Begin with problems, sandbox, identify trustworth vendors — a quick guide to getting started with AI

With 77% of companies already using or exploring the use of AI, and more than 80% claiming it’s a top priority, leaders are eager to get maximum value from the technology. However, the volume of solutions available and onslaught of marketing messages accompanying them can make finding a clear path difficult. Here are some guidelines to help you evaluate AI tools’ capabilities and determine the best fit for your organization. 

When the media lauds a particular platform, or you discover your competitors are using the same one, it’s natural to wonder if you should, too. But before examining a new system, identify the problems your business is facing. What are its key challenges? Its core needs? Once you’ve redirected your focus, reframe the solution you’re considering through this lens. 

If AI technology will solve well-defined, measurable issues your company has been encountering (that is, automating routine tasks or increasing team productivity), the tool is worth exploring. If it doesn’t directly connect to solving your problems, move on. AI can be incredibly powerful, but it does have limitations. Your goal should be to only apply it to the areas where it can make the most meaningful impact. 

Pilot programs and experimental budgets

When you’ve determined that a given system may strategically support your needs, you’ve fulfilled the first necessary criteria — but this doesn’t mean you’re ready to make a purchase. The next step is to take time to test the technology significantly through a small-scale pilot program to determine its efficacy. 

The most valuable testing uses a framework connecting to crucial key performance indicators (KPIs). According to Google Cloud: “KPIs are essential in gen AI deployments for a number of reasons: Objectively assessing performance, aligning with business goals, enabling data-driven adjustments, enhancing adaptability, facilitating clear stakeholder communication and demonstrating the AI project’s ROI. They are critical for measuring success and guiding improvements in AI initiatives.” 

In other words, your testing framework could be based on accuracy, coverage, risk or whichever KPI is most important to you. You just need to have clear KPIs. Once you do, gather five to 15 people to perform the testing. Two teams of seven people are ideal for this. As those experienced individuals begin testing those tools, you will be able to gather enough input to determine whether this system is worth scaling. 

Leaders often ask what they should do if a vendor isn’t willing to do a pilot program with them. This is a valid question, but the answer is simple. If you find yourself in this situation, do not engage further with the company. Any worthy vendor will consider it an honor to create a pilot program for you. 

Additionally, plan ahead and set aside funds for an experimental AI budget. This should be where you turn when you want to try various solutions without overcommitting resources. Even if everything seems to be going seamlessly, give your team plenty of time to familiarize themselves with the technology and adapt before making a purchase or scaling up. 

Prioritize data security and vendor transparency

When you consider a platform, remember you’re not just evaluating the technology but the company behind it. Vendors should be put through just as much scrutiny — if not more — than the technology itself. Make sure you only work with vendors that maintain the highest standards in terms of data security. They should adhere to global standards for data protection and ethical AI principles, and the platforms themselves should be certified as SOC 2 Type 1, SOC 2 Type 2, the general data protection regulation (GDPR) and ISO 27001.

Furthermore, verify that your vendors aren’t using your company’s data for AI training purposes without explicit consent. Virtual meeting provider Zoom is an example of a popular company that had planned to harvest customer content for use in its AI and ML models. Even though they ultimately didn’t carry out these plans, the incident should raise concerns for enterprises and consumers alike.

If you put a dedicated AI lead in charge of this area, this person can manage all data security needs and ensure organizational compliance. This might feel like unnecessary, additional work, but it’s essential. Remember that all it takes is a single data breach by one of your providers to make you lose customer trust — if not your customers. 

Final thoughts

Leaders must use a structured approach to assessing AI solutions to get maximum value from them. Focus first on problem-solving, followed closely by testing and pilot programs, data security and identifying tangible value. AI can be immensely powerful, but only when applied to the right problems after careful selection and implementation. 

Arjun Pillai is co-founder and CEO of DocketAI.