How to Avoid Wasting Millions on AI : An AI founder lists three common failures when companies implement AI into their systems.
By John Winner Edited by Micah Zimmerman
Opinions expressed by Entrepreneur contributors are their own.
Artificial Intelligence (AI) investments have historically been expensive. While a lean approach has cost hundreds of thousands of dollars annually, many businesses invest millions — with no guarantee of success or value generation for the business. Given the massive scale of these investments, and the competitive advantage that a successful deployment would bring, one might assume that many companies get it right.
Most, however, fall short. These failures are often multifactorial, but they fall into three broad categories: a deficient strategy, data issues and workforce shortcomings.
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Failure #1: Not understanding specifically how AI will help
The first point of failure is strategy. Before even reaching the launchpad, many companies doom their AI implementations by insufficiently strategizing. Most business leaders now see the power of AI as a tool but don't yet understand the exact use cases for their teams. Additionally, it's essential to understand the concrete inputs an AI implementation would require and the outputs that would generate value.
Every team has its own needs, inputs and outputs. To provide a few illustrations: a sales team might use an AI model to predict which leads are most likely to close, allowing the team to adjust their resources accordingly. The model could rely on all the same inputs a savvy salesperson would consider — the company's revenue, size, industry and past interactions. By comparing this lead to a rich history of previous leads, AI can forecast the likelihood of that deal closing and recommend the best actions for team members to increase the chances of winning the deal.
Increasingly, marketing teams will integrate AI to continually personalize what content they deliver, when it's delivered and through which channel. The model would use extensive customer data — demographic information, purchase and browsing history, satisfaction scores and location — to automatically send the most relevant products and offers to an individual or company.
Additionally, many customer service teams are already using natural language inputs from customer inquiries and any additional data they have about the customer to provide instantaneous replies or recommended actions - saving time and delivering better customer experiences.
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Failure #2: Data is a mess
Data is the core fuel of AI success. But businesses lose billions of hours and trillions of dollars every year due to siloed, disorganized or insufficient data. AI is impossible when a company's data assets are scattered across various tools and business segments.
Proper data hygiene and management — with clean, organized, accurate and unified data sets — are crucial precursors to success. If the data isn't ready, the machine learning model will be unable to learn. This may require consolidating and reorganizing data from various incompatible sources, reviewing this data for accuracy and quality and ensuring the adoption of unified data practices among employees going forward.
Less than 10% of businesses have implemented the groundwork of unified, organized data to enable a leap to widespread AI/ML implementation. Thus, preparing for the AI age represents a massive opportunity for businesses to gain a competitive advantage.
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Failure #3: The human element
A third factor that contributes to AI implementation failures is closely related: the human element. Specifically, the most common problem here is a lack of qualified data scientists and software engineers who have not only technical AI and ML knowledge but also an in-depth understanding of how to make the best use of data available and apply it in a specific use case, given a business's needs. These people are exceedingly rare and expensive to bring on board. And in truth, there will never be enough of them — demand will always exceed supply.
As a better alternative, companies are beginning to invest in programs that intelligently automate AI/ML model development and application. A new generation of tools, with no-code workflow builders and similar features, will drastically improve the accessibility of AI and lower costs. If workers can be upskilled to have a broad understanding of feasible applications of AI, as well as necessary outputs and inputs, they can generate comparable or even better value than traditional implementations.
The path to successful AI implementations
The companies that lead in this area and prepare to integrate a new generation of accessible AI tools — those that anyone across the enterprise can use — will have a massive competitive advantage.
The best way to use AI to accelerate your business's growth is to steer clear of these common pitfalls. Spend time in advance-planning a complete strategy. Make sure you have the right people in the room (technical and non-technical alike) and train them to understand the parameters around a successful implementation. Finally, organize and unify data across your organization, relying on no-code tools as much as possible to remove the bottleneck of software developers dealing with crippling IT backlogs.
Success will put you ahead of more than 90% of your competitors. It's worth it when you do it right.
