What Are the Top 10 Challenges of Machine Learning?

Machine learning has changed dramatically since 2021, when this article was first published. The wave of generative AI, large language models, and AI agents has expanded what’s possible — and added a whole new set of challenges that didn’t exist a few years ago. At the same time, the foundational problems remain: data quality, overfitting, deployment, security, and the operational discipline that turns research prototypes into reliable production systems.
Below is an updated list of the ten most pressing machine learning challenges in 2026 — the ones that consistently determine whether an ML initiative reaches production and stays useful there. The challenges span three categories: those rooted in data science (the data, models, and features), those that arise during machine learning projects (security, accessibility, deployment, operations), and those that emerge once models are applied in production (drift, hallucinations, regulation). We’ll cover each, why it matters, and how teams in 2026 typically address it.

Key Takeaways

• The biggest machine learning challenges in 2026 fall into three categories: data science (data quality, overfitting, features), project execution (security, deployment, MLOps), and production realities (model drift, hallucinations, regulation).
• Data quality and quantity remain the #1 blocker. Most ML projects fail because of messy or insufficient data — not because of algorithm choice.
• The rise of generative AI and LLMs has added new challenges to the list: hallucinations, runaway inference costs, prompt injection attacks, and the need for evaluation systems that didn’t exist before.
• MLOps has emerged as the discipline that determines whether models actually reach and stay in production. Model drift, version control, and continuous evaluation are now first-class concerns.
• Regulatory exposure is no longer optional — the EU AI Act (in force from August 2024) and sectoral rules in healthcare, finance, and HR materially affect what ML systems can do in production.

Data science-related challenges in machine learning

First off, let’s talk about data science. Every machine learning model needs training data to “learn” how to work. And this very first stage entails some crucial challenges:

Challenge #1: Lack of training data

In general, machine learning models need training data–information and examples representing exactly what you want them to do for your company. Let’s use a straightforward example. You want to devise a new machine learning-based algorithm that will distinguish safe emails and spam.

In this example, emails are our training data. You need examples of safe emails, and you need instances of spam. The more, the better.

Sometimes, in order to train an ML-based algorithm, you’ll need hundreds of examples. And that’s good news because more often than not, you’ll need… Millions of examples. Yes, to train just one “simple” machine learning algorithm!

And the truth is, in many instances, you simply don’t have access to millions of real examples representing the exact same thing. That’s the very first challenge machine learning specialists have to overcome.

Challenge #2: Poor quality of data

In other instances, the data you need is available, but the quality of it leaves a lot to be desired. If you start work with poor-quality data, you can’t expect to end up with a fully functional and effective algorithm. On the contrary, it will be defective and inefficient. That’s why it is said that the vast majority of the data scientists’ work revolves around organizing and cleaning data. Because otherwise, it’s useless from the AI perspective, no matter how voluminous it may be.

And this is where data quality tools come into play. They are designed to remove formatting errors, typos, redundancies, missing entries, and other issues that reduce the quality of your data. And you can believe us, in the majority of companies, these errors are very common. In the machine learning world, the more doesn’t necessarily mean the better.

Challenge #3: Data overfitting

In short, data overfitting is all about developing a too complicated machine learning model and trying to fit it into a limited set of data. In the human world, it’s called overgeneralization.

Again, let’s use an example. Suppose you were recently robbed by a man wearing a black beanie. Will you jump to the conclusion that every person wearing a black beanie is a mugger? If you do, you will fall into a trap called overgeneralization. And that’s exactly what can happen in the machine learning world, too.

As a result, your machine learning model works brilliantly on a training dataset (yes, in that particular situation, black beanie = a mugger), but in more instances and cases, it fails to generalize properly (in the real world, black beanie doesn’t necessarily mean a mugger). That’s data overfitting.

Challenge #4: Dat underfitting

Here, we deal with a reverse issue. Your model is too simple or misses parameters that it should have included in order to produce a clear and unbiased result. This means that your machine learning model cannot draw useful conclusions from the training data.

Actually, the only way to deal with both these problems is to develop an algorithm that’s built strictly with a specific purpose in mind. There is no copy-paste here. Everything has to be adjusted and tailored to your assignment or project.

