Bringing Real Intelligence into the Learning & Play Experience

Ramy Nassar, Director, Mattel Retail WONDER Innovation Lab

Do you remember your first toy?

Think back. Way back. Do you remember the first toy that you fell in love with? Maybe it was a doll or a model race car or a building set. Regardless of what that toy was, the impact on your growth and development is probably undeniable. It’s forged a permanent-and hopefully positive - memory that you’ve held onto for decades. Just like every other industry, the world of play is being disrupted on all fronts. Kids are spending more time in front of screens than in open fields, and for some reason, they seem to know how to use mobile technology better than we do-without ever being explicitly shown how.

Technology is impacting more than just how children play and the types of toys they engage with. It’s changing the nature in which we choose, research and ultimately purchase anything-especially toys.

Sure, there was a time where a strong recommendation from a friend or family member was enough to invoke a confident purchase decision. These days, there are as many sources of information as there are products to buy. And when it comes to how we buy for our children, paralysis by analysis is a common situation.

Technology is impacting more than just how children play and the types of toys they engage with

In the Retail Innovation Lab, we’re connecting, prototyping and testing new ways for parents, family members and gift buyers to discover and purchase toys that ultimately bring that same delightful experience to kids that we each once had!

The Changing Role of AI

Over the last 18 months, AI has come out of the cold and become a bit of an enigma in the minds of CIOs and CTOs. While most technology leaders are sold on AI becoming a mainstay in their digital roadmaps, the jury is still out on which AI-driven applications will have the most significant business impacts. Given this uncertainty, it’s important to take a play out of the world of tech startups and look for opportunities to experiment and gain early validation or leading indicators of success. Technology pilots are a great tool for this, but pilots often fail - not due to the technology but due to uncertainty around objectives. This takes us to our first rule:
1. Pilots and experiments leveraging AI need to have clear KPIs that can be measured over a short time period (1-3 months), and these indicators need to roll up to someone accountable for the metrics. For example, if using AI to power a chatbot for customer service, then it’s going to be important to measure call centre loads, wait times, NPS scores and other related metrics.

One of the dynamics that has evolved over the last couple of years is access to larger, more comprehensive data sets. These massive data sets (it wasn’t that long ago that we were all talking about the power of “big data”) are fundamental in training AI systems and algorithms.

Unfortunately, data in many enterprises is siloed across a range of systems, and this can make integration across data sets a challenge. So our second rule for embracing the changing role of AI.

2. Organizations need a comprehensive and shared data strategy. Without integrated, centralized, and synchronized data, the value of any kind of AI or machine learning will be greatly limited. For example, if an organization is looking to predict customer purchasing behavior and can access past purchase data but not web or social media content consumption, the ability to anticipate future purchases (and help shorten the sales cycle) is limited.

Finally, it’s important to be aware of varying levels of comfort with AI. There are those who fear a dystopian future in which AI takes over every aspect of our roles and renders millions unemployable.

One of the leading database software platforms in the market recently announced an AI-powered autonomous platform that promises to take on a lot of the work typically done by database administrators. Does this mean that DBAs will suddenly become unnecessary-definitely not. But it may mean that the role of the DBA evolves to solving more complex challenges within the IT strategy and execution.

3. Organizational and cultural adoption of AI is almost as important as technical implementation. While it’s important to frame the impact of any tool (including AI) in terms of the business drivers, the impact to the workforce should be considered and actively communicated.

Placing Some Bets

So where does one start in evaluating AI as a positive force for growth and efficiency? Ideally it begins with a review of your IT strategy and roadmap to ensure that areas of investment or investigation are aligned with organizational priorities.

In the B2B space, there may be an active investment around customer success, so perhaps applications that help provide a 360-degree view of the customer and support sales enablement are strong complements to that strategy.

On the B2C side, gaining efficiencies around customer service and reducing loads on call centers, for example, is a common challenge. If this is the case, it may make sense to invest in pilots related to leveraging conversational interfaces (chatbots, voice-based assistants, etc.) which help customers resolve problems with less (if any) human interaction.

Working in the toy industry, investments into AI and other cutting edge technologies are always aligned to the insights from real-world customers. As consumers change their toy-buying behavior, a customer-centric approach to designing solutions that adapt to those changes becomes critically important.

Regardless of your industry, if your AI initiatives aren’t aligned with broader organizational objectives, it won’t matter how amazing the technology is. So when placing your first bets, the best practice here is to work with peers across functional areas in the organization to understand where machine intelligence might help solve real business challenges.

And if you’re still stuck, you can always ask Alexa where she thinks you should begin!

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