An overview of emerging trends, known hurdles, and best practices in artificial intelligence.
By Ben Lorica and Paco Nathan.
[This post originally appeared on the O’Reilly Radar.]
Recently, O’Reilly Media published AI Adoption in the Enterprise: How Companies Are Planning and Prioritizing AI Projects in Practice, a report based on an industry survey. That was the third of three industry surveys conducted in 2018 to probe trends in artificial intelligence (AI), big data, and cloud adoption. The other two surveys were The State of Machine Learning Adoption in the Enterprise, released in July 2018, and Evolving Data Infrastructure, released in January 2019.
This article looks at those results in further detail, comparing high-level themes based on the three reports, plus related presentations at the Strata Data Conference and the AI Conference. These points would have been out of scope for any of the individual reports.
Exploring new markets by repurposing AI applications
Looking across industry sectors in AI Adoption in the Enterprise, we see how technology, health care, and retail tend to be the leaders in AI adoption, whereas the public sector (government) tends to be the laggards, along with education and manufacturing. Although that gap could be taken as commentary about the need for “data for social good,” it also points toward opportunities. Consider this: finance has enjoyed first-mover advantages in artificial intelligence adoption, as have the technology and retail sectors. After having matured in these practices, now we see financial services firms exploring opportunities that just a few years ago might have been considered niches. For example, at our recent AI Conference in London, two talks—Ashok Srivastava of Intuit and Johnny Ball of Fluidy—presented business applications for AI aimed at establishing safety nets for small businesses. Both teams applied anomaly detection techniques (for example, reused from aircraft engine monitoring) to spot when small businesses were likely to fail. That’s important since more than 50% of small businesses fail, mostly due to exactly those “anomalies”: cash flow problems and late payments.
Given how government and education trail as laggards in the AI space, could similar kinds of technology reuse apply there? For example, within the past few years, it’s become common practice in U.S. grade schools for teachers to provide detailed information online to parents about student assignments and grades. This data can be extremely helpful as early warning signals for at-risk students who might be failing school—although, quite frankly, few working parents can afford the time to track that much data. Moreover, few schools have resources to act on that data in aggregate. Even so, the anomaly detection used in small business cash-flow analysis is strikingly similar to what a homework “safety net” for students would need. Undoubtedly, there are areas within government (especially at the local level) where similar AI applications could lead to considerable public upside, which would otherwise be understaffed due to budget restraints. As the enterprise adoption of AI continues to mature, we can hope that diffusion from the leaders to the laggards comes through similarly innovative acts of technology repurposing. The trick seems to be finding enough people with depth in both technical and business skills who can recognize business use cases for AI.
Looking at the “Tools for Building AI Applications” section of AI Adoption in the Enterprise for trends about technology adoption, we see how frameworks such as Spark NLP, scikit-learn, and H2O hold popularity in finance, whereas Google Cloud ML Engine gets higher share within the health care industry. Compared with analysis last year, both Keras and PyTorch have picked up significant gains over the category leader TensorFlow. Also, while there has been debate in the industry about the relative merits of using Jupyter Notebooks in production, usage has been growing dramatically. We see from this survey’s results that support for notebooks (23%) now leads over support for IDEs (17%).
The summary results about health care and life sciences create an interesting picture. 70 percent of all respondents from the health sector are using AI for R&D projects. Respondents from the health care sector also had significantly less trouble identifying appropriate uses cases for AI, although hurdles for the sector seem to come later in the AI production lifecycle. In general, health care leads other verticals in how it checks for a broad range of AI-related risks, and this vertical makes more use of data visualization than others, as would be expected. It’s also gaining in use of reinforcement learning, which was not expected. Although we know of reinforcement learning production use cases in finance, we don’t have optics into how reinforcement learning is used in health care. That could be a good topic for a subsequent survey.
Advice from the leaders
Admittedly, the survey for AI Adoption in the Enterprise drew from the initiated: 81% of respondents work for organizations that already use AI. We have much to learn from their collective experiences. For example, there’s a story unfolding in the contrast between mature practices and firms that are earlier in their journey toward AI adoption. Some of the key advice emerging from the mature organizations includes:
- Work toward overcoming challenges related to company culture or not being able to recognize the business use cases.
