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

With 77% Companies that use or already explore the use of AI, and more than 80% saying that it is an absolute priority, managers are eager to obtain a maximum value of technology. However, the volume of solutions available and the assault of marketing messages that accompany them can make clear research difficult. Here are some guidelines to help you assess AI tool capacities And determine the best adjustment for your organization.

When the media greet a particular platform, or you discover that your competitors use the same, it is natural to wonder if you should also. But before examining a new system, identify the problems with which your business faces. What are its main challenges? His basic needs? Once you have redirected your attention, reframe the solution you are considering via this goal.

If AI technology will solve the well-defined measurable problems that your business has encountered (that is to say the automation of routine tasks or the increase in team productivity), the tool deserves to be explored. If it does not connect directly to the solving your problems, move on. AI can be incredibly powerful, but it has limits. Your goal should be to apply it to areas where it can have the most significant impact.

Experimental pilot and budget programs

When you have determined that a given system can meet your needs strategically, you have fulfilled the first necessary criteria – but that does not mean that you are ready to make a purchase. The next step is to take the time to considerably test the technology thanks to a small -scale pilot program to determine its effectiveness.

The most precious tests use a framework connecting to crucial key performance indicators (KPI). According to Google Cloud: “KPIs are essential in AI Gen deployments for a certain number of reasons: objectively assessing performance, align with commercial objectives, allow data -based adjustments, improve adaptability, facilitate communication clear stakeholders and demonstrate the king of the AI ​​project. They are essential to measure success and guide improvements in AI initiatives. »»

In other words, your test framework could be based on precision, coverage, risk or the most important KPI for you. You just need to have light kpi. Once you have done so, bring together five to 15 people to carry out the tests. Two teams of seven people are ideal for this. While these experienced people are starting to test these tools, you can collect enough contributions to determine if this system deserves to be put on the scale.

Managers often ask what they should do if a seller is not willing to do a pilot program with them. This is a valid question, but the answer is simple. If you are in this situation, do not engage more with the company. Any worthy supplier will consider that it is an honor to create a pilot program for you.

In addition, plan in advance and reserve funds for a Experimental AI budget. This should be where you turn when you want to try various solutions without too committed resources. Even if everything seems to be going transparently, give your team a lot of time to familiarize yourself with technology and adapt before making a purchase or scaling.

Prioritize data security and supplier transparency

When you are considering a platform, don’t forget that you are not only evaluating technology, but the company behind it. Sellers must be set up for a meticulous examination – if not more – than the technology itself. Make sure to work only with sellers who maintain the highest standards in terms of data security. They should comply with global data protection standards and ethical IA principles, and the platforms themselves must be certified SOC 2 Type 1, SOC 2 Type 2, the General Data Protection Regulations (RGPD) and ISO 27001 .

In addition, check that your suppliers do not use your business data for AI training purposes without explicit consent. The virtual meeting supplier Zoom is an example of a popular company that had foreseen To harvest the content of customers to be used in its AI and ML models. Even if they have not finally carried out these plans, the incident should raise concerns for businesses and consumers.

If you put a dedicated AI advance in charge of this field, this person can manage all data security needs and ensure organizational compliance. It may look like additional useless work, but it is essential. Remember that everything you need is a single data violation by one of your suppliers to make you lose customer confidence – otherwise your customers.

Final reflections

Managers must use a structured approach to assess AI solutions to obtain maximum value on their part. Concentrate first on problem solving, closely followed by tests and pilot programs, data security and the identification of tangible value. AI can be extremely powerful, but only when applied to the right problems after careful selection and implementation.

Arjun Pillai is co-founder and CEO of Docketai.