10 AI trends to watch in 2020

Artificial intelligence: Everybody’s doing it.

Few are doing it well, however. In fact, nine out of ten companies have made some investment in AI, but 70 percent said they have seen minimal or no impact from AI thus far, according to the 2019 MIT SMR-BCG Artificial Intelligence Global Executive Study and Research Report.

Looking ahead to 2020, CIOs will need to better assess the value of their AI bets and prove that ROI to the business, says Kara Longo Korte, director of product management at TetraVX. That’s the headline for Forrester’s AI prognostications as well: “We believe 2020 will be the year when companies become laser-focused on AI value, leap out of experimentation mode, and ground themselves in reality to accelerate adoption,” Forrester analysts write.

Artificial intelligence (AI) trends for 2020

It looks to be an active year ahead on the AI front, with a number of relevant trends unfolding that IT leaders should follow:

1. IT leaders will get real about measuring AI impact

Here’s a sobering stat: Fewer than two out of five companies reported business gains from AI in the past three years, according to the MIT AI survey. That will need to change in the new year, given the significant investment organizations are continuing to make in AI capabilities.

One way to achieve this is to change the way we measure results. Think reporting against things like ease of use, improved processes, and customer satisfaction. “CIOs will also need to continue to put more of their budgets against understanding how AI can benefit their organizations and implement solutions that provide real ROI,” says Jean-François Gagné, CEO and co-founder of software provider Element AI, “or risk falling behind competitors.”

2. Operationalization will be the name of the game

AI has the potential to become the new operating system for the enterprise. “Over the last decade, organizations have been picking up AI know-how and started working with the technology, but successfully putting models into production has remained a challenge,” Gagné says. “This year will be a tipping point for the infrastructure needed to support effective deployments, providing integrated learning environments and data ecosystems that support adaptive decision making by AI.”

3. Data governance will get sexy

2020 will be about bringing AI into production, agrees Pat Ryan, executive vice president of enterprise architecture at SPR. But that will require IT to put in work with the chief data officer’s organization. The problem, says Forrester in its 2020 AI predictions report, is “sourcing data from a complex portfolio of applications and convincing various data gatekeepers to play along.”

Next year, the luster of AI and ML will wear off as companies realize it’s not magic, but math, says Ryan. “Organizations now also know the need for high-quality data as the foundation for AI/ML, so come 2020, we’ll see a heightened sense of appreciation and need for data governance, data analysts, data engineers, and machine learning engineers.”

The goal: creating a data pipeline capable of continuous curation to drive more successful AI projects. That’s why “firms with chief data officers (CDOs) are already about 1.5 times more likely to use AI, ML, and/or deep learning for their insights initiatives than those without CDOs,” Forrester says.

4. AI pros will shine

At the very top of LinkedIn’s top 15 emerging jobs in the U.S. for 2020 is the AI specialist. Hiring for artificial intelligence pros of various titles (including AI and ML engineers) has grown 74 percent annually over the last four years, according to LinkedIn. “Artificial intelligence and machine learning have both become synonymous with innovation, and our data shows that’s more than just buzz,” LinkedIn said, with especially hot markets in the San Francisco Bay Area, New York, Boston, Seattle, and Los Angeles.

5. Data modeling will move to the edge

Expect a shift from cloud-only to cloud-edge hybrid strategies to enable machine learning (ML) in the next year. “Being able to analyze high-fidelity, high-resolution, raw machine data in the cloud is often expensive and does not happen in real-time due to transport and ecosystem considerations,” says Senthil Kumar, FogHorn’s vice president of software engineering. To date, many organizations have settled for smaller sample sizes or time-deferred data for their efforts, which can provide an incomplete or inaccurate picture.

Forrester predicts that edge cloud service market (infrastructure-as-a-service and advanced cloud-native programming services on distributed edge computing infrastructure) will grow by at least 50 percent in 2020. “By implementing edge-first solutions, organizations can synthesize data locally, identify machine learning inferences on core raw data sets, and deliver enhanced predictive capabilities,” Kumar says. “By running ‘edgified’ versions of ML models in real-time, organizations enable faster responses to real-time events and the ability to act, react, pro-act to events of interest at the source.”

Let’s check out trends 6-10: