How real-time data and open source software are powering ‘AI factories’
By Bryan Kirschner, Vice President, Strategy at DataStax
In their 2020 book Competing in the age of AIHarvard Business School professors Marco Iansiti and Karim Lakhani make some bold predictions about the winning companies 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 groundbreaking work, data collected from IT managers and practitioners lends much credence to Iansiti and Lakhani’s hypotheses—especially those regarding the types of technology architectures and strategies that create 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 by collecting ever-growing amounts of real-time data and accessing it instantly, tuning their predictions.
User experiences become more personal and intuitive; key decisions can be made almost instantly; and predictions can happen in real time, giving a business the opportunity to improve performance in the moment.
This opens up new avenues for growth: in the words of the authors, as AI factories “accumulate data by increasing scale (or even scope), the algorithms improve and the business creates greater value, enabling more use and thus the generation of more data.”
For more traditional companies to achieve this kind of success, a number of changes are required in both operating models and technology profiles.
Open source software and AI success
The State of the Data Race 2022 report is based on a survey of over 500 IT leaders and practitioners who have delved into their organization’s data strategies.
For the purpose of this analysis, the responses were divided into three groups:
- those where both AI and ML are already in widespread deployment
- those where AI and ML are at most in the pilot phase or early days
- those between these two extremes, characterized as being in “limited distribution”
The study hypothesized that organizations with large amounts of AI/ML in production provide useful information about the evolving shape of the “AI factory” and looked for differences across the three maturity stages.
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 the data they collect—not the software they develop in-house.
The survey data backs this up in spades. Strong majorities of each of the three AI/ML groups consider open source software (OSS) to be 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 broad deployment describe OSS this way (22% of “early days” organizations do, and this jumps to 46 % of those with AI /ML in limited distribution).
Perhaps even more strikingly, organizations not users OSS are a small minority (1%, 1% and 7%, ordered from “wide distribution” to “early days”). But a majority of those with AI/ML in broad deployment (55%) join companies like The Home Depot in having a company-wide mandate for using OSS.
Real-time data and AI
Consider the AI leaders mentioned above. These companies have put together technological infrastructures that enable immediate 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’s not surprising that Iansiti and Lakhani report that AI factories are leaning into real-time. “The best enterprises … develop tailored customer experiences, reduce the risk of customer churn, predict equipment failure and enable all kinds of process decisions in real time,” they say.
Just as with OSS, findings from The State of the Data Race point to real-time data (and the technology architecture that enables it) as a core strategy issue for AI leaders. Its significant use correlates with AI maturity: 81% of companies that have broadly implemented AI/ML say real-time data is a core strategy. 48 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 whopping 61% say leveraging real-time data is a strategic focus across the organization (four times as much as organizations in the early days, and more than twice as much as 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 a business 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 this possible for everyone, freeing the future organization to make the most of its unique customer interactions and datasets.
Uniphore: A case study in real-time data, AI and OSS
Uniphore helps 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 on thousands of customer reactions during video calls.
“We have about fourteen different AI models that we run in real-time to aggregate the data into something meaningful for our customers,” says Saurabh Saxena, Uniphore’s chief technology officer and VP of engineering. “Any kind of latency will 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® provided as a service via DataStax (my employer) Astra DB. Performance and reliability are key to ensuring that Uniphore’s system is something every salesperson is motivated to rely on to be more efficient in the moment.
But winning adoption among line employees points to another of Iansiti and Lakhani’s insights about the implications of AI for senior management. As the latter explained in a 2021 interview, “AI is good at predictions” — and predictions are “the fashion of an organization.” Executives must constantly ask, “Do I have the data now to improve my predictive power—my accuracy, my speed?”
As Uniphore points out, accuracy in sales forecasts is something most sales managers are concerned with. As a knock-on effect of using Uniphor’s tools, quantitative data on sentiment and engagement can flow into sales forecasting without the need for more staff time. In addition to the direct increase experienced by salespeople, forecasts are improved –– 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 beyond the benefits of individual use cases and point solutions.
Building an AI factory
Organizations that leaned into the insight of Competing in the age of AI may have stolen a march on their competition. Based on our survey data, they have been amply rewarded for doing so. The good news is that they have proven best practices for success—and the tools you need to accelerate your own progress on your journey to becoming an “AI factory” are ready and waiting.
Find out how DataStax enables AI-powered apps
About Bryan Kirschner:
Bryan is Vice President of Strategy at DataStax. For more than 20 years, he has helped large organizations build and execute strategies as they seek new ways forward and a future that is significantly different from the past. He specializes in removing fear, uncertainty and doubt from strategic decisions through empirical data and market sensing.
