<img src="https://secure.leadforensics.com/133892.png" alt="" style="display:none;">

Everyone agrees that AI has lots of potential value

From our phones to our inboxes, vacation plans shopping, AI permeates everything we do today.Our apps have learned to recognize our faces and understand our speech, our inboxes have learned to sort important messages from spam, and travel sites present optimized itineraries and accommodation options while some retailers seem to know what we want to buy even before we do. The day is quickly coming when AI-driven businesses are not just out-performing their competition – they are the only kind of businesses left.

What we think of as “AI” is more and more already proven technology and a standard part of the systems we work with every day.


AI has the potential to revolutionize any industry, big or small, old or new. Take the example of one company that produces bread products for resale in different types of retail stores in different markets. Having the right amount of items in stock at the right times is crucial for earnings, but the company sees that up to 40% of its product might go unsold in some stores while others sell out by midday. Here, AI can help translate large amounts of data about product types, store locations, buying behaviors and times of purchase into a set of predictions for what should be delivered where and when. Optimizing the supply strategy to the demand will not only have a major impact on earnings but also considerably reduce food waste with obvious climate benefits.

Another example is a company that sells heat pumps and related service agreements. When some pumps inevitably malfunction or break down the company incurs both service costs and unhappy customers. By incorporating sensors in their products that report their real time status over the internet, the company’s systems can monitor for threshold conditions that trigger alarms and provide detailed forensics to help with diagnosing problems. With AI, the company can take another step toward more predictive maintenance: the AI can learn from historical data to identify early warning signs of impending failures and alert customers or service technicians before they happen. These allow users and service personnel to address issues before they become failures: increasing uptime, improving performance, and improving customer satisfaction.

What You Don’t Know: It’s surprisingly easy to get started with AI

Today, everyone recognizes the enormous potential of AI. What far fewer yet discovered is that modern AI require a huge staff of data scientists with PhDs or even enormous budgets anymore. Using AI in your business is often more about leveraging the built-in AI capabilities of existing systems and cloud infrastructure than about designing new algorithms or data research.

What we think of as “AI” is more and more already proven technology and a standard part of the systems we work with every day. What businesses most often really need are solid data engineers and “AI mechanics” who can help implement the technology and properly integrate it with their existing operations.

For one example, Microsoft has integrated many AI tools into Power BI and the larger Power Suite ecosystem, making the technology available to anyone who wants to work with it in the context of business intelligence in a standard system.

I elaborate on AI capabilities in Power BI in this blog post: https://www.columbusglobal.com/en/blog/ai-and-power-bi-opportunities-for-business-development 

Similarly, Microsoft Azure offers a wide range of off-the-shelf APIs, pre-trained AI models, and low-code development tools to help companies to develop and roll out their own AI solutions in the cloud. Price is also much less of a concern these days, as there is often strong competition between different cloud providers of industry-standard tools and techniques.

In the end, no company should be deterred if they want to push their data to its full potential, improve processes and workflows and strengthen their business with AI. Indeed, the day may soon come when no company can afford not to.

In case you have not read my other blog post about what AI can do for you, I recommend you to do so: https://www.columbusglobal.com/en/blog/what-can-ai-do-for-you 

I hope that this blog found you well, and feel free to get in touch if you have any questions.

And if you are looking for a way to get started, I recommend reading our e-book: ‘6 Steps to Be Successful With Advanced Analytics’.

Grab the eBook


Discuss this post

Recommended posts

Making use of your organisation's data is essential for staying competitive in any industry. Having a modern data platform is a must, and it is important that management makes the right decisions for their investments from the start. We generate ever increasing amounts of data with systems and tracking that covers almost all of our business processes. Therefore, being able to leverage that data via a modern data platform is a priority for most organisations. This article covers: • What business leaders need to know about modern data platforms and their benefits • How you can get started with a modern data platform Modern data platform is a term that's thrown around with little clarity or consistency. Depending on the author it can mean multiple things and cover various technologies. On top you'll also hear terms like lakehouse, data mesh and fabric which make it even more difficult to understand what one actually is. Therefore, we’ll explore a few clarifiers to define and explain a modern data platform in an understandable way.
The hype around the rise of generative AI technologies makes huge promises about the potential of the technology. Yet it would be fair to say the vast majority of organizations are only experimenting with the technology or using it in isolated use cases.
If you organize your data and use AI strategically, you can make better decisions faster. You can for example improve your market understanding and forecasting, optimize your maintenance or reduce food waste. Choose what is most important for you!
True magic happens when you deploy an AI/ML model into the real world, where it can make predictions, optimize processes and drive insightful decisions. It’s where theory meets reality and where algorithms start delivering tangible value. Whether you’re predicting customer behaviors, optimizing supply chains or diagnosing medical conditions, the deployment phase is where these innovations step out of the lab and into the heart of your operations.
As artificial intelligence (AI) and machine learning (ML) reshape industries and redefine possibilities, mastering the art of AI and ML model development and evaluation has never been more crucial. Behind every powerful AI or ML model lies strategic decisions, intricate algorithms and critical evaluations. Within the Model and Evaluate phases of the Columbus AI Innovation Lab, we delve into these aspects and how to use diverse metrics to evaluate a machine learning model’s performance, along with its strengths and weaknesses.
right-arrow share search phone phone-filled menu filter envelope envelope-filled close checkmark caret-down arrow-up arrow-right arrow-left arrow-down