MiniDSP settings
• measurement results

A technical answer to one individual point may be found quickly. The more difficult part is seeing how the solution affects the entire system.

That is precisely where AI has been very useful to me.

We have been able to examine loudspeaker structures, compare different implementation methods and consider how acoustic solutions, DSP settings and physical placement affect one another.

Sometimes AI acts as a calculator.

Sometimes as a critic.

Sometimes it helps put into words a problem that I have already noticed but have not yet been able to define precisely.

Sometimes the most useful question is not:

Which crossover frequency should I choose?

But:

What are we actually trying to correct with this adjustment?

This distinction matters.

The first question seeks a number.

The second seeks understanding.

AI does not need a predefined box

People tend to divide their expertise into separate areas.

Work matters go into one box.

Hobbies into another.

Technology into a third.

Creativity into a fourth.

From AI’s perspective, these boundaries are not as rigid.

Calibrating a pH meter, counting aquarium fish, designing a hi-fi system and building a website may look like completely different tasks on the surface.

Yet they involve many of the same stages:

• identify the problem
• collect observations
• compare alternatives
• form a hypothesis
• test the solution
• evaluate the result
• adjust the direction

AI’s strength is that the same conversation partner can participate in all of these stages.

In the morning, it can help identify a calibration screw.

During the day, it can interpret a loudspeaker measurement.

In the evening, it can help design a company website.

The tool does not need to change every time the subject changes.

Small questions can lead to large projects

Using AI often starts with something very small.

You ask one question.

You receive an answer.

You ask a follow-up question.

Then you realise that the same matter could be developed further.

This has happened to me several times.

An individual technical problem has grown into an entire plan.

The plan into a prototype.

The prototype into a working solution.

For example, building my own Mac application did not begin with a decision to become a software developer.

I had a practical need to monitor the computer’s temperatures, memory usage and other system information more effectively.

The need led to a conversation.

The conversation led to a specification.

The specification became an application built with Codex.

This demonstrates one of the most interesting effects of AI.

It shortens the distance from an idea to the first experiment.

Previously, many ideas might have stopped here:

This would be useful, but I do not know how to build it.

Now the next thought can be:

Let’s first see how far we can get.

AI can both increase and conceal competence

AI also carries a risk.

When answers arrive quickly and the instructions appear convincing, users may begin to trust them too much.

This is especially important in technical, medical, legal or financial questions, where incorrect advice can cause real harm.

AI can identify a device incorrectly.

It can interpret an image incompletely.

It can suggest a solution that is technically possible but poor in practice.

It can also write with complete confidence about something it has misunderstood.

That is why using AI does not remove the need for human judgement.

It makes that judgement even more important.

A pH meter image may show two adjustment screws. I still need to understand which one is being adjusted and why.

A new crossover frequency may be suggested for a hi-fi system. I still need to measure, listen and evaluate the result.

The number of fish may be estimated from a video. I still need to account for how many individuals may remain hidden.

An AI answer is not the end of the process.

It is often the beginning of a new check.

I did not get all the answers – I found a better way to ask

The value of AI is easily measured by how many things it can do.

For me, the greater change has taken place in how I approach things.

I have learned to break problems down more effectively.

To describe the starting point more precisely.

To explain what I have already tried.

To ask for alternatives instead of a single answer.

To ask for reasoning.

To request risks and counterarguments.

To pause occasionally and review the bigger picture.

A good conversation with AI does not arise from a person writing one perfect prompt.

It develops step by step.

Just like a good conversation with another person.

The first answer rarely solves the entire issue. It helps form a better next question.

Not an oracle, but a working partner

I do not regard AI as an all-knowing authority.

It makes mistakes, forgets things and can misunderstand a situation.

Nor do I think it replaces people.

A better description is a working partner.

A working partner that is available whenever a question, observation or idea comes to mind.

With it, I can:

• think out loud
• explore alternatives
• examine images and videos
• test assumptions
• build plans
• write and program
• return to an earlier stage and try again

A working partner has no objective of its own.

I have to bring the objective into the discussion.

A working partner does not carry responsibility.

I have to carry it.

But with good guidance, it can help me do more, learn faster and see possibilities where I previously saw only limitations.

Perhaps the greatest change happens unnoticed

The impact of AI may not appear as one major turning point.

You notice it only when you look back.

You realise that you have asked for help with dozens of matters you would never previously have considered suitable for AI.

The number of aquarium fish.

Calibrating a pH meter.

Loudspeaker placement.

MiniDSP settings.

Computer temperatures.

Website structure.

The company’s services.

Individually, these are small or medium-sized use cases.

Together, they form a much larger change.

AI did not only change the way I work.

It changed the way I face a problem.

I no longer begin by asking whether I already know how to do it.

I ask:

What can we find out about this – and what would be a sensible first step?

AI can make work considerably easier. At the same time, it can get the user to the destination without them fully understanding the route they took. That is why ChatGPT reminds me of both an automatic transmission and a satnav – in two completely opposite ways.

Over the past year, I have become thoroughly fascinated by AI.

During the past few weeks, I have used ChatGPT to build an entire website for Sulonen Solutions, plan its structure and content, and write Gutenberg code and CSS definitions. I have developed my own booking module and used Codex to build a Mac application. Among other things, the app monitors the computer’s temperatures and memory usage.

Not long ago, I would never have imagined doing these things myself.

Or wait a minute – did I actually do them myself?

“Oh, ChatGPT built your website?”

This is probably one of the most common comments related to the use of AI:

“Oh, ChatGPT built your website? Then you didn’t really build it yourself.”

It is an interesting question, because by the same logic, a large part of modern work is no longer done by people themselves.

Did you write the report yourself even though you used Word?

