The rapid advancement of AI has compelled organizations to reevaluate their strategies for operationalizing it. Despite widespread market hype and proven operational value, 72% of executives are exercising restraint with generative AI investments (2024 Accenture Pulse survey). Only 27% say their organizations are ready to scale generative AI, and 44% believe it will take more than six months.
This hesitancy underscores a deeper truth: Successful AI deployment requires more than just new technology. It demands reimagining business operations and reinforcing governance. A Celonis-commissioned survey found that 89% of leaders are actively implementing AI, yet 72% worry that weak processes could impede further success. In the same study, 80%+ emphasized that processes are the lifeblood of their organizations, and 99% recognized process optimization as essential to meeting business goals.
Three keys to operationalizing AI are:
Below is a closer look at each key.
Much like any automation, AI will struggle—or even exacerbate issues—if your underlying processes are flawed. By optimizing processes first, you set a solid foundation for AI adoption and smooth integration. By following the steps below, an organization can optimize its processes to improve the successful deployment of AI. Remember, the goal is not just to implement AI but to improve efficiency and effectiveness and to provide value to the organization.
Design Authority acts as the quality control mechanism that eliminates the biggest barriers to deploying and scaling AI. Think of it as the gatekeeper ensuring consistency, best practices, and high-value outcomes across AI projects. In the case of any technology or creative project where something tangible is being produced, the most successful outcomes occur when design standards are in place, and quality and consistency are measured. In the world of AI, establishing design standards and managing quality is the realm and responsibility of the design authority.
In addition to these critical roles, depending on resource capacity, some organizations extend the responsibilities of the Design Authority to include application assessments and feasibility studies of new AI technologies. These additional duties further ensure the organization’s successful deployment and effective use of AI.
As AI can infiltrate every process of an organization, from customer service and manufacturing to finance and human resources, strategic thinking about AI is paramount. It requires a conscientious commitment to aligning business objectives with the complexities and concerns related to AI and identifying clear-cut goals for its use. With a central AI management function, consistent practices emerge that increase the risks of data theft, ethical shortfalls, and compliance missteps.
An AI steering committee of executives and leaders from relevant departments sets the overall direction for AI initiatives and provides high-level oversight. It plays a significant role in successfully deploying and scaling an AI program in an enterprise
In summary, a steering committee can provide the leadership, governance, and support necessary to deploy and scale an AI program in an enterprise successfully.
The market excitement and business opportunity of AI have forced more and more companies to revisit their strategy and make deeper investments in technology. The speed at which technical capabilities increase and ongoing economic disruptions require action and strong partners. Companies that embrace AI solutions will do business faster, better, and cheaper — but at greater risk.
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