Two years after Chatgpt, AI projects are multiplying, often without CAP or Clear King. To succeed, you have to master the costs, scalability and reliability, with a real business case.
Two years after the launch of Chatgpt, a massive awareness has taken place, accompanied by a strong dynamic and an increased desire to deploy projects in artificial intelligence and generative AI. Decisions remain largely dictated by the fear of missing an opportunity or to be distanced through competition. Organizations wish to explore the various options, while managers seek to capitalize on the craze to advance new initiatives.
The potential advantages are often perceived as higher than the risks, even when AI projects lack clear management. This has led to launches without real reflection on the expected results, on what success and on concrete means means a team to achieve the project and take advantage of it for the company.
This expensive approach led us to a peak in decisions taken in the euphoria, when many projects were launched without consideration for their long -term viability or for their return on investment (king).
How to adopt AI while keeping business control? While this technology continues to attract significant budgets, its adoption is still at an early stage of development and execution. The era of easy funding now seems to be over. Organizations must therefore ensure that each AI project generates a real king, and that it is not only based on the fear of missing an opportunity.
For an AI initiative to have a real impact on the company, it should be tackled through three essential prisms:
1. Estimate the cost and avoid the traps
At the time of the EA boom, many market players were encouraged to initiate new projects. The budgets have been redirected to initiatives managed by AI, without the cost being necessarily a priority. However, this enthusiasm and these investments have been hampered, because more and more organizations seek to control their costs and better understand the origin of the return on investment.
During the beginning of an AI project, it is essential to know how to experiment without engaging in significant funds on initiatives likely to fail. The ability to adjust the rising or downward resources allows companies to limit losses. Storage and calculation resources, central in AI projects, must be fully exploited to maximize the king. Acquiring graphic processors that remain unused for 18 months represents a heavily underused investment. It is better to be able to adjust the capacities according to the needs to keep cost control and create long -term value.
2. Master the extent of the project and its capacity for evolution
When an AI project is still at an experimental stage, its future magnitude remains difficult to identify, both in the test phase and in view of a large -scale deployment. It is crucial to be able to go from POC (Proof of Concept) to deployment, then to support the project over time. Companies must evolve without having to replace the existing infrastructure.
The Cloud is therefore a good experimentation lever, making it possible to adjust resources, explore the potential of AI, map the business value and plan in the long term. However, its cost can quickly prove to be prohibitive, so much so that a successful project can become economically unbearable. In fact, companies often ignore where they will be in 6 to 12 months. The project can take multiple directions, and the very definition of success varies according to contexts. In addition, there are regulatory and security constraints, as well as customer requirements. It may seem complex, but agile organizations can make it an asset.
It is relevant to consider “AS-A-Service” models, which eliminate uncertainty linked to consumption. Given the dependency of AI to data, a storage model “as a service” based on an evolving physical platform, capable of offering performance, flow and capacity to demand, makes it possible to respond immediately to the evolution of charges. This is an ideal solution for companies in the test phase, anxious not to finance an inactive infrastructure.
3. Ensure the reliability of the systems
Once the exploratory phases have passed, managers seek to make projects for real critical services for the company. Reliability and availability then become essential. In sectors such as bank or e-commerce, customer applications must be permanently accessible and cannot be based on unstable infrastructure. For reasons of reputation, compliance, cybersecurity or loyalty, the company cannot afford the slightest failure. Aiming total availability is essential, and this implies relying on suppliers capable of guaranteeing maximum reliability.
Return to the essentials in terms of financing
Admittedly, not all AI projects are excessively expensive. However, leaders are starting to end those whose expenses are not justified. Many have launched AI projects, convinced of their essential character to remain competitive. But the funding granted on the basis of simple intuitions are no longer accepted.
Projects without a clear objective, without a solid business case, are abandoned or never emerge. In the future, it will be necessary to show more rigor before validating the financing of an IA initiative.
It is time for companies to return to the essentials: instead of letting decisions be dictated by the fear of missing an opportunity, it is necessary to question the concrete impact of the project, its chances of success, its viability, its contribution to customer processes or interactions, and the availability of the necessary data. These are the elements that must be assessed before starting a budget. A structured business case and a formal verification grid must now precede any commitment.
