With Generative AI on the horizon, businesses face significant hurdles in bringing AI to their day-to-day operations. From redefining roles to adapting decision-making, the road ahead is challenging. In this article, you’ll find the main obstacles and the essential steps you can take to embrace Generative AI’s potential.
- 1st challenge: continue to equip ourselves with modern and robust technological foundations
- 2nd challenge: redefine roles and blur the boundaries between IT and business.
- 3rd challenge: reinvent the culture of decision-making and reaction time of the company.
1st challenge: continue to equip ourselves with modern and robust technological foundations.
First of all: the Cloud will be an essential accelerator for the development of Generative AI capabilities. Why? It allows us to access the phenomenal computing power that these models need. Secondly, it brings the necessary elasticity, especially in learning phases. Above all, it also gives us access to thousands of specialized graphic processors. However, it will be a struggle for many organisations to acquire these processors quickly because Nvidia’s order books are already full until the end of 2026.
Secondly, data will be the fuel for these algorithms. As mentioned, large language models, LLMs, will certainly be common to all companies. The real difference lies in the data that feed these models specific to your businesses, and your challenges. Hence the strategic importance of data governance and modern platforms to manage them. Will you connect your “on-premises” data to your LLMs? What about all the data trapped in your mainframes? And how do you effectively connect these old-fashioned applications to these Generative AIs? Do you have the right data? Are they in the appropriate format? Do you know how to manage such volumes? What is the real quality of the data in your databases?
And finally, cybersecurity issues are more critical than ever. Data sovereignty, model protection, training data security but also in Generative AI, ethics, should already be at the heart of your concerns. It is essential to embrace technologies wherever they come from – and explore even European options.
2nd challenge: redefine roles and blur the boundaries between IT and business.
AI will strongly shake up our way of working. We are on the threshold of changes that can be described as “tectonic”. When we can transfer part of our intelligence into an algorithm, the impact on jobs is inevitable. The question is not whether it will happen, but rather: which part of our intelligence are we willing to entrust to algorithms?
And it doesn’t have to be job destruction, the main challenge will be about change. About redefinition, perhaps abrupt, of the notion of profession, and of the relationships between the various departments of a company.
This concentration of democratised technologies and knowledge makes business teams less dependent on IT, and conversely, allows IT to deliver, industrialize, and test more with less. It is time to redefine the relationships between Business and IT, to face this tectonic evolution. Hackathons, labs, agile cycles… all means must be put in place to achieve this.
3rd challenge: reinvent the culture of decision-making and reaction time of the company.
We are going to witness the clash of temporalities as Generative AI spreads throughout the company: between the hyper-agility brought about by this revolution and the traditional delays inherent to organisations and culture.
For this, IT must become a facilitator that creates platforms rapidly and scales them, now and simultaneously with everything it is already doing.
Faced with all these challenges, the real risk is to put down the pencil, watch, and wait for proven solutions to emerge…In other words, it’s vital to start addressing known cases today. But it’s especially important to focus on the currently unattainable use cases. They’ll become achievable sooner than we think. The competitive edge lies in embracing these challenges.
