Retailers evaluate and adopt causal AI to identify changes to improve their operations and gain greater insights into customer requirements, industry-wide developments, and strategies/tactics. At the same time, retailers are constantly seeking ways to improve their own decisions and the integrity of their decision-making processes. With the help of causal AI, they can identify and understand cause and effect in ways that were never before possible. The ability to draw causal inferences separates causal AI from more traditional iterations of artificial intelligence.
Causal AI doesn’t offer fuzzy correlations and generalized recommendations. It tells retailers why things are happening, identifies the causal connections behind those outcomes, and recommends highly specific interventions to help the retailer achieve its objectives. Retailers can even test those interventions before implementing them. Overall, causal AI helps retailers optimize their use of time, energy, and resources in ways that were unimaginable only a few short years ago.
But what exactly does causal AI implementation involve, what steps should the business take, and in what order? Retailers should start with these proven five steps to ensure a successful causal AI implementation:
1. Put the Right Team Together.
If a retailer has decided to leverage causal AI, they need a strong, multi-disciplinary team that understands the technology and the business’s core objectives. Though the size of the team depends on the size of the retailer, a standard team should consist of data scientists, data experts, technologists, subject matter experts, business managers, and executives. Let’s be honest: most businesses are way too complex for any single individual to have all the knowledge and skills necessary to arrive at solutions to the most complex problems, and this is what makes a multidisciplinary team an absolute must.
In addition, even though the ideal team comprises experts in their respective fields, top-down authority remains essential. Business executives and managers must be able to direct the team based on the retailer’s direction. Strong hierarchical authority keeps the team organized and focused. Too many AI projects fail because the data scientists work in isolation and lack sufficient contact with other specialists representing different areas of the business. A dynamic, multi-functional team, constantly seeking to improve its capacities, rectifies this deficiency.
2. Define the Problem and Set Goals.
The retailer should begin by defining (and understanding) why a specific problem exists and how it should be addressed before developing the AI model. Senior management should present its objectives in detail, and once they are done, the project should be broken down into small, achievable goals. For example, the retailer might ask themselves, “Why are we losing customers under 30 years of age?” or “What approach to marketing our subscription plan will give us the biggest bang for the buck?” or “Which retail outlets should we close to minimize long-term losses?”
The problem or issue has to be defined accurately, starting with a hypothesis. Then, business objectives should be prioritized based on feasibility, impact, effort, and cost. Results should be realistic and quantifiable. The right data has to be collected, variables have to be chosen, causal relationships have to be understood and established, and, finally, effects have to be estimated. The team should be mindful of what they know and don’t know. The goal is always to end up with a model that’s transparent and easy to understand.
The process, as noted, is iterative—a fusion of testing, data integration, and analysis. Once the model has been hydrated with data and analyzed, then, algorithms can be built based on the model. However, testing, validating, and refining the model to ensure accurate results is ongoing work; retailers should be aware of this reality.
3. Maximize Data Relevance and Integrity.
Ultimately, causal AI is only as effective as the data it has to work with. Retailers seeking answers to specific problems must collect enough of the right kind of data to generate a viable causal AI solution. They should be prepared to cast a wide net and keep an open mind. Some of the data they need may be buried within data lakes. Some may be found in systems of record. Transactional, governmental, survey, and social science data are incredibly useful. A ton of data, ultimately, will come from third-party commercial enterprises.
Retailers should consider the utility of data, which was originally meant for very different purposes. They can treat, condition, and transform that data to create data sets that can be aggregated with the data they already have. Retailers can avoid this by ensuring that their data are sufficiently diverse and closely monitored. Again, this is where data preprocessing can optimize input data dramatically, leading directly to effective causal AI output.
Ongoing data discovery to add newly discovered, relevant data over time is also important. Developing an effective causal AI model is ultimately an iterative process that depends on endless little additions and refinements over time. Absent ongoing data discovery, causal AI’s ability to generate solutions becomes impaired. The system’s results’ accuracy, reliability, and fairness depend heavily on filling in any data gaps as quickly and comprehensively as possible.
4. Prepare for Deployment.
To successfully integrate causal AI, the retailer must accurately assess its technical and personnel requirements. Everything from on-premise, hybrid, and cloud computing to the entire tech stack, as well as security, privacy, and compliance, has to be considered carefully.
Regardless of size, any retailer should lay the groundwork for causal AI by running a pilot program to test the utility. This allows the retailer to refine its methodology before scaling up across multiple channels. The pilot project should be a real test of the full rollout and provide unambiguous evidence about its prospects.
5. Deploy Causal AI.
For the retailer, deploying causal AI and integrating it into the existing system can pose a significant challenge. The issue isn’t in writing the code but with the specific circumstances of each retailer. For example, some intend to share their causal AI insights with the widest possible network of outlets, managers, and marketers. As a result, they may decide to build a web or mobile-based application on top of the causal AI model and develop APIs or other interfaces to facilitate seamless communication between the two entities. An intuitive application of this sort can deliver personalized insights, recommendations, and decision support—all of it pulled directly from the model’s outputs and all of it actionable. Other retailers intent on introducing causal AI into a production environment may have little use for this application. The core challenge lies in harmonizing causal AI with each retailer’s needs.
Ultimately, a successful deployment strategy depends on factors like cost, performance, security requirements, scalability, existing computing infrastructure, and business processes. Successful deployment also requires a sufficient number of skilled personnel and sufficient time and commitment. Finally, success requires the retailer to continuously monitor and refine the model. Absent this sort of commitment, sustainable progress is impossible.
With causal AI, retailers gain what they lacked previously: the ability to understand, predict, and shape individual customer behavior in a reliable and consistent way. Armed with a deeper and broader grasp of causality, they’re growing more confident in their decisions and decision-making processes. However, there’s no getting around the fact that adoption and full integration of causal AI is a major undertaking that requires sustained commitment.
The above steps are incredibly important because causal AI, not traditional AI, is set to change the world. Retailers have to grasp this reality—even if many feel they’re not quite ready—and act accordingly. Ultimately, their future competitiveness and long-term survival may depend on their ability to meet this moment.
