When to Adopt AI: A Practical Guide for Businesses

Machine Learning and other AI techniques are transforming businesses and are at the heart of the productivity leaps we have witnessed in recent times. Despite the availability of AI solutions from major players like Microsoft, IBM, Google, and others, the adoption of AI in non-specialist environments still needs to grow. This is often due to a need for more understanding of AI's potential applications in business teams and familiarity with safe implementations in technology teams. Overcoming these deficiencies usually requires some experimentation.

In this article, we will explore two fundamental factors to consider when determining the applicability and necessity of Machine Learning:

• When it's impossible or challenging to implement a deterministic solution that operates in a reasonable time frame and produces satisfactory results for the business.
• When there is potential for improving results by accumulating executions or data.

Starting Small

It's important to note that we don't recommend "starting big" when adopting AI. Instead, we recommend looking for smaller components with well-defined responsibilities but substantial potential gains.

In our experience with clients, we have recommended and supported using Machine Learning for tasks such as sales forecasts, optimizing inventory levels, and workload distribution. These are areas where Machine Learning can provide significant benefits and enhance decision-making.

Challenges in Maintaining AI Projects

One key aspect is that maintaining a project involving computational learning or any other AI resource can be challenging. New skills will be required, along with expertise in new technologies and libraries. The process is iterative and demands collaboration with data experts to develop model architectures, success metrics, and application structures.

The Path to Digital Transformation

If there's a path to digital transformation, it is undoubtedly paved with data science. Both technology and business teams must embrace experimentation and the adoption of techniques like Machine Learning. To achieve this, they need to get hands-on experience. Waiting for the "perfect moment" in a rapidly changing landscape can be costly in the long run.

Conclusion

The adoption of AI and Machine Learning is not a one-size-fits-all endeavor. It requires a thoughtful assessment of where these technologies can provide the most value and a commitment to continuous learning and improvement. By starting small, addressing specific business challenges, and fostering a culture of experimentation, businesses can unlock the potential of AI and embark on a successful digital transformation journey.