There is a difference between how businesses have been traditionally run (even with the recent digitization efforts) versus the emerging AI-powered enterprises.
Traditional businesses are limited by the firm’s operating model, which encompasses all the assets and processes to deliver value to its customers. As the firm gets bigger, its operating model becomes increasingly complex, and with complexity come all sorts of problems.
Data and information become siloed, making it more difficult to harness the power of data to inform decision-making. Cross-functional collaboration suffers as departments become more insular. The list of challenges goes on and on as companies grow in size and complexity.
In a new age, however, AI-powered businesses will scale more easily and continue to improve despite the complexity of a company’s operating model. Contrary to the way today’s businesses become more complex as they grow, if architected well, the businesses of tomorrow should get increasingly efficient as they learn what works and what doesn’t from the data.
Thus, there is a scaling litmus test. Do the gaps between the strategy promise and the operating reality increase or decrease as the number of customers increases?
Two Important Data Concepts to Drive an AI-Powered Operating Model
To achieve this AI-powered operating model, there will be some key concepts for companies to understand, refine, and perfect.
Operational Data Factory
A well-architected future AI company has a modern computing platform with data pipelines for algorithm development and experimentation.
The operational systems must be enabled or retrofitted with software-embedded workflows. The data must contain enough context and depth to enable the development, testing and deployment of algorithms that power an intelligent business operating system.
Virtuous Service Improvement Cycle
More data produced should enable better algorithms (whether they are autonomous or serve as a human aid or co-pilot), which should allow better services for customers. Hopefully, this means there will be more customers and, therefore, more usage, thus producing more data to continually feed and improve the algorithms that make the business tick.
Reimagining how your company creates value
Broadly speaking, there are five steps to creating a transformation roadmap for an AI-enabled business:
Step 1: It’s critical to comprehend and set an enhanced vision for an AI-enabled company strategy, especially how the products and services can be differentiated, positioned, their cost structures, pricing, etc. This enhanced strategy will be the input for AI transformation.
Step 2: The next step is to analyze the business processes, especially the customer value streams, and prioritize those based on the highest value and efficiency gains.
Step 3: Identify use cases with AI potential within the context of the value streams.
Step 4: Decide what use cases can be fully automated vs. which ones are still human with an AI-aid.
Step 5: Prioritize these use cases for high ROI and develop an AI-transformation roadmap. Delivering value along the way is critical since the transformation will be complex, with far-reaching implications for people, how work is organized, and the priority order of technology implementation.
We at Knownwell are sensitive to the adoption of AI to ensure that organizations realize the full potential of AI in a moral, ethical, and prudent fashion. While there is undoubtedly risk in utilizing AI to reimagine how your company creates value, we believe it’s far riskier not to embark on this exploration in this period of rapid technological and business transformation.
Ready to get started on your AI journey? Take our free AI Assessment to make sure you have the right foundation in place.
