Stop Talking About AI, Start Solving Real Problems


With AI continuing to dominate the business and technology discourse, it is easy to get caught up in the hype and lose sight of what really matters - solving real problems. At Brightly, we have seen firsthand how successful AI is built on a thoughtful approach. Focusing on defining the problem clearly and ensuring that AI really is the right tool for the job can make all the difference. In this post, we will share insights on how to approach AI with a problem-solving mindset.


The Hype Cycle Trap


Figure 1: Gartner Hyper Cycle. (source:


The Gartner Hype Cycle in Fig. 1 illustrates the journey new technologies, like AI, undergo as they mature and find their way into value-generating use cases. This journey often involves phases of inflated expectations and disillusionment before eventually reaching a stage of productivity. At the peak of the hype, when expectations are at their highest, many fall into the trap of starting with a solution/technology (such as AI) and then start looking for problems to solve with it. This often leads to investing in technologies and solutions that do not address the most pressing challenges of your clients or deliver real value.

In times of great hype, such as the current AI boom, the importance of old-fashioned problem-solving cannot be overstated. By focusing on real challenges from the start, you can bypass the hype, not fall into disillusionment, and go straight to delivering value.


The Problem-Solving Mindset

Figure 2: A functional problem-solving approach isessentially simple – three steps guide you to finding the most relevantchallenges to solve to find the highest impact on your business.

To avoid getting sucked in by the hype and falling onto the roller coaster of Gartner hype cycle phases, we need to adopt a problem-solving mindset illustrated in Fig. 2.

Know your organization and customers

The first step is to deeply understand your organization, your customers, and their challenges.This means aligning with your organizational strategy and future goals, as well as understanding your role in your customers' lives and value chains.


Identify the most pressing challenges and opportunities

The second step is to identify the most pressing issues and opportunities, both current and future. As a precursor to step three, focusing on posing the right questions ensures that the ultimate solution serves a functional purpose and creates actual value.


Ideate solutions and start doing!

In the third step, now with a clearly defined problem, we should start discussing concrete solutions. And while AI, especially generative AI, is versatile technology, it’s crucial to remember that not every problem requires an AI solution. In fact, there may be simpler and more effective alternatives. Regardless of what kind of solution you decide on, start small, learn from your experiences, and iterate towards the best solution.


Case Studies: The Right and Wrong Way

Amid the AI frenzy, it is easy to take wrong turns. We have all run into various AI-driven solutions, ranging from invaluable to infuriating. Some of them scream ‘We wanted to do something with AI!’ In other cases, the AI solutions, while not complete misses, could have been solved better and cheaper without the AI. And, of course, there are many instances where AI has been put to great use, saving a lot of resources and enabling completely new workflows or ideas.


A prime example of a cringe-worthyAI solution is an AI Cooking System you can already find in the wild. It is a touch-screen thingamajig, which does not actually cook, but rather tells youhow to cook. Simply put, they have reinvented the plain cookbook. Indeed, starting your journey from the solution instead of the problem, you might end up solving a non-problem.


Already a classic among AI consultants and salesmen is an ‘AI Q&A bot for your company’s internal documents’. Here the problem to be solved is helping the employees find the correct information from thousands of pages of internal documentation. While anAI chatbot indeed solves the problem at hand, nine times out of ten simply building a proper search functionality would solve the problem completely. Remember to consider all possible solutions in step 3 of the Problem-Solving Mindset before committing to one.


Of course, AI can also be a truly groundbreaking solution. One example of a problem AI solves much better than any previous solution is discourse analysis. A global tech company faced the challenge of gaining insights into their brand image and the reception of their product launches. Traditionally, this would have required either a team of data scientists or an army of human laborers, turning the task into a time-consuming, large-scale project spanning weeks. Instead, they implemented a solution that leverages GenAI’s capabilities in understanding discourse. This solution can perform fully automated, unsupervised analysis of the social media posts in mere minutes, allowing also for near-real-time insights into the public’s perception. Not only did the solution save significant resources, but it also freed up employees' time to focus on ideation and analysis of the results.



Key Takeaways

  • Start by identifying real problems, not just looking for ways to use AI
  • Use insights and knowledge from your organization and customers to find the most pressing challenges and opportunities
  • Understand the capabilities and limitations of AI and other technologies
  • Consider alternative solutions before committing to AI
  • Start small, learn from your experiences, and iterate towards the best solution

At Brightly, we have seen the power of a problem-solving mindset firsthand. By focusing on real challenges and opportunities, we have helped our clients across industries deliver transformative solutions powered by AI, data, and IoT. Remember, the key to success with AI is not just talking about it - it is using it to solve real problems.



If you would like to learn more about how we can help your organization solve its toughest problems, please get in touch.

About the author
Janne Solanpää

Armed with a tech PhD and a strong background in AI, data science, data engineering, and software engineering, Janne Solanpää is a seasoned specialist in the field. His expertise lies in designing and implementing scalable data infrastructure on leading cloud platforms and in developing innovative software solutions. Leveraging advanced analytics and cutting-edge AI solutions, Janne has been instrumental in helping businesses unlock the potential of their data, driving growth and success.

About the author
Elina Korpela

Elina is a creative thinker specializing in service design. She is dedicated to defining business and service concepts that often come alive in digital products. Drawing on her design expertise and background in business, Elina has contributed to the design of services for large international companies and early-stage startups, particularly in the industrial and IoT service sectors.

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