At last year's Code & Cloud Conference in Richmond, Virginia, an idea raised in one of the keynote talks by Nvidia representative Jessica Clark echoed some of the conversations we have had lately within our own Center of Excellence in AI (AI-COE) at Ippon. The idea is that many organizations spend disproportionate time on small AI initiatives that yield limited returns while delaying the groundwork needed to unlock real value from AI. We have seen this in our conversations with clients. Most want to start small: a chatbot built for a single team or purpose, a summarization tool plugged into a single workflow step, or a prototype tucked away in an internal Slack channel. These “low-hanging fruit” projects are attractive because they promise quick wins with minimal investment. They allow organizations to test the waters without committing to a full transformation. But after over a year of working with teams across industries, talking to clients, and learning more about how to measure the value of AI, this pattern has become clear. The cost and effort required to adopt AI are largely fixed, but companies are focusing on quick wins without realizing that their goals are too narrow. Regardless of whether the first initiative is a small prototype or an ambitious, strategic program, the one-off experiments may feel lightweight on paper, but in practice, they drag companies through the same organizational processes that larger projects require. So why not make a bigger leap? The companies that are moving quickly and outperforming their competitors aren’t the ones dipping their toes in and testing the waters. They’re the ones that commit early, establish a clear vision, build the necessary foundations, and approach AI as a capability to be integrated broadly. And that is where Ippon and our AI-COE can help to shift the conversation as we move into another year of AI projects with our clients.
The initial work required with AI adoption looks almost identical regardless of the size of the first project. Before any model can be deployed in production, every organization must establish secure access to its data, select tools or platforms for hosting and monitoring models, and align with security, compliance, and legal teams. These steps take time. Even if the initial use case is as simple as text summarization or boilerplate generation, the infrastructure and governance needed to support the experiment resemble the foundations for much larger AI initiatives. The same is true for organizational readiness. Introducing AI, even in a limited form, forces process changes that affect multiple teams. Employees need guidance and training on using AI responsibly, legal teams need clarity on risk and policy, and engineering teams must determine acceptable quality, testing, and deployment practices. These adjustments are not incremental; they require coordinated effort to implement. Once an organization has done this work, it has effectively built the scaffolding required for far more impactful use cases, but many companies are still thinking too small.
Small, isolated AI projects are no different from any other POCs that can die on the vine because they are rarely designed with integration in mind. A prototype might function well within a single team’s workflow, but when the company wants to expand its use or plug it into systems that matter, the project must be rearchitected nearly from scratch. The initial “quick win” becomes a disposable artifact. These small experiments also produce confusing or misleading signals. A modest return on a tiny use case can make leaders feel that AI is overhyped or not worth the investment. Alternatively, an early prototype may show promising results, but as soon as teams attempt to use it in production, they discover unaddressed challenges involving data access, security, reliability, or compliance. Instead of building reusable foundations, these small experiments that cannot scale are misleading the AI investments. This is where organizations that choose to invest early in infrastructure, governance, and shared patterns can deliver new AI capabilities at a fraction of the effort.
Committing to AI does not mean launching a massive, multi-year program that attempts to reinvent everything at once. What it means is thinking through long-term goals and acknowledging that AI is a capability akin to adopting the cloud or DevOps, rather than installing a single tool. Capabilities require platforms, shared systems, and long-term thinking. When companies invest in foundational systems early, they create an environment in which AI work becomes far easier. Shared data pipelines, common integration patterns, unified access controls, and reusable knowledge sources reduce the complexity of each new project. Rather than reinventing the wheel for every initiative, teams can build on the same stable platform. Just as importantly, a strategic approach ensures that AI work aligns with real business goals. Instead of pursuing isolated prototypes, companies can map AI investments to specific improvements, revenue opportunities, or customer experience enhancements.
Successful organizations still begin with small, manageable projects; the difference is that these projects are anchored to the long-term vision. Leadership can articulate what an AI-enabled future looks like for their business and then select initial use cases that put the essential foundations to the test. In this approach, early projects are intentionally chosen to validate systems, policies, and workflows that will be used repeatedly. The projects are small, but the strategy is not, and every step builds reusable knowledge and infrastructure that can be trusted later.
If your company is exploring how to get started with building AI into your workflows, consider working with Ippon to help look beyond the easy wins. Building sustainable AI capabilities can begin with an assessment of your AI maturity. This includes defining where AI will deliver the greatest business value, which will depend on your current systems, aligning your goals with your current capabilities, and identifying the areas that need improvement. AI maturity is about understanding where data lives, how it can be accessed securely, and which processes currently cause bottlenecks and need to evolve. AI’s greatest impact is not unlocked through small, isolated experiments. While low-hanging fruit offers simplicity and speed, it can delay achieving more meaningful business value and often delays the foundational work that companies must do anyway. The organizations achieving the strongest outcomes recognize AI as a strategic capability and invest accordingly. They establish shared systems early, align their efforts to long-term goals, and deliver projects that build toward a coherent future.
While the cost of incorporating AI is already a factor, the true focus should be on building scalable solutions that deliver long-term value. Connect with us to discuss your AI goals or download our latest eBook, "Is Your Data Strategy AI-Ready?"