- What specific outcomes are we trying to achieve with AI?
- Are there simpler, more cost-effective solutions available?
- How will success be measured?
Many of my clients are taken aback when I raise these questions, which is a bit concerning. I’m there as an AI consultant; I could easily keep my mouth shut and collect my fees. I suspect other AI architects are doing just that. Enterprises need to realize that the misuse of this technology can cost five to seven times more than traditional application development, deployment, and operations technologies. Some businesses will likely make business-ending mistakes. However, these questions are fundamental to the problems to be solved and the value of the solutions that we leverage, whether AI or not.
The elements of a successful plan
Rather than embark on large-scale AI implementations, start with smaller, controlled pilot projects tailored to well-scoped use cases. Such projects evaluate effectiveness, model costs, and identify potential risks. AI technology is evolving rapidly. Deploying today’s cutting-edge models or tools doesn’t guarantee long-term relevance. Enterprises should build adaptable, modular systems that can grow with the technology landscape and remain cost-effective over time. As you plan a pilot project, keep in mind the following:
- Prepare your data. AI systems are only as good as the data they rely on. Many enterprises hastily jump on AI initiatives without first evaluating their data repositories. Key data-readiness steps include ensuring data accuracy, consistency, and quality. Finally, build pipelines that ensure AI systems can efficiently access and process the data needed.
- Be realistic. Like cloud services, AI can have hidden costs, from computing resources to training large data sets. Enterprises need to analyze the total cost of ownership and the feasibility of deploying AI systems based on current resources and infrastructure rather than relying on optimistic assumptions.
- Acquire the skills. Throwing tools at a problem doesn’t guarantee success. AI requires knowledgeable teams with the skills to design, implement, and monitor advanced systems. Enterprises should invest in upskilling workers, create cross-functional AI teams, and hire experts who can bridge the gap between business needs and AI capabilities.
- Implement governance. AI introduces ethical, security, and operational risks. Organizations need to establish clear structures to monitor AI system performance and mitigate risks. If AI involves sensitive data, you’ll need to establish governance standards for data privacy and compliance. Ensure transparency around how AI makes decisions, and prevent overuse or misuse of AI technology.
The AI-first movement holds enormous promise, but enthusiasm puts us at risk of repeating the costly mistakes of the cloud-first era. With AI, the lesson is clear: Decision-makers must avoid knee-jerk reactions and focus on long-term success through careful strategy, planning, and disciplined execution. Businesses that take a thoughtful, deliberate approach will likely lead the AI-driven future while others scramble to undo costly, short-sighted implementations. The time to plan is now. As we’ve seen, “move first, think later” rarely works out.