Out of Walmart’s fiscal 2021 revenue of $559 billion, $11 billion of this figure might have been attributed to forecasting and supply chain decisions driven by machine learning (ML) and artificial intelligence (AI).
Likewise, Procter & Gamble brands may have contributed $1 billion to fiscal 2020’s $71 billion through consumer and trade promotion decisions informed by ML and AI.
These are among the most progressive analytics organizations in their industries, if not any industry. Data science and solving “wicked problems” with advanced analytics are at the core of their cultures.
These estimates come by way of McKinsey research, which found that advanced analytics like machine learning applied to business processes such as forecasting and promotion produce growth of 1-2 percent of revenue.
That is exactly the goal CEOs have established for their organizations coming out of a difficult 2021.
In May of 2021, Gartner announced that its survey of CEOs found “over half report growth as their primary focus and see opportunity on the other side of the crisis.” Technology change and investment followed and “when it comes to specific technologies, CEOs see artificial intelligence (AI) as the most industry-impactful technology.”
Across all industries, there are significant economic opportunities for the taking with advanced analytics and AI. According to McKinsey, somewhere between $9 and 15 trillion dollars. Within the CPG industry alone, the estimate for potential supply chain improvement is nearly $100 billion.
If they are not already, CEOs will soon be looking for their teams to recommend ways to capture this value. To not is almost negligent to shareholder value.
Overcoming Adoption and Value Scale Challenges
From leadership and culture to strategy and project approach, the way AI and advanced analytics unfolds within an organization makes a huge difference to whether value is realized and scales. These are insights that executives must grasp quickly to make headway in 2022.
A dual focus on scale and continuous improvement are characteristics of analytic leaders you should model no matter your role – worker, analyst, data scientist, or executive. Thinking too small in scope or fixating on an endpoint risks mediocrity at least or failure at worst. If you are lucky enough to be part of a successful project or pilot but without a forward-looking plan for what’s next, scaling value beyond this point is unlikely. Leaders have discovered that:
- Outcomes for some use cases can be improved further with new or alternative analytic methods. While many Alteryx customers realize immense value from data-related tasks, it’s the ability to automate and share using Alteryx Server that allows users to consider how more advanced methods like predictive analytics can move the needle on business metrics.
- Some use cases reach a point of standardization, automation, and routine so they can be owned and operated by the business independently. This has been demonstrated by Walmart. In Alteryx terms, this also reflects the use of Server to expose workflows as analytic apps to potentially thousands of workers who depend on the insight but are not interested in developing workflows themselves.
- A pipeline of use case suggestions opens (or bursts) as the business learns to recognize what an AI use case looks like and how it creates value. Much like Walmart Chief Data Officer Bill Groves describes.
Addressing the Skills Gap
Even for mature organizations with data science teams, scaling use cases in pursuit of greater value is challenging. Supply chains especially have difficulty attracting the most skilled data science professionals. The best and brightest are more often attracted to working for digital natives. You thus either need to improve the productivity of existing quantitative professionals, or recruit others in the organization to upskill.
On the other side, organizations lacking data science teams or just getting started sense urgency to catch up. Faced with hundreds of technical and services options for analytics, they simply cannot execute a scalable AI roadmap if needing to master so many different approaches while reconciling these with existing tools, applications, and organizational models.
Both mature and less sophisticated Alteryx customers almost universally have greatly improved the time it takes to execute data-related steps within machine learning use cases – such as demand forecasting, inventory optimization, and machine maintenance.
Both also didn’t realize value overnight and first needed to learn how to apply Alteryx to their business. This is the role Alteryx services partners play in customer success. Bringing expertise in Alteryx, analytic methods, and business domain, partners lay the foundation for strategic solutions while supporting a customer’s journey to self-sufficiency and ongoing training.
Some of these customers also improved an insight from the current state by more easily accessing external data sources and applying more advanced analytic methods, like ML. The business outcomes in terms of sales, margin, customer experience, and productivity were in the many millions of dollars.
Similarly, companies that have already taken pieces of that $15 trillion AI pie did so by way of use case portfolios targeting smaller, but strategic projects that added materially to the top and bottom lines. They did so intentionally, not by accident, and not by leaving anything to chance. Alteryx can and should serve the role as accelerant to AI use case strategy.
Democratizing analytics using low or no code methods like Alteryx that make it easier for almost anyone needing or wanting access to data and insight offers a confident path to earning your share of that 15 trillion-dollar AI pie. Getting there is accomplished fastest with the greatest value through an intentional use case plan within functions like supply chain and beyond.