Questions to Ask When Measuring ROI in AI Projects,
It’s important to answer these questions that help in measuring the complexity of the AI project and to formulate key success criteria:
Partnering with AI-friendly business units: Do you have a broad base of business sponsorship and alignment within the organization for AI initiatives? Are your business unit leaders familiar with the technology and the outcomes it may deliver?
Selection of projects: Do you have enough clarity regarding the AI use cases in consideration of tangible values and a direct production path? The ability of AI and analytics initiatives to generate concrete value varies, hence you will need some low-hanging fruit to keep the success stories going and balance out more ambitious projects.
Fostering stakeholder trust: Do you have strong sponsorship from the heads of the functions or units where the AI applications will be implemented? Have you and your finance team agreed on the KPIs and metrics for “monetizing” the AI project? This is critical and needs to be determined prior to launching a project, including high-level cost estimates and expectations.
Learn fast and deploy faster: Do you have the right tools, methodologies, governance framework and change management practices to manage the AI project lifecycle? All AI projects start as experimental in nature; they go through several iterations and course corrections during the development phase. It is advisable to start with a pilot to assess and illustrate the value of the application because early detection of failure points can allow for quick adjustments and go/no-go decisions saving time, energy, money and critical resources.
AI Infrastructure: Do you have the AI infrastructure, including data to develop, test and deploy the application? AI projects need a lot of data and require the right tools, algorithms, compute power and scalable infrastructure to make the project successful.
Soumendra Mohanty Soumendra Mohanty Chief Strategy Officer & Chief Innovation Officer, Tredence Inc
March 6, 2023
Company leaders often ask which AI project will get the highest and fastest return on investment. The answer is different for every organization. Soumendra Mohanty, chief strategy officer and innovation officer at Tredence Inc, provides a roadmap for employing AI for the biggest business benefit.
Business leaders are drawn to Artificial Intelligence to generate new revenue, save money, expand infrastructure to serve customers, and establish a sustainable competitive advantage. What’s often more difficult is determining the real value of AI investments. That’s understandable since AI can spark questions about organizational maturity to understand and adopt solutions, the costs of building and deploying AI solutions, the readiness of data and platforms to innovate, in-house skills and competency to execute AI projects, and similar issues of planning and execution.
These are all critical issues for CXOs concerned with the development and deployment of AI solutions. AI is an experimental and expensive initiative, therefore, it’s essential to have a playbook to help determine ROI for enterprise investments.
Not All AI Projects Are the Same
One way to think about an AI project is to understand what outcomes it will deliver and when: short-term projects that deliver immediate value in the near term and moonshot projects that deliver significant value over the long term.
For short-term AI projects, ROI is generally determined through quantifiable factors like improved productivity, process efficiency, cost savings and visibility into operations. On the other hand, long-term AI projects are concerned with strategic growth-oriented objectives, such as new revenue sources, market differentiation, identifying and creating new products for unmet customer needs, and more.
Alternatively, one can also think of breaking down AI projects into the following categories: automation, continuous learning, and autonomous.
Automation entails finding enough patterns in repetitive rote tasks, automating those with predictable outputs and enabling the higher-value imaginative activity to be performed by humans, which all fall into the category of short-term AI projects.
Continuous learning incorporates the ability to learn from observations and apply the learning to solve previously unseen problems, just as a human would do throughout their lifespan, which falls into the category of short- to medium-term AI projects.
Autonomous AI projects focus on generating intelligence that allows systems to act on their own, independent of human intervention, which is a long-term AI project.
Now that we have a fair idea of the types of AI projects, let’s reflect on what questions organizations should ask before initiating a project.
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