Lessons from Past Technology Trends: Avoiding the Hype and Finding Real Value with AI

If the past 50 years of IT developments have taught us anything, it’s that businesses of all kinds tend to lose enormous amounts of money when they jump on new technology trends, for three main reasons:

1. They simply act too quickly.
2. They lack a clear understanding of the value they want to create with their investments.
3. They have a poor understanding of how these initiatives should be implemented.

During the 1970s and 1980s, the trend among organizations was to build massive legacy systems, often customized to meet what each organization believed were very specific needs. In the 1980s and 1990s, the focus shifted to standardized systems, but often with extensive customizations because “no one else is exactly like us.” In the mid-1990s, the internet became the focus, and the most highly valued companies in the world were those building websites – until the great IT crash. From 2010 to today, digitalization has been the dominant trend, with businesses striving to “go digital.” Now we face the next big technology trend: Artificial Intelligence (AI).

But what can we learn from past technology trends to better manage AI?

It’s important to note that it wasn’t wrong to build custom systems or invest in large standardized systems. On the contrary, it’s now a given that organizations, regardless of size, need various types of system support to function efficiently. It also wasn’t wrong to invest in the internet and digitalization – these innovations have radically changed how we do business, shop, travel, and even fall in love. And it’s equally clear that various applications of what we now call AI will bring significant changes to our businesses and our lives.

The problem lies in the costs and risks associated with these technology investments. Through all these technology transitions, many organizations have spent enormous sums to achieve actual benefits, and there are countless examples of failed IT initiatives. There are numerous everal well-known examples of organizations that have lost anywhere from hundreds of millions of dollars on IT implementations that could have been avoided with better planning and understanding of the technology.

So, how can we avoid these mistakes now that AI is in focus? Here are some tips for successfully integrating AI into your business strategy:

1. Define clear goals and problems to solve

   Before implementing AI, identify the specific problems AI is supposed to solve or the goals it will help the business achieve. AI should be seen as a tool to accomplish clear and measurable business objectives, rather than a solution in search of a problem.

2. Start with small, targeted projects

   Instead of trying to implement AI on a broad scale, start with small pilot projects where the benefits and risks are more manageable. These projects can serve as testbeds for learning how to best integrate AI and provide an opportunity to scale up after successful implementation.

3. Ensure access to relevant data

   AI systems depend on high-quality data to function effectively. Ensure that your business has access to the necessary data and that it is accurate and structured. If your data isn’t ready, the AI initiative may fail from the outset.

4. Build competence and understanding within the organization

   AI is a complex technology, and it’s important that key people within the business understand its possibilities and limitations. Invest in education and skill development, not only within the IT department but also among key strategic leaders and decision-makers, so your organization is prepared to work with AI effectively

5. Follow an iterative and flexible process

   AI projects should be conducted with an iterative and flexible methodology, where lessons from previous phases are used to improve and adjust the project’s direction. This reduces the risk of major failures and allows continuous optimization of AI solutions.

6. Consider ethical and legal aspects

   AI can bring ethical and legal challenges, particularly regarding data usage, decisions affecting individuals, and the automation of tasks. Ensure that AI projects comply with current laws and that there is an ethical framework for the use of AI within the organization.

Perhaps most importantly, don’t listen to all the “gurus” who talk about the need to create an “AI strategy.” The same individuals and experts wanted us to create “digitalization strategies” and “internet strategies.” Instead, the focus should be:

How can we use or integrate AI, digitalization, and all the other tools at our disposal to help us realize our strategies?

Of course, new tools can lead to changes in an organization’s strategy, as when banks launched “internet banking.” It was a major change in how banks served their customers, but it wasn’t just an “internet strategy” – it was part of the banks’ overall strategies.

The goal of a business can never be to use a tool. The purpose of the tools is to help the business achieve its goals.