According to Gartner’s CIO Agenda Survey, 46 percent of CIOs have plans to adopt AI in their corporate realm.

Over the past few years, AI has impacted various functions and industries and its adoption only seems to be growing. In fact, McKinsey predicts that AI will lead to a GDP growth of $13 trillion by the year 2030.

Although the adoption of AI is on the uptick, it can also be noted that it has not gained momentum the way it could have. One of the reasons is the time it takes enterprises to evaluate the risks involved in adopting an AI-related technology. Also, organizations will need to weigh between the risk of replacing legacy systems and the business value AI technologies generate.  

Since all organizations are starting from scratch, an AI pilot project can be an actionable starting point for an AI journey. It helps in validating use cases, evaluating the risks and measuring the ROI quickly.  Here are five factors to consider when selecting your first AI pilot project.

6 Factors To Consider When Selecting Your First AI Pilot Project

1. Have a well-defined challenge and define outcomes

Most early AI projects fail due to the lack of clear business objectives and outcomes. Enterprise leaders should keep the end result in mind while picking a project or use case. These outcomes could be improving the CSAT score, reducing contact center volume, reducing service desk costs, etc.  

The business problem does not have to be big or strategic. However, a clarity with respect to the business problem to experiment with, learn, and to get the desired outcome, is much needed. 

Clearly defining the desired outcome and identifying success metrics simplifies stakeholder buy-in and support for the project. 

2. Pick a simple project that can be completed with a short turnaround time

Since this will be the first venture with AI, a pilot project must be one that can be executed in a short duration of time, ideally about 6-12 months. The core objective of a pilot project is not to solve any major issues, rather it is meant to serve as a reference point for later implementations.

For instance, choosing a project with a higher success rate such as an AI chatbot implementation in the company’s contact centers is a good place to start. Since chatbots can be deployed quickly in a matter of a few weeks, they pose a minimum risk as well as helping companies see their impact in a short amount of time. 

The stakes are usually high during the pilot project. Ensure that the initial ambitions are sensible because if the initial AI project doesn’t work, the future AI initiatives may be suspended indefinitely by the organization.

3. Don’t Expect Perfection and Aim Low, to Begin With

It’s recommended not to aim for hard outcomes and direct financial gains when starting your pilot project journey. Start with a small scope and aim for soft outcomes such as process improvements, improving customer satisfaction or increasing efficiency, rather than aiming for something big.

Expect AI pilot projects to primarily produce lessons that can help drive future or consequent projects. Do not expect the pilot project to be perfect or a tremendous success right away. Set the targets as low as possible to have a referenceable outcome. 

4. Build a compact team and appoint a capable leader

As with all projects, the number of resources required varies according to the project requirement. Although the same is true for an AI pilot project, it is good practice to build a compact team that can effectively work cross-functionally.

Having a small team helps everyone communicate more effectively and stay on the same page with regards to the goals and outcome of the pilot project.

In order to steer the team effectively, a capable leader is required who can liaise between both AI and the domain/industry experts.

5. Use good, static data

In order to effectively implement an AI project, a large amount of good quality, dependable data is required. Data is the cornerstone of any AI undertaking as the intelligent system ‘learns’ by studying vast amounts of data over a period of time. 

Additionally, using data that is more static in nature, and does not keep changing rapidly, helps the algorithms produce consistent results. We must remember that AI on its own is not capable of discerning between good quality and bad quality data. 

An AI system that is fed bad data, will produce inconsistent and often incorrect insights. Hence it is important to use a large dataset of accurate content in order to be successful when implementing the AI pilot project.

6. Accelerate your pilot project with credible partners

Although AI pilot projects are ideally simple and easily manageable, they still require a great amount of expertise and the right resources to implement correctly. 

If your organization lacks an experienced AI team, it is best to work with a capable external partner to effectively execute the AI pilot project in the company. 

Conclusion

Selecting and starting your AI pilot project may be a little intimidating but by delaying the decision, you may fall behind your competitors who move faster. 

This list of factors to consider should help you get a good idea about choosing your first AI project. If you need in-depth insights, please feel free to get in touch with one of our AI experts for a personalized consultation.