Living in the Digital Globe (July 2024)

1. The lag of French companies in the digital sector is one of the causes of the lag of the French economy.

France’s economy is slowing down. One of the reasons why France’s economy is slowly lagging behind other economies is that France is lagging behind in digital. Digital is not just a technical issue, it affects the way France does business, it affects the margin and the revenue that France gets from its business, it affects competitiveness and productivity. For a company, digital is a way to scale up, I mean to smooth out costs as you grow, is a way to innovate quickly, to connect more easily with business partners.

France, Germany, United States GDP per Capita (Word Bank)

On the other hand, statistics show that investment in France is higher compared to United States and Germany. So this ask a question of efficiency, money is not conveyed to activities which are able to improve business, I specifically mean digital activities.

Gross Fixed Capital Formation in GDP% (Word Bank)

In short, in terms of digital, French companies suffer from a lack of understanding, they have made a poor link between business and digital. They don’t invest enough or they invest in the wrong direction, I mean in the renewal of their existing technologies. I wrote a =>paper on it few years ago.

Why is digital important to business? Because it makes any work environment accessible anywhere, anytime, on any device. Business events are captured and acted upon instantly. Such a company is at scale, has global processes, uses global solutions and leverages its data. Costs are flat and margins are higher.

If you are a shareholder in a French company, I recommend that you pay attention to promoting digital talent at the highest levels of your organization, investing in digital, improving the digital literacy of your employees, and managing and leveraging your data.

2. Becoming data-centric is the right approach to catching up in the digital age

Digital companies come either from startups that have scaled up to become global players in just a few short years, or from older companies that have grown externally.

The former have natively homogeneous business processes, homogeneous technological solutions and, with a few exceptions, are natively data-centric. Their main challenge is to expand their product portfolios to better meet market expectations.

The latter face the challenge of homogenizing their processes in order to be able to homogenize their technological solutions. They are not natively data-centric, and have little capacity to scale up. They must take up the challenge of rationalizing their product portfolio, business processes and technological solutions to become data-centric.

Their strength lies in the diversity of their product portfolio, which is also their weakness in terms of internal costs. Becoming data-centric is a way for them to launch and control the transformation dynamic.

Data is business: sales transactions, production, finance, etc. It’s obvious that mastering data means making better decisions, on time, and therefore succeeding in your strategy. Unless the cost of mastering data is higher than the business gain, as is the case for companies operating in very mature market segments.

For other companies, whatever their size, with growth potential and ambition, digital transformation and data centricity are a must.

3. All companies are rushing into artificial intelligence, but what will the benefits be in the end?

Goldman Sachs funded a study Gen AI : Too Much Spent, too little benefits. Although tech giants and beyond are set to spend over $1tn on AI investments in the coming years, Daron Acemoglu, Institute Professor at MIT, and Jim Covello of GS are skeptical about the payoff. While some companies may reap the real benefits of this promising technology, they point out that most will get little.

Most of the companies’ money will be captured by tech giants and consultants surfing on the companies’ fear of being AI laggards. A lot of effort will be spent by companies to improve the AI literacy of their employees and their ability to use the tools that vendors provide to all companies. So no real competitive advantage would come from this kind of investment, which is actually a cost.

The expected payback will come from investments in innovative use cases that can drive business for companies, such as simplifying complex transactions. For example, helping customers understand complex offerings, speeding decision making, and ultimately shortening the sales cycle for such offerings. It will pay off for complex industrial projects. Logistics and complex synchronization would also benefit from AI and add value to industry. The same goes for research and healthcare. This will be the true digital world.

To reap all these benefits, companies would have to master their data, I mean be able to manage their data at scale, to become truly digital. This is the main goal of modern data management practices and the focus of DAMA France.

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