Challenge #5: Irrelevant features

When it comes to machine learning algorithms, training data is not everything. You also need a good set of features on which your algorithm can be trained. Let’s go back to our spam/non-spam emails example. There is a specific list of features describing every email: Content, subject line, used words, links, etc. All these features are relevant. But there can also be irrelevant features like sending time or kilobytes.

What does the fact that this specific email weighs 20kB and was sent at 11 pm tell you about whether it’s spam or not? Nothing! It’s an irrelevant feature that should be eliminated from your model. The more irrelevant features you have, the less helpful your final product will be.

In our example, deciding which features are irrelevant was intuitive and simple. In many real-life cases, you’ll have to think about this issue a bit longer.

Challenges related to machine learning projects

Here, we need to tackle three specific Machine Learning problems:

Challenge #6: Data security

Although we listed data security as one challenge, it’s actually multifaceted. For starters, you need to make sure that every framework, every third-party app, and every piece of your IT infrastructure is properly secured against diverse cyber threats. Secondly, bear in mind that your employees and coworkers can also be a source of the problem.

For example, the bring your own device policy (BYOD) can be very convenient from your employees’ perspective, but it can also be very risky. After all, how can you be sure that their private devices are properly secured? In most instances, you can’t.

Another data security-related problem is fake data. This problem happens when your company is being attacked by hackers who replace your real data with fake information. Suppose you run a manufacturing company and you’re under a fake data attack. What could have happened if your real measurements had been replaced with fake ones? For instance, your devices could get a false temperature report, resulting in a severe malfunction.

The last element we want to talk about is related to access control. Here, the best way to avoid unnecessary complications lies in designing encrypted authentication and validation procedures so that users are verified before they can implement any changes into the system or data it stores.

Whatever you do, remember: As Clive Humby, a British data science entrepreneur, once said, “Data is the new oil”. It’s as valuable and fragile. Make sure you take good care of it and protect it from possible threats.

Challenge #7: Accessibility

The accessibility picture has improved significantly since this article was first written. No-code and low-code ML platforms like DataRobot, H2O.ai, Vertex AI AutoML, Azure ML Designer, and Akkio now let non-specialists build, train, and deploy models with drag-and-drop interfaces. Foundation model APIs (OpenAI, Anthropic, Google, Mistral) have removed another barrier entirely — you can now solve many NLP and vision problems without training a model from scratch, just by calling an API. And LLM-powered coding assistants (GitHub Copilot, Cursor, Claude Code) have dramatically lowered the technical bar for writing the integration code itself.

Custom, high-end ML still requires investment and skilled engineers — particularly for problems where off-the-shelf models don’t fit. But the gap between “we have an idea” and “we have a working prototype” has collapsed from months to days for many common use cases.

Challenge #8: Deployment and the gap to production

Industry research has consistently shown that a large share of enterprise ML projects never reach production — Gartner and McKinsey have repeatedly put the gap somewhere between half and three-quarters of all initiatives. The reasons are rarely about the model itself; they’re about everything around it.
The most common deployment failure modes:

• The model wasn’t designed for the business problem. ML teams build something technically impressive that doesn’t fit how the business actually operates.
• No production data infrastructure. The PoC ran on a notebook with a CSV; production needs reliable pipelines, monitoring, and lineage that nobody built.
• No operational ownership. The team that built the model isn’t the team that has to run it — and that handoff was never designed.
• No evaluation in production. Once the model is live, nobody knows when it stops working until users complain.

The discipline that addresses these failures is MLOps — the practice of treating ML deployment, monitoring, and management with the same engineering rigor as traditional software operations. MLOps covers model versioning, automated retraining, drift detection, A/B testing, and production monitoring. In 2026, mature ML organizations rarely deploy models without an MLOps platform underneath them (MLflow, Weights & Biases, Vertex AI, Databricks MLOps, SageMaker, or custom internal tooling).

The other persistent issue — the gap between ML and business understanding — hasn’t gone away. The most successful teams have cross-functional collaboration baked into how they work: product owners or business analysts embedded with the data science team, not consulted occasionally. We’ve written more about this in our MLOps consulting and AI proof of concept services.