- Be mindful that the lack of data and lack of skilled people will pose ongoing challenges.
- While hiring data scientists, complement by also hiring people who can identify business use cases for AI solutions.
- Beyond just optimizing for business metrics, also check for model transparency and interpretability, fairness and bias, and that your AI systems are reliable and safe.
- Explore use cases beyond deep learning: other solutions have gained significant traction, including human-in-the-loop, knowledge graphs, and reinforcement learning.
- Look for value in applications of transfer learning, which is a nuanced technique the more advanced organizations recognize.
- Your organization probably needs to invest more in infrastructure engineering than it thinks, perpetually.
This is a story about the relative mix of priorities as a team gains experience. That experience is often gained by learning from early mistakes. In other words, there’s quite a long list of potential issues and concerns that an organization might consider at the outset of AI adoption in enterprise. However, “Go invest in everything, all at once” is not much of a strategy. Advice from leaders at the more sophisticated AI practices tends to be: “Here are the N things we tried early and have learned not to prioritize as much.” We hope that these surveys offer helpful guidance that other organizations can follow.
This is also a story about how to pace investments and sequence large initiatives effectively. For example, you must address the more foundational pain points early—such as problems with company culture, or the lack of enough personnel who can identify the business uses—or those will become blockers for other AI initiatives down the road. Meanwhile, some investments must be ongoing, such as hiring appropriate talent and working to improve data sets. As an executive, don’t assume that one-shot initiatives will work as a panacea. These are ongoing challenges and you must budget for them as such.
Speaking of budget, firms are clearly taking the matter of AI adoption seriously, allocating significant amounts of their IT budgets for AI-related projects. Even if your firm isn’t, you can pretty much bet that the competition will be. Which side of that bet will pay off?
Heading toward a threshold point
Another issue emerged from the surveys that concerns messaging about AutoML. Adoption percentages for AutoML had been in single-digit territory in our earlier survey just two quarters ago. Now, we see many organizations making serious budget allocations toward integrating AutoML over the course of the next year. This is especially poignant for the more mature practices: 86% will be integrating AutoML within the next year, nearly two times that of the evaluation stage firms. That shift is timed almost precisely as cloud providers extend their AutoML offerings. For example, this was an important theme emphasized at Amazon’s recent re:Invent conference in Las Vegas. Both sides, demand and supply, are rolling the dice on AutoML in a big way.
Even so, there’s a risk that less-informed executives might interpret the growing uptake of AutoML as a signal that “AI capabilities are readily available off-the-shelf.” That’s anything but the case at hand. The process of leveraging AI capabilities, even within the AutoML category, depends on multi-year transformations for organizations. That effort requires substantial capital investments and typically an extensive evolution of mindshare by the leadership. It’s not an impulse buy. Another important point to remember is that AutoML is only one portion of the automation that’s needed. See the recent Data Show Podcast interview “Building tools for enterprise data science” with Vitaly Gordon, VP of data science and engineering at Salesforce, about their TransmogrifAI open source project for machine learning workflows. It’s clear that automating the model building and model search step—the AutoML part—is just one piece of the puzzle.
We’ve also known—since studies published in 2017 plus the analysis that followed—that a “digital divide” is growing in enterprise between the leaders and the laggards in AI adoption. See the excellent “Notes from the frontier: Making AI work,” by Michael Chui at McKinsey Global Institute, plus the related report, AI adoption advances, but foundational barriers remain. What we observe now in Q4 2018 and Q1 2019 is how the mature practices are investing significantly, and based on lessons learned, they’re investing more wisely. However, most of the laggards aren’t even beginning to invest in crucial transformations that will require years. We cannot overstress how this demonstrates a growing divide between “haves” and “have nots” among enterprise organizations. At some threshold point relatively soon, the “have nots” might simply fall too many years behind their competitors to be worth the investments that will be needed to catch up.