That's true. The year is 2026, and someone still has to sit down and manually copy data from one system to another. You're not the only one in this — it's happening everywhere, including mega-organizations that invest heavily in AI. A recent survey found that only about 40% of those who began implementing AI solutions saw an actual improvement in cost savings, and even in those cases, the savings were no more than about ten percent. On top of that, roughly half of small business owners still describe themselves as just "experimenting" with AI, without it actually doing the day-to-day work. Further reading — an article published in TheMarker (Hebrew).

Many companies and organizations implemented AI-based solutions, only to end up disappointed with the results. About 48% reported that their main obstacle was scattered, inconsistent data — in plain terms, "a mess across their information systems." The logic is simple: even if you bring into your business a smart tool that can perform complex operations, it can't be accurate or reliable as long as it's running its calculations on faulty data. That's why I put special emphasis on the data-review stage in my process: it's not a prep step before the "real work" — it's an essential part of the path to success. More on how to properly organize your data ←

Alongside that, it's important to remember that AI isn't a perfect technology. It's prone to hallucinations, mistakes, and sometimes does things that are just plain strange — much like a person. Any workflow that incorporates AI has to account for that, and include mechanisms for double-checking or human intervention. Further reading.

The simplest and safest way is to start small. Every business or organization has to deal with repetitive manual tasks that eat up valuable time — things like typing the same piece of data into several places, copying data from one table into a periodic report, finding duplicate entries, summarizing documents, and so on. The idea is to pick one such task that's relatively easy to automate, define a process for it that includes a self-check mechanism, and once it's set up and working, watch the results over time. Working well? We move on to the next thing. Further reading.

Almost anything is technically possible — it's all a question of time and price. A few hundred shekels, or many thousands? The answer depends on the specific solution chosen for you. Sometimes a cheap, simple solution that covers 70% of the problem is the right choice, rather than a "perfect" solution that's more complex to build — and therefore more expensive.

There are almost always two layers of cost: a one-time setup cost (building the solution, connecting the systems), and alongside it an ongoing cost, if subscriptions to complementary services are needed. Another thing worth watching out for is over-enthusiasm: new AI tools promising solutions to common problems pop up online every day, and without noticing, you can end up paying a bit of subscription fee here, a bit there, until it adds up to a meaningful chunk of your budget (you can find an example of exactly that here).

Part of my job is exactly to prevent that from happening to you: choosing in advance only the tools that are truly necessary, and building a realistic budget that accounts for ongoing costs too — not just the initial investment.

You can significantly reduce the chances of errors and failures, but you can't guarantee 100% success 100% of the time. In fact, the one thing you can guarantee in advance is that at some point, something will go wrong. Don't forget that every piece of technology, without exception, is:

• Sensitive to power outages
• Prone to server crashes
• Occasionally hit by failed version updates
• Exposed to cyberattacks
• A favorite target for abuse by especially energetic house cats

My suggestion is not to treat AI as a flawless super-intelligence, but as a smart, hardworking team member who usually does excellent work, but every once in a while gets confused, or doesn't show up because she's not feeling well.

The decision is entirely yours, of course. But if you put together everything that comes out of the research and real-world experience, you can define four principles that significantly increase your chances of success:

• Bring comprehensive order to all your knowledge bases and data
• Precisely define the problem that's eating up too much time and resources
• Build a solution specific to that problem — within your budget
• Keep your finger on the pulse — regularly check the AI's output, and make sure it's not making mistakes in the places that really matter for your business