In our latest blog, we took a look at the importance of artificial intelligence (AI) and some of the reasons AI projects fail despite an organisation's best intentions. Here, we will explore how those failures can be turned into successes by sticking to five key principles.
Both business leaders and engineers have a role to play in avoiding misunderstandings that lead to AI project failures. Business leaders need to help their technical staff understand what the AI project’s goals are and engineers need to earn the trust of business leaders by keeping them up to date on the progress of the project. Effective communication can be the difference between success and failure.
Successful AI projects take time and patience to complete. Data scientists and engineers need space to understand and curate data before attempting to train an AI model that will learn how to behave from that data. Before they begin any AI project, business leaders should be prepared to commit at least a year to engineers being able to solve a specific problem. If an AI project is not worth such a long-term commitment, it most likely is not worth committing to at all.
Organisations that seek the latest advancements in AI simply to be able to use the technology are more likely to fail. Successful projects are laser-focused on the problem that needs to be solved. To ensure this, business leaders need to collaborate with technology experts to ensure that they select AI projects that are a good fit for their company and solve a real problem for their intended users.
Investing in infrastructure to reliably clean, ingest, and monitor data streams will ensure that more of an organisation’s AI projects ultimately succeed. Infrastructure that automatically deploys AI models allows organisations to put them into production more rapidly and reliably, where they can deliver real benefits to real users.
Despite the hype around artificial intelligence as a technology, AI still has its limitations. When considering a potential AI project, leaders should consult technical experts to assess the project’s feasibility. It needs to be a good fit for AI’s capabilities and deliver meaningful value to the organisation.
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