Why Artificial Intelligence (AI) pilot projects fail: 4 reasons

Artificial intelligence (AI) is poised for takeoff as companies increasingly find ways to tap its benefits – but figuring out how to implement it can be challenging. Consider these common roadblocks
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The artificial intelligence (AI) industry is continually evolving, with new solutions being created and deployed every day. Gartner predicts that 75 percent of organizations will have operational AI by 2024. However, Gartner’s research shows that only 53 percent of AI projects make it from prototype to production. What is holding new AI pilot projects back from hitting production?

Successful AI projects are all around us, but there is no single best way to create and deploy an AI product with all of these developments. There are, however, four reasons businesses might be missing the mark when it comes to their AI solution.

1. Not enough data

AI is consistently learning and growing from its results and algorithms to provide better, more efficient, and more accurate outcomes in the future. For AI projects to learn, they need an abundance of information. The more data AI can ingest, the higher the accuracy of its output. Yet a common issue is a lack of sufficient data sets for developing AI solutions.

AI needs to ingest enough data to identify patterns within the dataset. A lack of data can impact predictions and output. Providing AI with substantial training data sets can combat this issue and help limit the risk of biases.

[ Check out our primer on 10 key artificial intelligence terms for IT and business leaders: Cheat sheet: AI glossary. ]

Where large data sets can be daunting to a human, AI has the power of speed on its side and can learn quickly. Providing the proper quality and quantity of data allows these solutions to yield exceptional accuracy. If your team still feels bogged down from the endless tasks that the AI solution was supposed to solve, you will need to continue training your AI.

2. Sticking with one means of learning

You don’t just need a lot of data; you also need a lot of sources. For AI to work, it first has to learn. Limiting AI’s learning to one source or knowledge base can negatively affect how the end product operates. Without a range of information from different means, an AI solution will have gaps in its deliverables, causing issues for both creators and end-users.

Successful AI tools use a combination of deep learning models to provide well-rounded solutions. AI projects need to employ various techniques, algorithms, and learning efforts cohesively with straightforward and easy accessibility for humans to engage should there be a need. These “ensemble” algorithms often outperform any individual method of prediction.

With that being said, humans carry unconscious bias, whether they mean to or not. If only one person is providing information to the AI solution, that solution will adopt and share the same biases as the person who taught it. When building an AI project, developers and business leaders need to be laser-focused and aware of potential biases, such as cultural and environmental factors playing a role in developing an AI system, as well as how they can intervene to eliminate biases that could arise. Staying up-to-date on AI developments, how others are implementing and adjusting their projects, and the regulations put in place are essential to developing a successful AI solution.

When building an AI project, developers and business leaders need to be laser-focused and aware of potential biases, such as cultural and environmental factors playing a role in developing an AI system.

Some steps to take to make sure an AI solution is as bias-free as possible include:

  • Assessing potential outcomes from the AI solution
  • Feeding in a wide and diverse set of data for AI learning
  • Reducing bias with a self-learning system able to receive feedback and improve responses
  • Diversifying training individuals to provide a range of input messages
  • Collaborating with other organizations using AI to reduce biases on a wider scale
  • Providing transparency to users so they confidently understand the reasoning behind the AI’s decisions

3. Lack of understanding from other employees

Not every person working on an AI-based project is an AI genius. However, successfully deploying an AI solution requires a general understanding by every employee and end-user. Everyone within an organization should understand the possibilities and limitations. With a lack of knowledge by all involved comes a lack of deployment.

For example, the sales department can’t sell AI solutions if they don’t understand and know their use cases. On the other end, HR teams can’t adopt AI into their everyday workflows for onboarding new employees if they don’t know how, when, and where to use it.

Everyone from executives to employees needs open feedback loops to allow for discussions on AI and getting people acquainted with the solution. Those more familiar with AI then have the opportunity to clearly communicate the level of interaction it requires to ensure everyone has the correct information needed for maximum efficiency.

Leading the change management to implement AI for digital transformation success is not limited to the role of a CIO or IT team. Instead, businesses as a whole need to work together to ensure every department has the proper tools and technologies in place to their respective standards. CIOs and IT teams are crucial to improving digital literacy throughout the organization, but when it comes down to what matters for a successful transformation, the driver of change is how each department can help transform a company’s technology, data, processes, and culture.

4. User experience is put on the back burner

Lastly, the success of an AI project relies not only on the solution itself; it also relies on the users and their experience. If a user doesn’t understand the tool’s purpose or how to use it, they won’t buy it. Just as it is crucial to build an AI project that is accurate and efficient, you also need to make sure the interface is user-friendly. Customers and users need to realize the value of what you’re doing, how it solves the problems they are facing, and how they can use it with ease.

Said another way, if your dataset comes from your system of record and your AI powers your system of intelligence, then your system of engagement is what will help drive adoption of the platform. The user experience is paramount for a successful AI implementation.

The future of AI holds great promise as new data and tools are collected and deployed each day. AI projects done right provide major benefits for the business. But company leaders must be wary of making mistakes that could jeopardize their projects, such as limiting the information and sources they are pulling from or neglecting the end project. Companies that can learn, adapt, and implement the proper processes for their AI project will be the frontrunners in this evolving industry.

[ How does AI connect to hybrid cloud strategy? Get the free eBooks, Hybrid Cloud Strategy for Dummies and Multi-Cloud Portability for Dummies. ]

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David Karandish is Founder & CEO of Capacity – an enterprise artificial intelligence SaaS company headquartered in St. Louis, MO. Capacity’s secure, AI-native support automation platform helps teams do their best work.

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