Operationalizing AI: Three Keys to Deploying and Scaling AI Programs to Maximize Value and Mitigate Risks | Automate Smarter
The rapid advancement of AI technology has compelled organizations to reevaluate their strategies and increase investments. Yet despite the market hype and operational value, 72% of executives purposely exercise restraint with generative AI investments. Only 27% in the 2024 Accenture Pulse survey said their organizations are ready to scale up generative AI, and 44% said it will take more than six months.
It is about more than just embracing AI. To effectively deploy and scale AI that maximizes its potential benefits, we must reimagine business operations, and governance needs to be enhanced. Most leaders (89%) say they’re actively implementing AI, but 72% worry that process shortcomings may hold back further success. That’s according to a Celonis-commissioned survey. Over 80% of those surveyed said that processes are the lifeblood of their organization, and 99% consider it essential to optimize their processes to meet their business objectives.
The three keys to operationalizing AI include (1) reengineering processes, (2) implementing design authority, and (3) establishing a steering committee. Read on for details on the approach.
#1 Reengineering – AI Models Rely on Process Optimization for Deployment
Optimization is crucial in successfully deploying AI in an enterprise as it prepares the organization for the changes that AI will bring, ensuring that the transition is as smooth as possible and that the full benefits of AI can be realized. Bill Gates taught us, “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.” AI is no different – applying it to a bad process makes it worse.
Preparing an organization’s processes to deploy AI successfully requires a strategic and systematic approach.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Risk Management: Ensure that any risks associated with AI, such as data privacy and security, are managed appropriately.
By following these steps, 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.
#2 Governance – A Well-Oiled Design Authority to Successfully Deploy AI
Design Authority eliminates the most significant inhibitors to deploying and scaling AI. Quality control is a vital part of any impactful project regardless of what is being delivered – whether this takes the shape of formal peer review or soliciting group feedback. The end goal is to ensure a high-quality output and, ultimately, value. 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 the context of successfully deploying AI, a Design Authority plays a pivotal role. Although the specific duties can vary between organizations, some common principles and responsibilities contribute to the successful implementation of AI.
- 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.
- 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.
- AI Code Review: The Design Authority performs code reviews to ensure adherence to best practices and the quality of AI processes slated for deployment.
- 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 Provides the Guardrails to Align Strategy and Opportunity
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.
- 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.
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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. To better understand how AI technology can be safely deployed and scaled, let’s talk.