Did you make the calculation yourself even though Excel added up the figures?

Did you take the photograph yourself even though the camera focused automatically?

Did you build the piece of furniture yourself even though you used a power drill?

Did you drive from Vantaa to Helsinki yourself even though your car had an automatic transmission?

That last comparison probably describes my own experience of using ChatGPT best.

An automatic transmission does not drive the car for me

I learned to drive in a manual car. It is still a useful skill, and when necessary, I can change gears myself.

Today, however, automatic transmissions are so good, unobtrusive and comfortable that I can think of very few reasons why I would insist on using a manual gearbox in ordinary traffic.

The automatic transmission handles one part of driving for me. At the same time, I have more capacity to monitor traffic, observe my surroundings and focus on driving itself.

It does not, however, decide where I am going.

If I drive from Vantaa to Helsinki, I decide to make the journey. I have a reason to get to Helsinki. I choose the destination, steer the car, monitor the traffic and take responsibility for the decisions I make.

Nobody says after the journey:

“You didn’t drive to Helsinki yourself. The automatic transmission made the trip.”

No, it did not.

I drove. The automatic transmission made the journey I had chosen easier and more comfortable.

In the same way, ChatGPT did not decide to establish Sulonen Solutions. It did not define the company’s services, objectives or client promise. It did not know what the website should look like or what kind of impression I wanted the company to create.

I defined the objective.

I evaluated the suggestions.

I approved, rejected and requested changes to the solutions.

I decided when the result was good enough and when the work still needed to continue.

ChatGPT and Codex made the implementation faster. They removed technical barriers and helped me do things for which I would previously have needed several different specialists.

In this sense, AI is an excellent automatic transmission.

But the comparison has another side as well.

A satnav can get you there without teaching you the route

While building the website, I sometimes worked with ChatGPT in very small steps.

I received a short instruction. I carried it out in WordPress and took a screenshot.

ChatGPT checked the image and gave me the next instruction.

I carried that out as well and sent another screenshot.

This is how we proceeded, step by step, for several hours.

At some point, I noticed something strange: I was no longer entirely sure which stage of the overall project we were in or what we had already changed.

I had focused carefully on each individual instruction, but keeping track of the bigger picture had received less attention.

Exactly the same thing can happen when using a satnav.

The satnav says:

Turn right at the next junction.
Continue for 300 metres.
Take the second exit at the roundabout.

By following the instructions, you will usually reach your destination efficiently. But if someone asks afterwards which route you took, answering can be surprisingly difficult.

You did not follow the city, the compass directions or the overall route. You followed only the next instruction.

If you make the same journey without a satnav, you have to understand the route differently. You pay attention to roads, junctions, landmarks and the direction in which the destination lies.

The journey may require more concentration, but at the same time, you learn the route.

The same thing can happen with AI.

You can follow a long series of perfectly correct instructions and eventually end up with a functioning system. That does not automatically mean that you understand how the system works.

Did you merely arrive, or did you also learn the route?

When using AI, it is therefore not enough to ask only:

“What should I do next?”

Sometimes you also need to stop and ask:

• What have we built so far?
• Why was this solution implemented in this particular way?
• How do the different parts relate to one another?
• What changes have been made, and to which files?
• What are the risks and dependencies of the solution?
• What happens if one part stops working?
• Could I explain the whole system to another person?

I learned this during my own website project as well.

Once enough stages had accumulated, simply following the next instruction was no longer sufficient. We needed summaries, documentation, code clean-up and a fresh understanding of the whole.

In other words, sometimes I had to stop following the satnav and open the map.

AI should be used – not obeyed

The greatest benefit of a tool like ChatGPT does not come from giving the user an endless stream of instructions.

The benefit comes from the human being knowing how to lead the collaboration.

You need to be able to tell the AI:

• what is being done
• why it is being done
• what kind of outcome is wanted
• what constraints need to be considered
• how success will be evaluated

In addition, the user needs to be able to recognise when a suggestion does not match the objective.

AI can write technically functional code that still does not fit the wider system. It can suggest impressive content that does not match the company’s tone of voice. It can solve one problem in a way that creates three new ones later.

That is why responsibility does not transfer to AI.

A good driver makes use of the car’s features but continues to monitor the traffic.

A good satnav user listens to the directions but also checks the full route from time to time.

A good AI user takes advantage of the tool’s speed and expertise but does not surrender decision-making authority or responsibility to it.

What if AI did part of the work?

Of course it did.

That is exactly why I used it.

I do not use technology to prove that I can do everything in the most difficult way possible. I use it to achieve a better result faster and more sensibly.

In business, it should ultimately be fairly irrelevant how many times someone pressed the space bar or whether they wrote every line of code by hand.

Far more important questions are:

• Did the work solve the right problem?
• Was the outcome good?
• Did it save time or money?
• Did someone understand the whole?
• Was the outcome checked?
• Who made the decisions?
• Who carries the responsibility?

If AI helps one person do things that previously required an entire team, it does not reduce the value of that person’s work.

It changes the nature of the work.

Alongside technical execution, greater emphasis is placed on defining objectives, understanding complex systems, making decisions, evaluating quality and asking the right questions.

Those are also core leadership skills.

Use the automatic transmission, but do not lose the map

I am happy to use an automatic transmission. I do not need to prove to anyone that I know how to change gears manually.

I am also happy to use a satnav. Even so, I sometimes want to look at the map and understand where I am and where I am going.

That is how I want to use AI as well.

I want to take advantage of the speed, ease and opportunities it offers. But I do not want to surrender my own judgement, my understanding of the whole or my responsibility for the outcome.

AI can make the journey considerably easier.

But the human being still has to decide where we are going – and keep their hands on the wheel.