Free DownloadWhat is machine learning? Guide, definition and examples
Machine learning is a branch of AI focused on building computer systems that learn from data. TechTarget's guide to machine learning serves as a primer on this important field, explaining what machine learning is, how to implement it and its business applications. You'll find information on the various types of ML algorithms, challenges and best practices associated with developing and deploying ML models, and what the future holds for machine learning. Throughout the guide, there are hyperlinks to related articles that cover these topics in greater depth.
Machine learning promises insights that can help businesses boost customer retention, combat fraud and anticipate the demand for products or services. However, deploying the technology -- and realizing the anticipated benefits -- can prove difficult.
The thorny issues of introducing any new tool come into play, such as insufficient investment and lack of user acceptance. But organizations deploying machine learning (ML) must address an even broader set of concerns, from ethics to epistemic uncertainty.
Organizations take on a certain amount of risk when pursuing emerging technologies. In the case of ML, the potential hazards are multidimensional and loom large. Here are seven ML challenges businesses should consider:
1. Dealing with ML bias
Bias in machine learning models ranks among the top ethical issues. For example, training data might not represent all the groups in a given population, so the resulting model will produce systemically prejudiced results. But the issue of bias extends beyond the model itself.
"Bias is always something we think is top of mind," said Derek Perry, CTO at Sparq, an engineering consulting company. "When we look at the bias component, in particular, the models aren't the problem. It's often much more the data and the summarization of data that you're putting into models that are the challenge."
Other ethical issues arise when organizations use AI to make decisions in sensitive contexts, Perry added. Perry said he looks at such considerations within the broader context of safety.
"We want to ensure the AI experiences that we're building have safety and guardrails in place, whether they're ML-oriented or generative use cases," he noted.
2. Framing the ML problem
In the rush to build models, organizations struggle to frame a problem that ML can address.
"That's an area we've spent a lot of time with organizations on," said Perry, whose company helps customers eliminate risk in their AI investments. "How can we understand the problem more, as opposed to going directly into the technology solution? Going straight to the solution leaves critical complexities on the floor, and those are exactly what determine whether machine learning or generative AI proves value in production."
It takes a lot of belief in where AI is going to be able to put the wheels in motion.
David FrigeriChief AI officer at EisnerAmper
David Frigeri, chief AI officer at EisnerAmper, a business advisory firm, said he's seen improvement in how organizations identify problems and opportunity areas. The difficulty lies in determining how to act on them, he noted. What's needed now is a mindset that recognizes AI models can handle entire processes.
"There's a legacy mindset that has you using AI to solidify the status quo or strengthen the status quo," Frigeri said.
In contrast, some companies are re-envisioning the process, rather than automating yesterday's problem, he said. This approach should also consider whether a particular process even needs to exist, he added. "It takes a lot of belief in where AI is going to be able to put the wheels in motion."
3. Addressing data literacy
Organizations should acknowledge data literacy as a top consideration when deploying ML. Employees unfamiliar with the basics of interpreting and communicating data will struggle to use the technology effectively.
A 2026 survey from DataCamp, which provides a data and AI skills-building platform, found that 42% of business leaders that had mature literacy upskilling programs across their companies reported significant positive ROI from AI investments. That compares with the 21% of the respondents in the overall survey population who reported that level of ROI.
The survey also identified a data literacy disconnect: While 88% of business leaders considered data literacy important or very important, 60% of respondents cited a data skills gap in their organization. Those gaps were most evident in foundational data and AI areas, such as interpreting information and turning insight into decisions, according to the report.
Businesses can build data literacy programs with jargon-free, enterprise-wide terminology and interactive training as key components.
4. Investing in data quality
Data must be prepared, cleansed and structured before a company can build effective ML tools. But businesses sometimes skip this data engineering step and jump into model development. The DataCamp report cited data quality and governance issues as barriers to effective AI use.
"Problems such as dirty or inconsistent data, unclear ownership and conflicting definitions make it difficult for employees to trust dashboards, analyses or AI outputs," the report said.
Data harmonization is also important, especially for organizations planning to use an ML model across different sites. For instance, in healthcare, a model that's good for one hospital might not work in another facility using a different data format, said Jesse Meyer, assistant professor of computational biomedicine at Cedars-Sinai, who has worked on ML research at the healthcare organization. "The model you build doesn't immediately translate to another hospital," he said.
Businesses must overcome technical and risk mitigation challenges when they take on ML.
5. Ensuring ML adoption
Two years ago, companies pursued change management and cultural considerations as critical for successful adoption. Now, the emphasis has shifted to the mechanics of AI and ML, rather than selling users on new technology.
"You don't have to convince them to use it, "EisnerAmper's Frigeri said. "They want to use it. It's our job to put it in a place that's really easy for them, [and] make it more accessible and useable as part of their everyday work."
The task is to incorporate AI directly into an organization's workflow. Otherwise, there's friction that blocks the business from deploying the technology where it's most important, Frigeri said.
Avitesh Kesharwani, senior principal consultant at Genpact, a professional and technology services company, said getting ML output to flow into enterprise workflows has become the hard part of deployment -- surpassing the task of building the model itself.
For example, Kesharwani cited an insurance company project where it proved difficult to wire ML output into the business's legacy SMTP infrastructure, compliance-driven routing rules and systems that were never designed to talk to one another.
6. Optimizing ML models
The ML journey doesn't end with adoption. Organizations must monitor and update models to ensure performance and accuracy over time. Poorly designed models might gobble excessive amounts of compute resources or take too long to make a prediction. In addition, model drift can hamper a model's ability to accurately identify trends. This problem occurs when training data starts to deviate from the real-world data the model encounters.
However, ML engineers use various techniques to improve model performance. Model optimization might involve modifying a model's underlying code to reduce memory and CPU use. Another approach is retraining a model on new data to address drift. ML regularization approaches help models generalize better and prevent overfitting.
In this context, machine learning operations (MLOps) practices can help businesses manage the entire ML lifecycle, including model monitoring and retraining to boost performance. Capital One, a financial services company, is among the enterprises that have adopted MLOps to handle the design, deployment and ongoing management of ML models.
7. Accepting uncertainty
The magnitude and speed of technology advancement is a top challenge with AI and ML, particularly in terms of governance and security concerns.
"One [concern] is vendors releasing stuff that we didn't even know they were going to release," Frigeri said. "It just pops up in our UI, and everyone has access to a particular feature. That's a challenge."
Frigeri also cited the advent of tools such as OpenClaw. The open source AI agent can orchestrate ML training workflows, forcing companies to have the necessary controls in place.
Such tools, Frigeri said, spark conversations between chief AI officers and chief information security officers. At EisnerAmper, those discussions deal with issues such as "what's going to be our position on agents that are able to take control of a laptop and take action as opposed to just providing information?" he noted.
Open source models are another topic of discussion, especially as EisnerAmper moves toward using them rather than black-box models to create more tailored solutions, Frigeri said.
In addition, businesses, especially those new to ML, must get used to systems that lack the deterministic nature of traditional IT systems. Predictive ML applications produce outputs based on probabilities, which can be jarring for companies accustomed to more certainty.
John Moore is a freelance writer who has covered business and technology topics for 40 years. He focuses on enterprise IT strategy, AI adoption, data management and partner ecosystems.
