How to Lead Your First AI Project

Whether you’re taking over an existing AI project or beginning a new one, leading the initiative can be overwhelming, especially if you’ve never done either before. However, the challenges of being an AI project leader shouldn’t stand in the way of your ability to plan, build and deploy an invaluable AI solution.

We want to help you succeed in your mission to transform your organization with Intelligent Automation, AIOps and Conversational AI. Each project undoubtedly has its own unique requirements, quirks and goals. Nonetheless, there are some common elements to most AI projects that we've witnessed from our clients' experiences, and knowing these ahead of time can help make projects run more smoothly. Here’s a list of the top three most important steps to take when beginning your journey as an AI project leader.

Step 1: Assess Your Company’s Appetite for AI

Employees’ reluctance to use or engage with AI is one of the most common challenges to a successful AI deployment. The source of this reluctance varies depending on the company. For example, unfamiliarity with AI technology, concern that the technology will contract or eliminate certain roles and functions for human employees, or questions over long-term ROI are typical issues. AI projects often struggle to reach their full potential or deliver the most possible value when employees are not in support of deploying the technology from the outset, but there are ways to counter these perceptions.

Many of the AI experts featured in Amelia's Women in AI series, for example, explain that people fear AI will take away their jobs when they lack an understanding of how the technology works and how it is used. As an AI project leader, the silver lining to this challenge is you can overcome misunderstandings by engaging in thoughtful and educational communication with your colleagues.

When taking on an existing AI project, or planning a new deployment, the first step in your project roadmap should be to foster open discussions with employees regarding the role that AI will play at your company. In this Harvard Business Review article, AI leaders are encouraged to explain why they are creating their AI project to reassure employees that AI is meant to enhance, not diminish, their roles.

In fact, many companies deploy automation and AI solutions specifically to improve employee experience. Within a digital-human hybrid workforce, Digital Employees handle high-volume, mundane and repeatable tasks, giving human employees more time to focus on more complex, creative and challenging job functions. As an AI project leader, you can increase the likelihood of internal AI adoption by educating employees on these benefits and being transparent on how the technology works to resolve workflow issues.

Step 2: Gather the Right Stakeholders

Once you determine the use case for your AI project, it’s time to choose your team members. (Tip: If you need help determining your use case, visit our Get Started webpage for examples of use cases based on real-world experiences from our customers.) When choosing your AI project team members, consider colleagues whose unique insights would strengthen your AI solution, as well as those who would advocate for your solution across the company. Sometimes this involves looking across departments and job functions, not solely those linked to IT.

For example, if your Conversational AI use case is for an HR assistant that helps streamline the employee onboarding and offboarding process, you should include your company’s HR director or manager, a payroll specialist and benefits lead, as well as your IT and procurement managers in your project. While the HR-related stakeholders would bring their HR-specific perspective on the solution, the IT and procurement managers would provide insight on procuring and managing employee equipment, as well as enabling and disabling employees’ access to your company’s VPN, Active Directory, Wi-Fi and more.

Including the right stakeholders in your AI project will allow you to connect and automate disparate processes across your company, helping to reduce inefficiencies caused by human latency and improve productivity. It’s no surprise then why this Harvard Business Review article argues that AI solutions developed by cross-functional teams have the biggest impact.

However, as your team begins working on your AI solution, be wary of feeling as though you need to “boil the ocean.” Overcomplicating your AI project with too many objectives before the solution even launches may derail the project altogether. As you collaborate with your team members, make sure to always keep your key use case(s) in mind and avoid straying from your main objective.

Step 3: Move Quickly but Cautiously

You and your team may feel compelled to repeatedly pilot and refine your solution, as it is easy to get caught up in the pursuit of “perfection.” However, delaying the launch of your project means running the risk of falling behind your AI-enabled competitors. That said, we caution against quickly launching an AI project merely to keep up with the competitors in your industry in the short-term.

So, how can you move quickly but cautiously as an AI project leader? Start with a simple solution that aligns with the business value you wish to derive from AI, such as increased Net Promoter Scores, reduced time to resolution, 24/7/365 customer service or others. Once deployed, be receptive to feedback from end-users and be willing to improve and expand upon your initial AI deployment.

Many of our customer stories showcase the benefit of continuously improving upon your initial AI deployment. For example, several companies began by deploying Amelia, the market-leading Conversational AI, to address a handful of skills, and over time they have adjusted and expanded her responsibilities. By doing so, they better serve their end-users by addressing real pain points and remaining flexible as new challenges arise.

Leading a successful AI deployment is a substantial responsibility that takes a considerable amount of planning, but with the support of your colleagues, a well-rounded team of stakeholders and a clear vision for how you expect your solution to create business value, you will be well equipped to drive long-term success with your AI solution.