Artificial intelligence (AI) can be a gamechanger for companies that know how to harness its full potential. Technology has the power to completely transform operations and free humans up from repetitive, mundane tasks that detract from truly exciting work. So why is there so much uncertainty surrounding AI project risk?

The problem is that enterprise leaders often wrongly assume that because AI is more accessible than ever, it’s easy to leverage. The reality is that 85% of AI projects fail, according to a 2019 Pactera whitepaper. And Gartner predicts that the same percentage of AI projects will continue to produce erroneous outcomes through 2022. 

Executive teams tend to forget these stats and, as a result, they underestimate how much work it takes to deliver AI projects from ideation to production. They overestimate expectations and underestimate what is required to succeed, throwing what they do invest down the drain and dooming the outcomes of their project.

We don’t just know this from anecdotal evidence – we’ve seen time and time again at Daitan that underfunded and under-resourced AI projects are highly likely to fail. And when they do, C-suites back off from their innovation goals, leaving room in the market for competitors to create that next great, disruptive offering.

To prevent a derailed project, technology executives have to keep close tabs on key leading indicators that suggest AI projects are headed in the wrong direction. In this post, we’ll explain what these indicators are so that leaders can catch minor issues before they grow into bigger problems.

The 5 Most Common Signs That Your AI Project Is Off Track

AI project risk skyrockets when one or more of the following conditions are true:

• AI teams can’t measure progress 
• AI teams have lost sight of overarching goals
• AI teams don’t know how much projects cost
• AI teams realize they don’t have the right skills or experience
• Executives lose motivation or interest

Enterprise leaders must be aware of these warning signs and the reality of AI project risk, in order to take proactive steps to ensure they don’t get in the way of long-term growth.

#1. AI Project Teams Can’t Measure Progress

The first sign that AI projects are likely to fail is that teams can’t adequately measure the effectiveness of their efforts. They either don’t have access to reliable data, or they don’t know what metrics they should use to benchmark progress. As a result, AI teams can’t set reasonable milestones or determine whether they are on track to hit certain goals. They also can’t evaluate MVPs in the marketplace or understand how customers perceive new offerings. 

Companies that pursue AI innovation without having a well-defined strategy for measuring progress are flying blind in airspace that’s already risky and filled with unknowns. For more information on how to measure AI progress well, read this whitepaper. 

#2. AI Teams Have Lost Sight Of Overarching Goals

The second sign that AI projects are at risk of failing is that teams heading up initiatives have poorly defined goals for what they hope to accomplish. Clear project goals give people something to target, whereas good tactics for measuring progress give people a way to see how far they’ve come.

When AI teams understand the business requirements of a project, they make better decisions when it comes to investing their resources. Articulating goals also makes it easier to avoid chasing good ideas that have the potential to lead companies astray from accomplishing high-level goals. 

#3. AI Teams Don’t Know How Much Projects Cost

A third sign that AI projects are headed for failure is when teams don’t know how much their efforts are costing the enterprise, both in terms of capital invested and resources allocated. When AI project leads don’t know how to estimate costs, they can’t calculate the ROI of their work or prove that what they are doing is more advantageous than pursuing another idea.

Without financial clarity, executives struggle to sustain capital and resource investments in AI projects. Consequently, team leads should always have a handle on how much the enterprise is spending on AI applications, keeping in mind that there will be productization costs that come after the development work is done. For more information, read our article on How Much AI Projects Really Cost.

#4. AI Teams Realize They Don’t Have The Right Skills Or Experience

It’s not uncommon for companies to realize that they don’t have the AI skills or experience they need until after they’ve already started investing in the technology. What makes this challenge particularly hard to overcome is that sophisticated AI skill is hard to come by and retain. Plus, depending on the use case, AI projects frequently require individuals with different AI specializations (e.g., data science, data engineering, data visualization, etc.), as well as software development skills such as DevOps. Read more on the topic of building the right AI project team and the skills needed for end-to-end development.

After recognizing they don’t have the right people in-house, some executives try to solve for talent issues in the wrong ways. They hire new and expensive full-time resources, not realizing they won’t need those skills once projects are in production. Or, they try to train up existing staff to take on the complex work of managing AI projects from whiteboard to market. Both of these paths are costly and may not end up positioning the enterprise better for long-term AI success. The better option is to look outside the enterprise as soon as it becomes obvious that a third-party consultancy could fill gaps temporarily at a lower overall cost. 

#5. Executives Lose Motivation Or Interest 

The final sign to watch out for is when executives lose excitement for AI before the technology produces tangible outcomes. When key business stakeholders stop buying into AI, it becomes much harder for project leads to obtain the funding they need to deliver on expectations. They also lose their license to experiment and explore problem domains fully, increasing the risk that AI projects don’t meet real customer needs. 

Should executives start to lose interest in AI, team leads need to learn what might have changed. They should also have a compelling business case on hand and deliver regular progress reports to ensure C-suite officers know what progress is being made. Otherwise, AI projects lose their life support and slowly fade into the background. 

Mitigate AI Project Risk With Daitan

Most AI failure modes come down to not having the tools or expertise needed to see a project through from beginning to end. AI projects require constant experimentation and iteration. Teams have to be willing to go back to the drawing board, revisit hypotheses, engage customers, and more, all of which are difficult when there are so many unknowns. 

Given these circumstances, one of the best ways to maximize the chances of succeeding is to work with a third-party digital product development firm, like Daitan, that can get you started on the right track before the leading indicators above the surface. We can set you up for success by identifying potential risks in advance and equipping you with the resources you need to fulfill your company’s unique AI goals. 

To learn more about how we position our clients to be one of the few to succeed with AI.