If you’re struggling to get your artificial intelligence (AI) project off the ground or to deliver measurable results to the business, you’re not alone! Fortunately, AI project success is becoming less elusive for companies. According to Gartner, “By the end of 2024, 75% of enterprises will shift from piloting to operationalizing AI, driving a 5X increase in streaming data and analytics infrastructures.” That’s good news.
If you’re looking to accelerate your AI efforts and avoid the common pitfalls that can slow your company down, read on for some useful tips and advice.
Avoid the “Lab” Approach
According to a recent Forbes article, a recurring reason why many AI projects fail is that companies take a “lab” style approach to execution, expecting that a siloed group of data scientists and engineers can quickly carry out company initiatives. The problem with this approach is that successful AI projects require a company-wide strategy, with participation from key stakeholders from across departments, including not only data-oriented job roles but also business analysts and users from across the company. Companies can overcome this challenge by setting up dedicated, multi-functional teams to drive AI projects to successful completion, according to the article. This process may require upskilling current employees with more AI skills and working with external providers and vendors to fill knowledge gaps.
Put in the Time and Effort—and the Data
According to the article, “Developing and deploying ML models and ensuring that they drive real actions require a considerable time investment and are an ongoing process.” Success starts with ensuring that the right training data is available for your AI and machine learning (ML) system. This requires a single source or repository for all the data needed for your project and the ability to constantly feed or update it with the latest information. Integration with existing legacy systems is a challenge for virtually every company, but working with cloud-based, easy-to-use platforms and applications can help ease your IT department’s data integration challenges.
Finally, success requires turning your data into action. According to the article, “It is vital to establish a clear relationship between data, insights, intelligence, and outcome.”
Align the Organization
AI must deliver company-wide benefits, especially to business users, and also be aligned with business goals and objectives to demonstrate meaningful success. Achieving these goals requires that you convince key stakeholders and leaders across the company of your project’s business value. You can help overcome any resistance by starting with simple, clearly defined use cases and regularly communicating on your AI initiative throughout the implementation process and beyond.
If you’d like to learn more, read this recent APEX of Innovation post on five essential strategies for AI success.