RCAS 2019: 4 Takeaways on Digital Transformation

The Retail Consumer Goods and Analytics Summit 2019, hosted by Consumer Goods Technology, was buzzing with the possibilities of disruptive technology.

AI and machine learning solutions in particular held the spotlight across the keynote speeches and general sessions. These transformative technologies carry immense potential for consumer goods companies and retailers, but they can also fundamentally shift the way employees operate. One of the biggest challenges discussed at RCAS19 was the process of successfully adopting AI-driven tools into the workplace.

For many professionals in the consumer goods space, there are several hurdles that must be tackled to reap the benefits of AI. These hurdles were addressed in depth during “Unlocking the Future: Using AI-Driven Analytics to Determine Corporate Strategy,” presented by Derek Smith, VP of Services at Prevedere, primarily:

  • Getting buy-in from executives. Transformative technology can seem risky, and many executives may be uncertain about pulling the trigger.
  • Building trust in the solution. So much of AI occurs “behind-the-scenes” of the tools that leverage it. How do employees ensure they’re seeing accurate results?
  • Managing the change that transformative technologies bring, including the fears and concerns of employees who are slated to adopt the tools.

These challenges were certainly easy enough to empathize with, but the thought leaders at RCAS19 came to bat with a roadmap to address them head-on.

Let’s talk through the steps to successfully adopt technologies that leverage AI.

1. Define the business challenge or opportunity and how AI can help.

AI has innumerable applications for consumer goods companies, and RCAS19 illustrated some of the most innovative adaptations in space. The conversation started with the opening keynote, “How Walmart is Designing the Intelligent Enterprise,” presented by Clay Johnson, EVP & Enterprise Chief Information Officer at Walmart.

In this talk, he explained how Walmart is using a machine learning algorithm for predictive analytics to hire better associates. The results? A dramatic decrease in attrition and an increase in quality ratings.

In this case, a specific problem was identified (lack of quality associates), and a tailored, AI-based solution was used to address it. AI advocates must understand the strategic vision and plans of their company’s executives to identify which challenges are the biggest pain points and which opportunities will deliver the most ROI. This step is key to getting executive buy-in.

Further, a major factor in successful adoption of AI-based technology is incentivizing the employees who’ll use the tool in their work. It’s important that both analyst teams and business teams understand the mutual benefits. Otherwise, analysts may fear that they’re being replaced by the tools, and business people may fear that they’ll be saddled with technology they can’t use effectively.

At AnswerRocket, our AI-powered analytics solution empowers business people to get answers to questions like “how did Brand A do last quarter?” and at the same time, it frees up data analysts from backlogs of basic reports. Explained this way, it’s clear how CPG professionals in different roles can leverage our technology to become more effective in their jobs.

In addition to clarifying how these tools will empower employees, businesses should also field open dialogues around employees’ fears and goals. Locking down some employee sponsors who can fully commit their time and energy to learning and adopting the tool can also be enormous assets later on in the implementation process, as they act as advocates across their team.

2. Conduct a proof of value to show the impact of AI.

Since AI is transformative, a focused business case is critical for successful adoption. Companies should start small, focusing on one area, department, or team.

For example, a leading CPG company has implemented AnswerRocket into their consumer insights team to address specific challenges like:

  • Understanding how brands perform comparatively in stores vs online.
  • Discovering how competitors are performing by brand and/or category.
  • Learning which actions will be most effective in gaining market share.

Similarly, companies who can fit their solution to the specific needs and challenges of a team can generate more ROI, which is why small-scale, targeted adoption is crucial at first. The impact of the tool should be quantifiable and tangible to its users. For the consumer insights team, questions that took days and weeks to answer previously were answered in seconds— a simple but effective figure in demonstrating the value of AI-powered analytics to the people who’d use it everyday.

While zeroing in on one area of the business, it’s also important to keep executives in the loop with transparent information on the process.

3. Integrate AI-powered technology into the business.

The next step in the roadmap is getting your employees comfortable with the technology you’re adopting. Terms like AI and machine learning are susceptible to buzzword treatment, and their true meanings can become obscured in a litany of marketing materials, sales pitches, and presentations.

According to Clay Johnson, ensuring that employees understand the technology they’re using is important to maintain usage and avoid confusion. Further, employees who better understand the implications of machine learning are more likely to readily adopt and advocate for it.

That’s why it’s important to follow these steps to integrate AI in your business, outlined by Eric Chen, Director of Analytics and Data Science at Unilever Asia Private Limited during his session, “Attacking the Biggest Business opportunities with Advanced Analytics”:

  • Get to a minimally viable product (MVP) as soon as possible. In this case, getting to a product that is 80% of the way there that can effectively demonstrate the business case and build momentum is better than waiting for perfection.
  • Get the tool in the hands of your employees so they can see the impact for themselves. Machine learning tools can manage perfunctory, time-consuming tasks like generating reports and crunching numbers so that employees can spend time leveraging their more creative, human skills.

It’s also important to involve middle managers in the process. These key employees will need to trust the tools to enable their direct reports to use them consistently. The sooner a team can drive the product themselves, the more they’ll back up its efficacy to other areas of the business, which brings us to:

4. Scale the value of AI across the business.

Further discussed by Eric Chen was the importance of scaling new technologies. When the integration goes well in one area, it’s time to think through the larger impacts AI-driven technologies can generate across other areas of the business.

This time, you’ll have vocal champions of the solution. Step back and let them advocate for the tool with their personal successes and accolades.

We’re confident that great AI-powered solutions lead to empowered employees who ask more questions and generate more impact.

Want to learn more about AI-powered analytics? Schedule a custom demo with AnswerRocket.

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