In practice, companies still find it difficult to make the transition from thinking about AI as a source of innovation to a critical source of business value. There’s a state of paralysis beyond the pilot. Why? Until now, there hasn’t been a proven blueprint for scaling, and organizations can fall into some common traps. First, companies don’t have an AI roadmap or "route to live"—the steps to take their AI project from POC to production, effectively and expediently. AI is different from "traditional" software implementation projects, which companies are typically set up to deliver. Changing the status quo requires agility, openness to trying a new way of working and the ability to recognize when an idea works—and when it needs to be scrapped.
Second, the unfamiliar landscape of AI also means businesses can be tempted to fall back on their time-honored behaviors, reinventing the wheel and building from scratch. Big mistake. There are many proven, low-cost AI options to buy "off the shelf" and start using right away. It is key to leverage what already exists, customize as needed for the organization and start proving the value of AI as the first step to successful scaling.
But don’t get bogged down in the technology. Be driven by the business strategy and vision, and let that dictate the AI approach. Focus on finding the right way of working that will allow AI to flourish, diversifying skills and talent beyond the data scientists. And get the right governance approach in place from the outset, with outcomes in mind. Applying these critical success factors can help you unlock a new wave of exponential value by scaling AI successfully.