- What specific results are we trying to achieve with AI?
- Are there simpler and more profitable solutions available?
- How will success be measured?
Many of my clients are surprised when I ask these questions, which is a bit worrying. I am there as AI consultant; I could easily keep my mouth closed and collect my rates. I suspect that other architects are doing exactly that. Companies must realize that the misuse of this technology can cost between five and seven times more than the development of traditional applications, implementation and operations technologies. It is likely that some companies make mistakes that end the businesses. However, these questions are fundamental for the problems to be solved and the value of the solutions we take advantage of, either at AI or not.
The elements of a successful plan
Instead of embarking on large -scale AI implementations, start with smaller and controlled pilot projects adapted to well -escort cases. These projects evaluate the effectiveness, the costs of the model and identify the potential risks. IA technology is evolving rapidly. Implementing today’s avant -garde models or tools does not guarantee long -term relevance. Companies must build adaptable modular systems that can grow with the technological panorama and remain profitable over time. While planning a pilot project, take into account the following:
- Prepare your data. AI systems are as good as the data they trust. Many companies go up to AI initiatives without first evaluating their data repositories. The key data preparation steps include guaranteeing data accuracy, consistency and quality. Finally, you create pipes that guarantee that IA systems can access and process the necessary data efficiently.
- Be realistic. Like cloud services, AI can have hidden costs, from computer resources to training large data sets. Companies must analyze the total property cost and the viability of implementing AI systems based on current resources and infrastructure instead of depending on optimistic assumptions.
- Acquire skills. Laning tools to a problem does not guarantee success. AI requires knowledgeable equipment with the skills to design, implement and monitor advanced systems. Companies must invest in socking workers, create interfunctional equipment and hire experts that can close the gap between commercial needs and AI capabilities.
- Implement governance. AI introduces ethical, security and operational risks. Organizations must establish clear structures to monitor the performance of the AI system and mitigate the risks. If AI implies confidential data, you must establish governance standards for privacy and data compliance. Ensure transparency around how AI makes decisions and avoid excessive use or misuse of AI technology.
The Ai-First movement is a huge promise, but enthusiasm puts us at risk of repeating the expensive mistakes of the cloud era first. With AI, the lesson is clear: decision makers should avoid instinctive reactions and focus on long -term success through a careful strategy, planning and disciplined execution. Companies that adopt a reflexive and deliberate approach will probably lead the future driven by AI, while others fight to undo expensive implementations and myopic. The time to plan is now. As we have seen, “move first, think later” rarely works.
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