Blog Artificial Intelligence 🤖 What AI can and can’t do: Setting realistic expectations for your business

What AI can and can’t do: Setting realistic expectations for your business

Artificial Intelligence 🤖

Oleksandr Budnik

Oleksandr Budnik

Chief Technology Officer

Oleksandr is innovative, collaborative, and results-driven. Knowing that his work can transform businesses, improve lives, and push the boundaries of technology fuels his dedication to working hard and striving for excellence.

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Artificial Intelligence

June 17, 2025
Reading Time: 6 minutes

Reviewed by Karyna Hanenko
Edited by Ruslan Grigurko

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Table of Contents

Artificial intelligence holds a unique place among modern technologies. While widely adopted by many businesses to enhance services and optimise internal operations, some people still don’t fully understand what AI is or what it can truly do.

The lack of understanding fuels public misconceptions and can be a serious risk for businesses. Misjudging AI’s capabilities and limitations can result in failed projects, wasted budgets, and misaligned strategies.

To avoid these costly outcomes, the first step is to understand AI’s current capabilities.

The current state of artificial intelligence

AI technology has drastically improved over the past decade. Ten years ago, artificial intelligence was mostly limited to narrow tasks like image recognition, translation, or basic automation. AI showed promise, but many real-world applications were still out of reach.

Nowadays, we have a very different situation.

AI is more accessible, more capable, and present in everything from customer service to creative work. Large language models, generative tools, and advanced automation represent a major leap forward from what was available in the past.

To make smart use of AI, you need a clear view of its practical capabilities and its boundaries.

What AI can and can’t do

AI is a powerful tool, but like any tool, its value depends on how and where you use it. At its best, it can identify patterns, automate operations, and personalise user experiences at a scale no human team could match. But that doesn’t mean it can replace human judgment or that every process is ripe for automation.

Here’s what works.

Use AI for repetitive, rules-based tasks

AI thrives on structure. It’s highly effective in automating workflows like document classification, invoice processing, or customer email triage, freeing your teams to focus on creative or strategic work.

Apply AI to extract value from your data

Business intelligence platforms powered by AI can uncover trends, anomalies, and opportunities that are hard to spot using traditional dashboards. In industries like finance or logistics, this can directly drive competitive advantage.

Personalise at scale with AI

From product recommendations to curated content feeds, AI can enhance user engagement in real time, so long as you have the right data architecture in place.

That being said, there are still areas for growth. Be careful where you rely too heavily on AI.

Don’t expect it to handle ambiguity

AI lacks common-sense reasoning and struggles in unfamiliar or nuanced scenarios. It works well within the patterns it’s trained on, but falters when the rules change or context is missing.

Don’t remove the human from high-stakes decisions

Decisions with ethical or legal consequences should always include human oversight. AI can support the process, but the final word should always be on human.

Don’t assume more data equals better outcomes

Poor-quality or biased data leads to poor AI decisions. If your data is inconsistent or incomplete, your results will reflect that, no matter how advanced the model is.

To put it simply, AI technology is far from understanding the context the same way as people do.

How AI tools impact business

AI can bring real value when applied to the right problems: automating routine tasks, analysing massive datasets, or personalising user experiences at scale.

But not every business faces challenges that require that level of complexity. Moreover, pushing AI to power every feature often leads to wasted resources, misaligned expectations, and disappointing results.

Before adopting AI, ask yourself: do you really need it?

Some businesses that could benefit from AI are hesitant to do so. It made sense in the past, when there were few practical examples of how AI could deliver real value.

But that’s no longer the case. Today, AI is embedded in tools and platforms we use every day:

Google Search and Google Translate use advanced AI models to deliver real-time language translation and search relevance.

Tesla’s Autopilot combines computer vision and machine learning for semi-autonomous driving.

Netflix & Spotify recommendations use AI to analyse user behaviour to adapt content offerings.

Zest AI helps mid-sized lenders make better credit decisions, improving approval rates, and managing risk.

Mastercard and Visa use machine learning models to analyse transaction patterns in real-time.

Stitch Fix recommends clothing based on style preferences and feedback

These use cases highlight a critical shift: AI is no longer a tool reserved for tech giants. As Andrew Ng, founder of DeepLearningAI, emphasised in his TED Talk, technology is now accessible to companies of all sizes.

Still, success with AI depends on more than just adoption. Understanding its limitations and how to work around them can make the difference between meaningful impact and wasted investment.

Challenges of AI systems

Integrating AI into business is a complicated process. There are fundamental challenges that go beyond technical questions.

Ethical concerns

Who is responsible for AI’s decisions?

For now, there is no clear answer. As AI systems take on more decision-making roles in areas like finance, healthcare, and law enforcement, the question of accountability grows more urgent.

