January 3, 2020

5 data trends for 2020

Media Coverage
CIO Dive
by: Roberto Torres
Jan 3, 2020
Photo Credit: Lotame

Companies will increase reliance of artificial intelligence and advanced data analytics in 2020 as they seek a competitive edge.

The company of the future will make most of its major decisions with a little help from data analytics.

From a staffing firm looking for the right software vendor to a pharma company in search of the next miracle drug or a retailer working to streamline its supply chain, industries turn to machine learning and artificial intelligence to stay ahead of competition.

But the impact of these technologies will spread beyond the C-suite and strategic decision making. Daily, workers can expect to see their capabilities augmented by tools such as chatbots, robotic process automation (RPA) and virtual assistants.

In the artificial intelligence field, the leading cloud computing vendors — Amazon, Microsoft and Google — see themselves as key players in the AI ecosystem. Since 2010, these companies have led the way in the consolidation of the AI market, acquiring companies in record numbers each year.

The next frontier of AI awaits, as IT leaders wade through a complex vendor market to find where true value lies, and how they can make their investments deliver the biggest returns.

Here are trends in data analytics CIO Dive will track closely in 2020:

1. Laws, consumer rights will push companies toward transparent AI

Privacy legislation at the local and international levels gives new context to the responsibility companies have over data.

The first symbols of a renewed sense of obligation are the record-setting fines levied against Google, Marriott and British Airways in Europe. But pieces of legislation like the General Data Protection Regulation will also impact the level of control users have over their data's interaction with artificial intelligence.

Provisions in the GDPR require companies to offer consumers the right to ask how AI tools make decisions about them. Consumers can ask for human intervention in the process.

The concept of explainable AI — platforms transparent enough so that a human expert can identify how conclusions are made — will gain relevance in the coming year.

Explainable AI marks a next stage of human augmentation, as transparent AI empowers humans to take corrective actions based on to the explanations given, according to a report from Accenture.

"Within three years, we believe [explainable AI] will have come to dominate the AI landscape for businesses — because it will enable people to understand and act responsibly, as well as creating effective teaming between human and machines," the firm said in its report.

2. As data becomes more business-critical, its presence expands in the C-suite

In the current digital landscape, CIOs must strike a balance between keeping existing systems running smoothly while finding ways to innovate.

On their own, most CIOs can't execute the kind of overarching changes an AI roadmap supposes, said Steve Hill, global head of innovation at KPMG, in an interview with CIO Dive.

"Having someone who wakes up every day understanding that roadmap, crafting and maintaining it as a living document and working with the business and the technology to make the change happen is so important," said Hill. "If you try to put this responsibility [on] the shoulders of somebody who's oftentimes sweating to keep up with the status quo, that's a mistake."

As the complexity of AI grows, industry can expect additional C-suite roles which focus on transformation to come into the fold.

3. Beyond chatbots and RPA, automation empowers humans at work

Augmented workers are projected by Gartner to enter the workforce in considerable numbers over the coming years.

But before reality catches up with that prediction, AI will gradually enter more mature stages, and take over some backend processes at the enterprise level.

In the coming year "we'll be moving AI from what's been the initial adoption" of chatbots and virtual agents, and moving closer to the automation of processes, embedding machine learning capabilities right into solutions, said Nancy Hornberger, EVP of Healthcare at ElectrifAi, in an interview with CIO Dive.

To get there, companies will need to devote resources and time to power those solutions.

Prep work on data is "the hardest part of moving forward with any kind of analytics and especially machine learning," Hornberger said. "You have to have that data prepared, tagged, have that sample set to train the model. ... It's the hardest part."

4. Consolidation in the AI market rolls on, with cloud vendors at the forefront

Leading names in tech leverage their coffers to bring further consolidation to the AI market. Since 2010, a list of acquirers, topped by Apple, Google and Microsoft, bought 635 AI startups.

Expect consolidation to continue as appetite for third-party providers grows. Companies will eventually spend half their resources on third-party services, said Hill. Currently, two-thirds of investments are internal.

"We're just now seeing the AI as a service market emerge," said Hill. "That's going to start playing a much larger role in how incumbents acquire the capabilities they need to leverage AI."

5. Count on more AI disillusionment

How high is the expectation bar for AI? The limit does not exist.

Business leaders expect AI to boost workplace satisfaction. They want direct impact on business efficiency by 2022. After an AI-related acquisition, they expect to see revenues rise.

And yet, a quarter of companies say half their AI projects have missed the mark. In the year ahead, managing expectations around AI is key to avoiding a mismatch between projections and reality.

"Despite a seemingly unending barrage of headlines in 2019, 2020 won't be the year AI changes the world," said Sudheesh Nair, CEO of ThoughtSpot, in an email ot CIO Dive. "In fact, 2020 is shaping up to be a tough year for AI with people getting disillusioned with the distance between marketing and reality."

One thing we can expect is companies to invest in the data frameworks needed to make AI work.

"Companies will continue to invest in infrastructure that can handle AI capabilities and a rash of APIs to connect data together to train and feed algorithms," Nair said.

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