What ‘cloud first’ can teach us about ‘AI first’

In the early 2010s, enterprises enthusiastically embraced the “cloud first” ethos. Between 2010 and 2016, businesses aggressively migrated applications and data to the public cloud, spurred on by promises of lower costs, greater efficiency, and unbeatable scalability. However, this movement quickly revealed significant shortcomings.

Many organizations transferred applications and workloads to the cloud without comprehensive planning, failing to account for long-term financial implications, data complexities, and performance requirements. Today, we’re witnessing enterprises repatriate workloads back to on-premises or hybrid environments due to unexpected costs and a mismatch of capabilities.

Like the cloud first frenzy, enterprises are barreling toward the next big wave: the “AI first” mandate. This rush to implement artificial intelligence technologies without a disciplined, strategic framework is eerily familiar. If history is any indication, failing to plan carefully will again lead to substantial mistakes, wasted budgets, and underwhelming results.

The cloud-first cautionary tale

The drawbacks of the cloud-first movement weren’t apparent immediately. In theory, moving workloads to the public cloud seemed like an ideal solution to outdated infrastructure, and it gave the added promise of cost savings. However, these migrations were often driven by FOMO (fear of missing out) rather than practicality. Organizations moved applications and data without optimizing them for public cloud platforms, overlooking aspects like workload performance, governance, and comprehensive cost analysis.

Years later, many companies discovered that hosting these workloads in the cloud was far more expensive than initially anticipated. Costs ballooned due to unoptimized architectures, excessive egress fees, and persistent underestimation of cloud pricing models. That lesson is now painfully remedied by a return to hybrid or completely on-premises systems, but not without significant cost and effort.

What went wrong during the cloud-first boom wasn’t just flawed execution but a fundamental lack of strategic planning. Instead of understanding which workloads would genuinely benefit from the cloud and optimizing them for that environment, enterprises treated cloud adoption as a blanket mandate. As businesses face the AI-first mandate, they do so under similar circumstances: enticing technology, unclear benefits, and an overwhelming urgency to act.

Is AI the right tool?

AI is undeniably transformative. It has the potential to enhance decision-making, automate processes, and open up unprecedented business opportunities. However, companies are indiscriminately layering AI into systems and methods without carefully evaluating its suitability or ROI. Enterprises may use AI to solve problems it isn’t well-suited to solve, or deploy it at scales that far outstrip the ability of the infrastructure to support it.

Worse yet, some organizations are tackling AI projects without fully understanding their costs or the data complexities involved, especially in view of new data privacy and ethics regulations. Much like the cloud rush, companies risk building expensive, poorly optimized AI systems that deliver little value or introduce risk. The AI-first movement feels uncomfortably reminiscent of the early days of cloud computing.

If there’s one lesson to learn from the cloud-first era, it is that strategic planning is the backbone of successful technology adoption. Before adopting AI to keep up with competitors, organizations should assess their unique business goals and determine whether AI is truly the right solution. Not every business problem needs AI. Leaders should ask hard questions:

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.