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Intelligent Products are more than just AI—they’re a complete ecosystem that allows AI to thrive

By Slalom Build

AI is proving to be an essential differentiator for early adopters, and organizations that continue to ignore AI may not survive in the marketplace long-term. A 2022 survey of data and tech executives found that “companies view wider AI adoption as mission-critical for their future.”[1]

Unfortunately, those high stakes can lead companies to undertake AI projects without building the other capabilities required to realize value from them. Fully 46% of AI projects fail to progress from prototype to production,[2] and 40% of organizations that invest significantly in AI do not report business gains from AI.[3]

All of which suggests that AI initiatives are more like giant pandas than cockroaches when it comes to developmental success—they need the right environment and supports to thrive.

Technology and intelligence, the first two pillars of Intelligent Products, are necessary for creating dynamic solutions that are always improving. But they’re not sufficient on their own for creating business benefits. They need to be accompanied by the second two pillars:

  • Experience, which ensures that intelligent products are intentionally designed to meet important user needs in a delightful way.
  • Operations, which drives organizational change in order to build intelligent products quickly and responsively and put them into production.

Why are AI-led projects more vulnerable to failure than intelligent products, which leverage experience and operations to promote success? Here’s a breakdown of some of their characteristics.

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AI projects can have a lower barrier to entry because of their narrow scope and siloed nature, but their usefulness to the company is also limited. Since they are not designed for wider production, there’s a constant need to rebuild or come up with new projects.

On the other hand, intelligent products, which incorporate the pillars of experience and operations, can deliver more value and impact across the organization. Let’s look at how that plays out in greater detail.

Experience keeps the focus on people, not data

It’s easy to get caught up in the data aspects of AI and machine learning (ML) because data provides the fuel. But an intelligent product needs to live in service of something—or rather, someone.

Business intelligence is just the leading edge. As much as data products serving insights through dashboards are useful, the future of modern technology companies lies in customer-facing intelligent products. Focusing on serving people helps create experiences that are:

  • Differentiated, to give customers a reason to choose you over your competitors.
  • Personalized, to create emotional significance and meaningful interactions.
  • Always improving, to increase satisfaction and loyalty.

Intelligent products with experience design fall onto a continuum, from simple features to full-fledged product lines.

For example, if you click to see the vehicle details on a typical used car website, you’ll get a long, unwieldly list of unsorted details that’s almost unreadable. That data is simply pulled and dumped as free text, with no processing. A model could make that list more useful for potential buyers by categorizing the details and presenting them alphabetically under meaningful headings such as Exterior, Interior, Mechanical, Safety, and Warranty. This simple website feature is a great example of an intelligent product giving users a better experience, making it easier for them to find the information they care about.

Now dream a little bigger. Imagine a model that takes data about customer behavior and applies ML to personalize a banking app based on that behavior. A teenager working their first job, a new homeowner, and a person nearing retirement would all have customized experiences that make them feel uniquely seen and taken care of. Using ML to understand and improve people’s experiences automatically is an exciting implementation that allows companies to serve niche audiences at scale.

Operations clears the path to value

Intelligent products require a different approach to organizational structure. The silos separating R&D, engineering, data, and revenue departments need to come down so that everyone can coordinate efforts in service of driving true value from the product. Without the dividing walls, cross-functional teams can collaborate more seamlessly, spur change more effectively, iterate quickly, shorten development and deployment timelines, and support the product across its lifecycle.

Product development operations become much more strategic because intelligent products are fueled by data that helps drive prioritization for what new features to add and which bugs to fix. The product lifecycle gets extended, as the product is continuously improving and receiving operational support.

Most importantly, operations ensures that the product is aligned to the broader business goals, even as models evolve through testing, validation, and new insight. With a clear path to value identified, AI projects are much better positioned to deliver a return on investment.

Experience and operations on the ground

The collaboration between Slalom and Kawasaki Heavy Industries is a prime example of how experience and operations contribute to a successful intelligent product.

Kawasaki is working to improve rail track monitoring and maintenance, thereby reducing slowdown orders and derailments. Locomotive sensors collect track data that is then aggregated into a single user-friendly view, which allows freight rail operators to dispatch teams to fix problems before they happen. The solution leverages advanced IoT, ML, and AI capabilities powered by Microsoft Azure.

Slalom Build is on board to help develop the platform for even greater speed and customer value. Our expertise in experience design and operations is helping Kawasaki continuously improve the offering for rail operators and create its own product engineering team to expand the platform globally.

Our involvement also supports Kawasaki in elevating this project from an AI exercise to a true intelligent product. Experience and operations are critical to product success—neglecting one or both will have consequences that affect performance and business outcomes. Intentional experience design ensures that the platform works for all users: train riders, maintenance crews, and service providers. Operations brings the vision to life and expands the way it can be implemented, clearing the path to value.

The vision for intelligent products isn’t something that comes across in a five-minute pitch—it’s more complex than a single tool and broader than a single capability. But we believe it’s worth taking the time. Organizations that make the effort to understand intelligent products and explore how they could fit into their roadmap will have an advantage in the market.

Interested in learning more about how intelligent products can change your company’s future? Then contact us to learn more or plan a workshop with us. You’ll get a better sense of how intelligent products can support your business goals so you can decide on your next intelligent move.

[1] MIT Technology Review, CIO Vision 2025: Bridging the Gap Between BI and AI, 2022,

[2] Sean Michael Kerner, “New Gartner survey: Only half of AI models make it into production,” August 2022,

[3] MIT Sloan Management Review, MIT SMR-BCG Artificial Intelligence Global Executive Study and Research Report, 2019,

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