Notably, the COMPAS algorithm used in U.S. courts to predict recidivism risk faced criticism. A study showed it had a systemic bias in the algorithm, which is why it was more likely to falsely label some defendants as high-risk due to their race. The case raised concerns about fairness, transparency, and accountability in AI decision-making.

AI can operate without direct human oversight, but that doesn’t mean it should. Ethical and responsible AI deployment increasingly depends on human-in-the-loop systems where humans are involved in key decision points to ensure accountability, context awareness, and trust.

Biased models

AI models learn from data. If the data is biased, the model will be too.

One recent example is xAI’s Grok, which was criticised for exhibiting ideological bias by favouring certain political views over others. The issue allegedly stemmed from its training data, which is mostly X posts, leading to concerns about trust and fairness.

But fixing bias isn’t as simple as removing “bad data.”

Often, bias reflects deeper societal patterns embedded in historical records. That’s why it’s critical to implement continuous bias monitoring and involve diverse, cross-functional teams in the development process. These steps help spot blind spots early and build AI systems that better reflect the real world.

Computing power limitations

To train an AI model, you need a lot of computing power, something many businesses lack. Many lack state-of-the-art hardware or large server farms to handle such workloads.

Even when using pre-trained models, companies may face infrastructure upgrades or depend on cloud vendors, which come with usage fees.

Often, balancing performance needs and cost is what stretches or even breaks budgets.

Security concerns

AI systems can be vulnerable to manipulation. For example, attackers can intentionally feed false data into a model to make it behave incorrectly, called adversarial attacks.

Moreover, AI introduces new data privacy challenges. The more data your AI has access to, the more effective it becomes, but that also increases the risks if that data is exposed or misused.

Transparency issue

AI decisions can be hard to explain. Complex models, especially deep learning systems, are often seen as “black boxes.” You get an output, but you can’t always trace how or why the model come to that conclusion.

This becomes a serious issue in regulated industries or when customer trust is on the line. Although, there are efforts to mitigate this problem with explainable AI (XAI).

Conclusion

AI is a powerful tool, but it has limits.

It excels at well-defined tasks backed by large volumes of data, but it still can’t reason, adapt, or interpret the world the way humans do. Its real strength lies in enhancing automation, surfacing insights, and accelerating decisions, not replacing human judgment.

This is why not every business problem needs an AI solution.

Many AI projects fail not because of technical shortcomings, but because expectations were misaligned from the start.

Before you think about AI adoption, ask yourself:

Do you have the right data?

Can you clearly define the problem?

Are you prepared for the ethical, legal, and operational implications?

Clarity on what AI can’t do is just as important as understanding what it can. The difference between meaningful innovation and costly experimentation often comes down to asking the right questions before you begin.

How Altamira can help

Whether you’re looking to add AI tools to your business, assess AI readiness, or incorporate AI into your product, our approach is guaranteed to reduce your stress.

Zero headaches: We build AI-driven digital products and automate internal workflows to reduce manual effort and increase efficiency. With a proven track record of pushing the boundaries of what’s possible with AI, we will guide you in avoiding common pitfalls and ensuring your success.

Results in days: We focus on delivering tangible value in days, not months. We validate the business value and assess technical risks by building a small, functional increment that tests multiple use cases to identify the most impactful direction.

Control everything: We deliver complete documentation, explainability assets, and hands-on training for your team. Transparent communication guarantees that you always stay in the loop and won’t have to worry about the details.

Get in touch and empower your products and services with AI today.

FAQ

What is something AI cannot do?

AI may be able to analyse text, generate responses, and even mimic tone, but it still doesn’t truly understand context the way humans do.

It lacks common sense, emotional intuition, and lived experience.

These gaps become especially clear in conversations that rely on subtle cues, social dynamics, or cultural nuance, areas where human understanding is essential and machines still fall short.

What not to do with AI?

Don’t rely on AI for decisions that require judgment, ethics, or deep expertise.

You can use AI to draft ideas, speed up research, or explore options, but it shouldn’t be the final word in areas like medical advice, legal opinions, or hiring.

Also, avoid feeding it sensitive data. AI tools don’t “forget,” and not all systems are designed with privacy in mind.

What tasks is AI bad at?

AI struggles with open-ended creativity, physical interaction, and real-world problem solving.

It’s good at remixing existing patterns (like writing or image generation), but it doesn’t invent from scratch.

AI also fails in tasks that require hands-on experience, like repairing hardware, navigating unpredictable environments, or understanding human behaviour in complex settings.

Does AI have a limit?

The short answer is yes.

It doesn’t have goals, consciousness, or a sense of consequence. It’s a tool, not a replacement for human insight, responsibility, or accountability.