Many analytics projects pass a pilot test with flying colors but fail to earn wide adoption. Here are five common culprits that doom projects – and advice for tackling them.
AI in 2019: 8 trends to watch
Forget the job-stealing robot predictions. Let's focus on artificial intelligence trends – around talent, security, data analytics, and more – that will matter to IT leaders
5. Security will increasingly depend on AI
According to Tomer Weingarten, CEO of SentinelOne, too many organizations still use yesterday’s tools and processes to combat the cybersecurity threats of today and tomorrow. That problem is compounded by a well-known security skills shortage.
[ How hot is security talent? Read IT security jobs by the numbers: 12 stats. ]
As Kashyap suggests above, AI and machine learning will become an increasing part of the solution to that problem.
“AI will continue to reinvent how organizations run cybersecurity programs. Successful cybersecurity is fundamentally about solving a big data problem at machine speed – the use of AI in this domain is wildly effective,” Weingarten says. “As cyber-attackers continue to evolve their techniques and as the gap between the supply of trained cybersecurity professionals and the demand for their skills continues to widen, AI and automation are paramount to protecting enterprises in 2019.”
While the detailed view may vary from person to person or organization to organization, it’s become a widely held perspective: The future of cybersecurity will be automated – again, not eliminating humans, but enabling them to keep up with an otherwise untenable magnitude of threats.
“With the number of cybersecurity threats growing each day and increased digitization of assets and processes that could be vulnerable to those threats, it is mathematically impossible for humans to monitor for threats and sift through hundreds of thousands of vulnerabilities to determine which to prioritize,” says Gaurav Banga, CEO and founder of Balbix. “Even the largest security team comprised of highly skilled IT professionals can’t effectively accomplish this without the assistance of artificial intelligence.”
6. Open source AI communities will thrive
Many of the cloud and cloud-native technologies in use today were born out of open source projects: Consider Kubernetes Exhibit A in that argument. Expect the development of AI, machine learning, and related technologies to follow a similar path.
“Today, more leading-edge software development occurs inside open source communities than ever before, and it is becoming increasingly difficult for proprietary projects to keep up with the rapid pace of development that open source offers,” says Ibrahim Haddad, director of research at The Linux Foundation, which includes the LF Deep Learning Foundation. “AI is no different and is becoming dominated by open source software that brings together multiple collaborators and organizations.”
In particular, Haddad expects more cutting-edge technology companies and early adopters to open source their internal AI work to catalyze the next waves of development, not unlike how Google spun Kubernetes out from an internal system to an open source project.
“We foresee more companies open sourcing their internal AI stacks in order to build communities around those projects,” Haddad says. “This will enable companies and communities to harmonize across a set of critical projects that together will create a comprehensive open source stack in the AI, machine learning, and deep learning space. Large companies that were the first to take their AI efforts open source are already seeing early mover advantages, and we expect this to increase.”
7. AI will empower you to actually analyze more data
Remember big data? It’s still big – and perpetually growing. That’s one reason why roles such as data scientists have been so hard for CIOs to fill. It’s also been one of the lingering problems in big data and analytics: The information is only as valuable as you make it.
“No company has been able to hire fast enough to keep pace with how fast their data is growing. Simply put: Data is doubling every year, but budgets and team sizes aren’t,” says Horton of Unsupervised.com. He expects companies to increasingly lean on AI solutions to help manage and analyze their data going forward.
“2019 will see more companies investing in AI engines that enable existing teams in a number of functions to easily scale with more data," he says.
Dean Teffer, principal scientist for machine learning at JASK, expects the mature learning field in particular to mature and, as a result, play a more pivotal role in how companies work with their data.
“The biggest trend I see in enterprise AI in 2019 is machine learning finally extending beyond the current “off the shelf” use cases of image labeling and text translation and beginning to help companies exploit the vast amount of data they’ve been collecting and storing for years,” Teffer says.
Teffer clarifies that he’s not referring to “point” uses like one-off analyses or even search enablement. Instead, he sees more organizations “engineering data pipelines that perform tedious data organization and modeling tasks that enable business users to really engage with these emerging AI systems in a more collaborative way.”
That will have a cascading effect that will increase the visibility of data management roles.
“A key part of this trend in 2019 will be elevating the role of enterprise data management in the organization, as it’s data – not algorithms – that are the repository of value,” Teffer says.
8. AI will become increasingly invisible
From a customer experience standpoint – think both internal and external customers or users – so many of the major technology shifts in recent decades have been readily visible and tangible. Mass adoption of mobile devices and apps? We’re all literally holding the impacts of that change in our hands. The multiple evolutionary phases of the consumer web? We may take it all for granted now, but it has produced countless changes to how we interact with businesses and each other – many of which are highly visible, even obvious, to us: You can grocery shop without leaving your home or car, for instance.
AI will have similarly significant outcomes, but most people will be less likely to point to something and say: “That’s AI.”
“The biggest AI happening in 2019 could just be invisible to most of us,” says Bui at the University of Hawaii.
That comes with a particular danger for businesses. Just because you don’t see how AI is changing your industries, markets, or business models doesn’t mean it’s not changing them.
“AI will already be everywhere – behind the scenes, embedded in applications – so users most likely won’t even know it’s there,” says Reihl of LexisNexis. “Any tech company expecting to be taken seriously should already be in AI, beyond the dabbling phase. If they’re not using AI, are they really developing a competitive advantage?”
[ Want lessons learned from CIOs applying AI? Get the new HBR Analytic Services report, An Executive's Guide to Real-World AI. ]