AI service providers: challenges and solutions on the way to your first AI project

Keeping track of the market for artificial intelligence has become an art in itself, even for AI service providers, meaning the people who work in the field. All over the globe, capable people are working on advancing AI. By now the topic has reached a level of maturity that makes it very interesting for a large number of application scenarios.

Our specialists, too, are repeatedly faced with the task of assessing whether AI technologies are a good fit. Just recently I had a client who was wondering: "Is it worth using AI in my process, or is the time not right yet?"

Because many people are probably in a similar position, I would like to show you, from the perspective of an experienced service provider for artificial intelligence, how you can master the four most common challenges on the way to your first AI project.

‍

‍

1 Identifying the right use case, with or without an AI service provider

Challenge: Identifying the first use case for using AI in your own company can be difficult. Quite often the internal know-how is missing to realistically assess expectations and AI potential in relation to your own processes.

Solution: In order to develop as precise an idea as possible of how AI could be used in your organization, one important step is to get an insight into existing AI solutions. Introductory workshops from AI service providers can help here. In a workshop setting you can then apply problem-oriented, solution-oriented or data-oriented methods together with specialists in order to work out real business cases. The next step is also the most important one: build a prototype for your most promising business case quickly. That is the only way to validate with real data whether your potential solution really does solve the problem satisfactorily.

‍

‍

2 Building acceptance and trust with the help of your AI service provider

Challenge: What good is the best AI solution if your colleagues do not use it? For the solution to be used and accepted by the employees and customers in your company, trust in the technology often has to be created first. Of course this does not apply to every company to the same extent. The most important factors here are the characteristics of the AI in use, the characteristics of the users and the decision-making situation. Incidentally, the classic worry that people fear their job could be replaced by AI is something we have hardly encountered as an AI service provider so far.

Solution: Make sure to involve the people who will be using your AI solution in the custom software development as early as possible. That is the only way to create both the best possible product and the greatest possible acceptance. Beyond that, I would point to the upcoming European AI Act and to Twilio's interesting proposal to introduce an "AI Nutrition Label". As with nutritional information on food packaging, the idea is to present all relevant facts about the use of AI in the respective product transparently. Transparency is the central point. Set out transparently where AI algorithms are used and to what extent they influence decision-making. Depending on the starting situation in your company, change management can help as well. For internal communication I would focus on the reasons and the goals (for example 30% savings) and take possible concerns seriously by answering questions transparently, if necessary with the support of AI consulting from a service provider.

‍

‍

3 Clarifying the need for and the availability of data with your AI service provider

Challenge: Some machine learning use cases require high-quality data in large volumes. For example when your AI model first has to be trained on many data sets. In my experience as managing director of a leading AI service provider, there is also a slight tendency to overestimate the quality of your own data. On top of that, companies often find it difficult to judge how information needs to be structured in order to meet the requirements.

Solution: Find out whether and which data you actually need. Alternative approaches now work very well, for example entirely without your own data, with individual data sets or with only a few example data sets. Further keywords here are "few-shot learning" and "zero-shot learning". Once you have established which specific information you need, you can still start collecting it systematically and preparing it in a structured way. Sometimes it is not too late for that. In addition, you have the option of using synthetic data or simulation data.

‍

‍

4 Deciding how to allocate resources

Challenge: One essential challenge is the allocation of your company's (limited) resources. By that I primarily do not mean money and time, but knowledge. Innovation projects also have a hard time again and again getting internal approval, because other projects in day-to-day business have higher urgency and priority.

Solution: If you find yourself at this point, my recommendation is not to start building your own AI team under any circumstances. Instead, look for a suitable partner as a (temporary) AI service provider who brings the technological know-how and the manpower. That way you can test your first AI project quickly and at manageable risk within an agile MVP approach, and scale it if needed. You will of course convince your management best with a fully calculated business case. Show that the solution you have in mind solves a relevant problem and thereby improves your process decisively.

Are you wondering what potential AI can unlock in your company? Request an AI workshop now.

‍

‍

What you can achieve with AI in general and with an AI service provider

The potential of AI is diverse and can offer considerable opportunities for your large or medium-sized company, in particular in the areas of automation, data analysis, personalization, customer service and cost reduction. In our experience as an AI service provider, these four areas are also the ones companies look at first when they want to implement AI technology and achieve measurable results quickly.

• Automating tasks: Automating repetitive manual tasks increases efficiency and considerably reduces the workload of your employees. What is particularly exciting is that the developments around language models (ChatGPT and the like) now also make it possible to automate tasks that previously could only be carried out by people because they were "unstructured". This applies above all to tasks in customer service. AI-based chatbots and virtual assistants are available to your customers around the clock and can also ensure fast support.
• Personalization: With the help of an artificial intelligence service provider, companies can create personalized offers and marketing campaigns, for example, in order to strengthen customer loyalty and increase customer satisfaction.
• Data analysis and prediction: Machine learning models make it possible to analyze large volumes of data effectively and to make predictions, which supports your data-driven decisions.
• Cost reduction and efficiency gains: AI can help to lower your operating costs by optimizing processes and improving the use of resources, which in turn can increase your profit margins.

‍

‍

Conclusion

In order to discover AI potential in your company, a broad market overview, regular exchange with other AI specialists and practical experience also play a central role. Be honest with yourself here and, if the know-how is missing at the beginning, rely on a technology partner as a (temporary) AI service provider before you build your own team.

Are you also asking yourself whether and how AI can move your company forward? Or would you like insights into specific use cases from your industry? At SPRYLAB we do not carry ready-made answers around with us. What we do bring is a great deal of expertise, curiosity about your challenge and a passion for AI technology. On our AI services page you can find out more about our offering and our expertise around artificial intelligence.