By Bryan Kirschner, Vice President, Strategy at DataStax
In their 2020 book Competing in the Age of AI, Harvard Business School professors Marco Iansiti and Karim Lakhani make some bold predictions about the winning enterprises of the future.
These organizations, which they refer to as “AI factories,” build a “virtuous cycle between user engagement, data collection, algorithm design, prediction, and improvement,” unlocking new paths to growth as software moves to the core of the enterprise.
A little more than two years after the publication of their seminal work, data gathered from IT leaders and practitioners lend a lot of credence to Iansiti and Lakhani’s hypotheses — particularly those regarding the kind of technology architectures and strategies that engender success with AI.
The AI factory
Successful AI companies — think Apple, Netflix, Google, Uber, or FedEx — build innovative applications and, as they scale, start the flywheel of data, growth, and improvement spinning by gathering ever-growing amounts of real-time data, accessing it instantly, and tuning their predictions.
User experiences become more personal and intuitive; key decisions can be made nearly instantaneously; and predictions can occur in real-time, empowering a business to improve outcomes in the moment.
This unlocks new paths to growth: in the authors’ words, as AI factories “accumulate data by increasing scale (or even scope), the algorithms get better and the business creates greater value, something that enables more usage and thus the generation of even more data.”
For more traditional firms to achieve this kind of success requires a host of changes in both their operating models and technology profiles.
The State of the Data Race 2022 report is based on a survey of over 500 IT leaders and practitioners that delved into their organizations’ data strategies.
For the purpose of this analysis, responses were divided into three groups:
those where both AI and ML are already in widespread deploymentthose where AI and ML are at most in the pilot phase or early daysthose in between these two extremes, characterized as being in “limited deployment”
The study assumed the organizations with AI/ML widely in production provide useful information about the evolving shape of the “AI factory” and looked for differences across the three stages of maturity.
Iansiti and Lakhani wrote that AI factories will evolve “from a focus on proprietary technologies and software to an emphasis on shared development and open source” because the competitive advantage they enjoy comes from data they accumulate — not the software they develop in-house.
The survey data backs this up in spades. A strong majority of each of the three AI/ML groups considers open-source software (OSS) at least “somewhat” important to their organization (73%, 96%, and 97%, respectively, ordered from “early days” to “wide deployment”).
But ratings of “very” important closely track AI/ML maturity: 84% of companies with AI/ML in wide deployment describe OSS this way (22%of “early days” organizations do, and this jumps to 46% of those with AI/ML in limited deployment).
Perhaps even more striking, organizations not using OSS are a tiny minority (1%, 1%, and 7%, ordered from “wide deployment” to “early days”). But a majority of those with AI/ML in wide deployment (55%) join companies like The Home Depot in having a company-wide mandate for use of OSS.
Real-time data and AI
Consider the AI leaders mentioned above. These companies have assembled technology infrastructures that enable instantaneous changes and decisions based on real-time feedback. Relying on day-old data and batch processing to update the routing of a package to ensure on-time delivery just doesn’t cut it at FedEx.
So, it isn’t surprising that Iansiti and Lakhani report that AI factories lean into real time. “The top enterprises … develop tailored customer experiences, mitigate the risk of customer churn, anticipate equipment failure, and enable all kinds of process decisions in real time,” they say.
Much like with OSS, findings from The State of the Data Race point to real-time data (and the technology architecture that enables it) as a matter of core strategy for the AI leaders. The substantial use of this correlates with AI maturity: 81% of companies that have broadly deployed AI/ML say real-time data is a core strategy. Forty-eight percent of organizations with limited AI/ML deployment describe it as a core strategy; the figure was 32% for companies in the early stages of AI/ML.
But among the advanced group, a full 61% say that leveraging real-time data is a strategic focus across their organization (four times that of organizations in the early days, and more than twice that of those with limited deployment). And 96%of today’s AI/ML leaders expect all or most of their apps to be real time within three years.
This makes sense: as an enterprise intentionally rewires its operations to make the most of AI/ML, it becomes especially important to eliminate any arbitrary architectural barriers to new use cases that require “speed at scale” anywhere in the business.
Today’s OSS as-a-service ecosystem makes that possible for everyone, freeing the future organization to make the most of its unique customer interactions and datasets.
Uniphore helps its enterprise customers cultivate more fruitful relationships with their customers by applying AI to sales and customer service communications. The company relies on real-time data to quickly analyze and provide feedback to salespeople upon thousands of customer reactions during video calls.
“We have about fourteen different AI models we run in real time to coalesce the data into something meaningful for our clients,” says Saurabh Saxena, Uniphore’s head of technology and VP of engineering. “Any kind of latency is going to have a negative effect on the real time side.”
“Without the ability to process data in real-time, our solution really wouldn’t be possible,” he adds.
To get “the speed they need,” Uniphore relies on open-source Apache Cassandra® delivered as a service via DataStax (my employer) Astra DB. Its performance and reliability are key to ensuring Uniphore’s system is something every salesperson is motivated to rely on in order to be more effective in the moment.
But winning adoption among line staff points to another of Iansiti and Lakhani’s insights on the implications of AI for senior management. As the latter explained in a 2021 interview, “AI is good at predictions” — and predictions are “the guts of an organization.” Senior executives need to constantly ask, “Do I have data now to improve my prediction power — my accuracy, my speed?”
As Uniphore points out, sales forecast accuracy is something most sales leaders are concerned about. As a knock-on effect of using Uniphore’s tools, quantitative data on sentiment and engagement can flow into sales forecasts without the need for more staff time. In addition to the direct uplift that sellers experience, forecasts improve– — management to spend their time on more important things, like investing for growth, with greater confidence.
This closes the loop on Iansiti and Lakhani’s insight that AI factories can unlock a more powerful operating model over and above the benefits of individual use cases and point solutions.
Building an AI factory
Organizations that leaned into the insights in Competing in the Age of AI may have stolen a march on their competition. Judging from our survey data, they’ve been amply rewarded for doing so. The good news is that they’ve proven best practices for success — and the tools you need to accelerate your own progress on the journey to becoming an “AI factory” are ready and waiting.
About Bryan Kirschner:
Bryan is Vice President, Strategy at DataStax. For more than 20 years he has helped large organizations build and execute strategy when they are seeking new ways forward and a future materially different from their past. He specializes in removing fear, uncertainty, and doubt from strategic decision-making through empirical data and market sensing.
Artificial Intelligence, IT Leadership