Start with problems, sandbox, identify Trustworth providers – a short guide to start with AI

With 77% Companies that already use or examine the use of AI and claim more than 80% that it is a top priority. However, the volume of the available solutions and the rush of marketing messages that accompany you can make a clear path difficult. Here are some guidelines with which they can be evaluated Skills of AI tools And determine the best fit for your organization.

If the media attract a certain platform or find that your competitors use it, it is of course to ask yourself whether you should. However, before checking a new system, identify the problems with which your company is confronted. What are your most important challenges? Its core needs? As soon as you have redirected your focus, put the solution that you consider through this lens.

If AI technology If well -defined, measurable problems that your company has met is solved (i.e. the automation of routine tasks or increasing team productivity) is worth exploring the tool. If it does not directly connect to solving your problems, continue. AI can be incredibly powerful, but it has restrictions. Your goal should be to only apply to the areas in which it can have the most sensible influence.

Pilot programs and experimental budgets

If you have found that a certain system strategically supports your requirements, you have met the first necessary criteria – but this does not mean that you make a purchase. The next step is to take the time to test the technology significantly by a small pilot program to determine its effectiveness.

The most valuable test uses a framework that is connected to important important performance indicators (KPIS). Accordingly Google Cloud: “For a number of reasons, KPIs are of essential importance for the provision of gene AI deployments: objective evaluation of performance, the agreement with the business objectives, the enabling of data-controlled adjustments, improving the adaptability, facilitating clear stakeholder communication and the detection of the ROI of the AI ​​project. They are crucial for measuring the success and the management of improvements in the AI ​​initiatives. ”

In other words, your test framework could be based on accuracy, cover, risk or which KPI is most important for you. You just have to have clear KPIs. As soon as you do this, gather five to 15 people to carry out the tests. Two teams of seven people are ideal for this. If these experienced people test these tools, you can collect enough entries to determine whether this system is worth scaling.

Managers often ask what you should do if a seller is not willing to carry out a pilot program with you. This is a valid question, but the answer is simple. If you are in this situation, do not get involved with the company. Every worthy provider will consider it an honor to create a pilot program for you.

In addition, they plan ahead and place funds for one Experiment Ki -Budget. Here you should turn around if you want to try out different solutions without over -the -go resources. Even if everything seems to go seamless, give your team a lot of time to familiarize yourself with the technology and adapt before you make a purchase or scaling.

Prioritize data security and the transparency of the providers

When you look at a platform, remember that you not only evaluate the technology, but the company behind it. Providers should be checked just as much – if not more – like the technology itself. Make sure that you only work with providers who maintain the highest standards Data security. You should adhere to global standards for data protection and ethical AI principles, and the platforms themselves should be certified as SOC 2 Type 2, SOC 2, the general data protection regulation (GDPR) and ISO 27001.

Also ensure that your provider does not use your company’s data without express consent for AI training purposes. The virtual meeting provider Zoom is an example of a popular company that had planned To harvest customer content for use in the KI and ML models. Although you did not ultimately do these plans, the incident should raise concerns for companies and consumers alike.

If you blame a dedicated AI lead for this area, this person can manage all data security requirements and ensure compliance with the organization. This may feel unnecessary and additional work, but it is essential. Remember that only one of your providers’ individual data violation is necessary so that you lose the customer’s trust – if not your customers.

Last thoughts

Managers must use a structured approach to evaluate AI solutions in order to obtain a maximum value. First focus on the problem solving, which are followed by test and pilot programs, data security and the determination of the tangible value. AI can be powerful, but only if they are applied to the right problems after careful selection and implementation.

Arjun Pillai is a co -founder and CEO of Dockets.