Rethinking AI: Why It Matters and Where to Begin
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:
- Reengineering Processes
- Implementing Design Authority
- Establishing a Steering Committee
Below is a closer look at each key.
#1 Reengineering Processes: Why AI Depends on Optimization
“The first rule of any technology used in a business is that automation applied to an efficient operation will magnify the efficiency. The second is that automation applied to an inefficient operation will magnify the inefficiency.” — Bill Gates
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.
Steps for Process Reengineering
1. Identify Key Processes: Identify and prioritize the business processes that could benefit the most from AI integration. These could be areas with a high volume of repetitive tasks or areas where predictive analytics could improve decision-making.
2. Understand Current Processes: Analyze the current processes thoroughly to understand how they work, their strengths, weaknesses, and areas for improvement. This step involves mapping out process flows and identifying bottlenecks.
3. Data Preparation: AI relies heavily on data. Therefore, ensure that your data is clean, accurate, and relevant. This might involve data cleansing, data integration, and data transformation activities.
4. Process Redesign: Redesign the identified processes to incorporate AI technology. This could involve automating repetitive tasks, using AI for predictive analytics, or using machine learning to improve decision-making. The redesigned process should be more efficient and effective.
5. Training and Development: Employees must understand how to work with AI and how it will impact their roles. This may require training and development to upskill staff and ensure they are comfortable with the new technology.
6. Implement and Test: After redesigning the processes and training staff, implement and test the new processes to ensure they work as expected. Make necessary adjustments as needed.
7. Monitor and Adjust: Continuously monitor the new processes and make adjustments as necessary. AI is a learning technology and will improve over time, so the processes around it may need to be adjusted accordingly.
8. Risk Management: Ensure that any risks associated with AI, such as data privacy and security, are managed appropriately.
#2 Governance: The Power of a Well-Defined Design Authority
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.
Key Responsibilities of a Design Authority
1. Setting AI Standards and Best Practices: The Design Authority establishes AI design standards and best practices. They ensure not only that these guidelines are accessible to all development teams but also that these teams are appropriately trained to apply them.
2. AI Design Review: As a critical checkpoint in the delivery lifecycle, the Design Authority reviews and approves AI design before the build phase begins. Early involvement in this phase prevents time-consuming and costly revisions during the code review stage and promotes the use of reusable components.
3. AI Code Review: The Design Authority performs code reviews to ensure adherence to best practices and the quality of AI processes slated for deployment.
4. Management of Reusable AI Component Library: The Design Authority owns and manages a reusable AI code components library. By driving the use of these reusable components, mainly through their involvement in design reviews, they facilitate a continual increase in delivery speed across the entire AI implementation program.
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.
#3 Structure: A Steering Committee to Align Strategy and Opportunities
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
How a Steering Committee Drives AI Success
Strategic Direction: The committee can provide strategic direction and oversight for the AI program. This includes setting the AI program’s vision, objectives, and expected outcomes.
Resource Allocation: The steering committee can ensure that the AI program has the necessary resources, including budget, personnel, and technology, to be successful. They can also help prioritize AI initiatives based on potential impact and return on investment.
Risk Management: The committee can help identify potential risks and challenges associated with the AI program and develop mitigation strategies. This includes technology, data privacy, security, and ethics risks.
Governance: The steering committee can establish governance structures and processes to ensure the AI program is managed effectively and ethically. This includes setting policies and procedures for data management, AI model development, and use of AI outputs.
Stakeholder Management: The steering committee can help manage various stakeholders involved in the AI program. This includes ensuring clear communication about the program’s progress, managing expectations, and addressing concerns or issues.
Change Management: Implementing AI can significantly change business processes and employee roles. The steering committee can oversee change management efforts to ensure a smooth transition.
Knowledge Sharing: The committee can promote sharing and collaboration across different departments and teams. This can help foster a culture of learning and innovation, which is crucial for successfully deploying and scaling AI.
Performance Monitoring: The committee can establish key performance indicators (KPIs) and monitor the performance of the AI program against these KPIs. This helps ensure the AI program delivers the expected value and benefits.
In summary, a steering committee can provide the leadership, governance, and support necessary to deploy and scale an AI program in an enterprise successfully.
Navigate AI’s Promise—Safely and Swiftly
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.
If you want to learn more about deploying AI safely and scaling its impact, connect with us to see how we can help you achieve maximum value from your AI investments.
