Successful initiatives balance technology and business principles to build superior products that solve customer problems and react quickly as needs change.
After having spent over two decades developing and applying AI and cognitive technologies — from early-stage startups to Fortune 50 companies— I have learned that successful initiatives balance technology and business principles to build superior products that solve their customer problems and react quickly as the customer needs evolve.
AI/ML techniques have since been democratized by the ready availability of frameworks that enable anyone to extract insights from their datasets using state-of-the-art algorithms on infinitely scalable cloud infrastructure.
Over the years, I’ve also seen plenty of failed AI projects for all kinds of reasons: from insufficient or low-value data; to team dynamics and AI-readiness; to not having a clear direction for the product or company. While the AI strategy is key — so is the business strategy and the alignment with a clear vision.
Using my years of expertise, I focus on setting the right AI strategy.
During my PhD research, which was funded by Intel at the University of Illinois at Urbana-Champaign, I explored how system design principles could be applied to enable desktop computers to solve supercomputer class problems.
The outcome was a set of CPU design elements to optimize computing over large datasets.
I was exposed early on to the design principles espoused by Uhlrich and Eppinger in the book Product Design and Development.
I learned that the notion of a system includes the customer needs, design for manufacturing, prototyping and industrial design.
At HP, I was given an exciting high-visibility AI project to manage. I collaborated with The New York Times on pioneering their content personalization and targeting system. Other AI initiatives I led included an approach for a digital printing press, together with audience engagement systems to improve engagement of customers with digital media. These were system-design projects where insights extracted from large datasets were applied to solve the apt customer problems.
After a couple of decades at HP I was itching to leave and head into startupland.
I worked at emerging startups in CTO roles to build AI-based SaaS products. I learned the value of Agile methods and of Lean Design principles as expressed in the book The Lean Startup by Eric Ries.
A key attribute that AI startups have is a relentless focus on continual experimentation to extract viable signal from large datasets by applying the appropriate modeling techniques, which are improving at a rapid pace.
I became super-focused on extracting signal from data!
AI-infused projects differ from other software development projects in that extracting actionable signal from datasets is a key success factor.
AI projects are data-centric.
In AI projects, model variations can significantly affect the behavior of the product. Also, a change in one part of the system can propagate elsewhere. In AI projects modeling is hard, but scaling up is even harder.
After years of living first-hand of these differences, we have developed an agile approach to data-centric AI-first projects that includes the signal data experimentation phase of an AI/ML project.
The process looks like this:
The main steps for AI data signal experimentation are as follows:
I’ve built out this process in my experience both at large companies and at startups multiple times.
For example, I led a startup team in building an audience targeting system for app downloads. The initial design of the algorithms was iterative as shown above. Also, as we scaled up the product, we chose to grow the training sets by about three orders of magnitude to improve our predictions.
Several times in our product journey we had to take a step back and rerun the above process from the ground up. This led us to redesign significant aspects of our data pipeline to apply different frameworks and cloud mechanisms as were appropriate to the overall system. The above approach was critical to maintaining our sanity as the system dimensions changed with increasing scale.
System design principles continue to be critical to successful delivery of game-changing products to serve your customer needs.
The following questions of the system must be considered in an AI-first project where your company can lead:
Businesses have tried to enhance their products with AI and there are lots of reasons for why they failed. Your AI-led technology efforts need to combine with the business strategy.
Great companies combine the two. So should you.
Stay tuned for more insight.
Launching a new product into a crowded market that's better — but not different — isn’t a path to market success. But if a product is truly different, a well thought out category strategy can have enormous impact on the market. This article outlines the origins of category strategy.