Machine learning (ML) is a kind of artificial intelligence where algorithms improve their problem-solving ability independently, like the human brain. While many organizations are trying to enhance their business with machine learning solutions, they often fail.

Why do many machine learning projects fail before they begin? The reasons can range from the absence of governance frameworks to data drift or a problem with outdated data. Some businesses give up when they fail, while others keep trying until they get it right.

Neither of these options is viable to be successful. To survive in this fast-paced world, you need to perfect machine learning quickly and figure out how to adapt it to your business. Let’s see why companies fail:

– Fear of Failure

– Lack Of A Driven Delivery Manager To Spearhead The Project

– Lack Of A Strong Resource Acquisition Team For The ML Effort

– Doing Too Much Too Soon

Fear Of Failure

Fear can be both a strong motivator and a deterrent for businesses. Athena Reilly, a managing director at Accenture, noted that “In certain cases, corporate data management problems have ballooned to the point where some executives feel overwhelmed, and they use those problems ‘as an excuse’ to avoid AI entirely.”

She also noted that wary executives tell themselves, “I have bad data, therefore I can’t do things like AI” She advises that companies recognize the new business opportunities AI/ML represents and provide customers and employees valuable services.

Lack Of A Strong Resource Acquisition Team For The ML Effort

Not having suitable leaders to spearhead your ML project means it’s headed for failure. The management might have access to excessive data guarded by stakeholders, preventing the start of the project.

Deborah Leff, CTO of AI and data science at IBM, said that “most organizations are highly siloed, with owners who are simply not collaborating and leaders who are not facilitating communication.” She also noted that “I’ve had data scientists look me in the face and say we could do that project, but we can’t get access to the data.”

People in leadership positions need to support your idea and advocate for you, or the ML project might die before it begins.

Lack Of A Driven Delivery Manager To Spearhead The Project

Figure out what’s stopping your IT department manager from committing to the project. The project will not yield desirable results if there isn’t a supportive team from day one. To do this, you need a driven manager.

Maybe they’re unsure of the processes or management support or fear that there may be a sudden lack of funding. The manager might also be uncertain of the project itself and doesn’t want to risk a career on a doomed project.

To achieve the best results, involve the managers from the beginning to help shape the project scope and quality.

Doing Too Much Too Soon

It’s not uncommon to see a sign saying “Take all you want but eat all that you take” in a corporate cafeteria. This idea of not taking on more than you can handle applies in the business world. It is essential when launching a machine learning initiative in your organization.

It is advantageous to deploy a Machine learning initiative, especially considering its many advantages. If you do too much with ML, you might feel discouraged or overwhelmed with the poor results due to a challenging project or poor design.

Remain patient and clear about your goals and whether you can meet them before involving artificial intelligence and machine learning. If in doubt, consult experts.

The Takeaway

Machine learning is critical to developing technology. It provides AI the capability to learn from its experiences without human intervention or explicit programming. When using ML solutions, it is essential to pick a fundamental problem to solve.

If your business does choose to go ahead with machine learning, you can achieve the best results if you act like there’s no way back. This is why you select a fundamental issue that needs solving. Picking an issue will enable your organization to support the ML effort.