Beyond AI: how Intelligent Products drive real AI impact on business
AI is an essential differentiator for early adopters, and organizations that continue to ignore AI may not survive in the marketplace long-term. Those who incorporate AI into their products can provide customer experiences and use data in ways their competitors can’t. A 2022 survey of data and tech executives found that “companies view wider AI adoption as mission-critical for their future.”
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, and 40% of organizations that invest significantly in AI do not report business gains from AI.
All of this suggests that AI initiatives are more like giant pandas than cockroaches when it comes to developmental success—they need the right environment and support to thrive.
To understand why AI-led projects are vulnerable to failure, take a look at this breakdown of their characteristics in contrast to Intelligent Products.
AI projects do leverage technology and intelligence, the first two of the four pillars of intelligent products to create dynamic solutions that are always improving.
But even though AI projects can have a lower barrier to entry because of their narrow scope and siloed nature, 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.
- Experience ensures that intelligent products are intentionally designed to meet important user needs in an engaging way.
- Operations drives organizational change in order to build intelligent products quickly and responsively and put them into production.
Let’s look at how these two pillars play out in greater detail.
Experience keeps the focus on people, not data
It’s common to get caught up in the data aspects of AI and machine learning (ML) because data provides the fuel. But an intelligent product lives 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 intelligent products designed for people. Focusing on serving end users helps create experiences that are:
- Empowering, to help people do what they do best more efficiently.
- 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, unwieldy list of unsorted details that’s almost unreadable. That data is simply pulled and dumped as free text, with no processing.
An algorithmic 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 let’s 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 clear 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.
Adopting modern DevOps and MLOps is another key operational change that helps products make an impact sooner by tightening timeframes from months to weeks.
Most importantly, operations ensure 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, intelligent products are much better positioned to deliver a return on investment.
Experience and operations on the ground
The collaboration between Slalom and Kawasaki Heavy Industries, Ltd., 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 bring the vision to life and expand 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? This whitepaper will give you ideas on what and how you could start building. Or you can contact us to plan a workshop and explore in real time. You’ll get a better sense of how intelligent products can support your business goals so you can decide on your next intelligent move.
 MIT Technology Review, CIO Vision 2025: Bridging the Gap Between BI and AI, 2022, https://www.databricks.com/p/w....
 Sean Michael Kerner, “New Gartner survey: Only half of AI models make it into production,” August 2022, https://venturebeat.com/ai/new....
 MIT Sloan Management Review, MIT SMR-BCG Artificial Intelligence Global Executive Study and Research Report, 2019, https://sloanreview.mit.edu/pr...
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