Challenges related to machine learning applications

Lastly, we have two more machine learning challenges that refer to specific applications of its algorithms. Let’s take a closer look at them:

Challenge #9: Hallucinations and reliability of generative AI

The most significant new challenge since this article was first written is hallucinations — when a generative AI model produces confident, plausible-sounding text that is factually wrong. This isn’t a “bug” that can be patched away; it’s a fundamental property of how large language models work. They predict the most likely next token based on patterns in their training data, not on a verified knowledge base.
For enterprise deployments, hallucinations are not just a quality problem — they’re a trust and liability problem. An AI assistant that confidently invents legal precedents, medical recommendations, or product specifications creates real downstream risk.
The practical responses in 2026:

• Retrieval-augmented generation (RAG). Ground the model in your own documents at query time so it answers from verified sources, not from training-data guesses.
• Source citation and traceability. Surface the documents the model used so users can verify outputs.
• Confidence thresholds and abstention. Let the model say “I don’t know” rather than guess.
• Continuous evaluation. Use frameworks like Ragas, TruLens, LangSmith, or your own test sets to catch quality regressions automatically.
• Human-in-the-loop for high-stakes outputs. A human reviewer confirms anything that would cause material business or legal impact.

Hallucinations can be managed but not eliminated. A mature 2026 LLM application is designed around this reality rather than pretending it doesn’t exist.

Challenge #10: Regulatory compliance and AI governance

The regulatory environment around machine learning has shifted significantly in the past two years, and continues to evolve rapidly. What was largely an internal engineering and ethics concern in 2021 has become a hard compliance requirement in 2026.
The main regulatory pressures:

• The EU AI Act entered into force in August 2024 and is being phased in through 2026–2027. It classifies AI systems by risk tier (unacceptable, high, limited, minimal) and imposes documentation, transparency, and human-oversight obligations on higher-risk systems. ML systems used in HR, education, credit scoring, law enforcement, and healthcare typically fall into the “high-risk” category.
• GDPR continues to govern any ML system processing EU personal data, including specific provisions on automated decision-making and the right to human review.
• Sectoral regulations add further constraints: HIPAA in US healthcare, DORA and PSD2 in EU financial services, SOC 2 for enterprise software, plus emerging state-level AI laws (Colorado AI Act, California SB-1047 and successors, Illinois AI Video Interview Act).
• Standards bodies — ISO/IEC 42001 (AI management systems), NIST AI Risk Management Framework — are increasingly referenced in procurement and audit requirements.

The practical implication: ML teams now need to think about compliance from day one of a project, not as an afterthought. Documentation of training data sources, model evaluation methods, bias audits, and human-oversight mechanisms are no longer optional in regulated industries. Production ML deployments increasingly need a risk classification, an impact assessment, and an audit trail — disciplines that didn’t exist as standard practice when this article was first written.
Teams that build these practices in from the start move faster than those who retrofit them under regulatory pressure.

Interested in machine learning? Read our article: Machine Learning. What it is and why it is essential to business?

Challenges of machine learning – summary

Is that a complete list of machine learning challenges? By no means! These are the challenges that we find most pressing and urgent. Some of them will be solved within the next few months. Some will, unfortunately, have to wait a bit longer. But the direction is clear. We work tirelessly to make machine learning more accessible and effective.

As you can see, although machine learning is a tremendous technology, there’s still a lot to achieve. We still have some significant challenges that we have to overcome in order to develop this technology further. However, it doesn’t mean that you can’t use machine learning for the benefit of your company today! In fact, thanks to deep learning, we’ve been able to achieve some spectacular results!

Thanks to ML, companies all over the world work in a more effective and automated way. Intelligent machines and applications now execute many mundane tasks. If you’d like to find out how machine learning can help your company grow–you’re in the right place!

At Addepto, we provide machine learning consulting. Our role is to help clients find and implement the solution that suits their needs and helps them overcome their everyday challenges. Find out more today!

See our machine learning consulting services to find out more.

References

[1] McKinsey & Company. The state of AI: How organizations are rewiring to capture value. (Annual State of AI report.) URL: https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai
[2] Gartner. AI in the Enterprise. URL: https://www.gartner.com/en/ai
[3] European Commission. EU Artificial Intelligence Act. URL: https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai
[4] NIST. AI Risk Management Framework. URL: https://www.nist.gov/itl/ai-risk-management-framework
[5] Anthropic. Building effective AI agents. URL: https://www.anthropic.com/engineering/building-effective-agents