Where to Expect Enterprise AI Growth in 2021: More Predictions from Our AI Experts 

So many industry predictions for how AI use in business will change and grow in 2021 have come in to EnterpriseAI that we didn’t have room to share them all in our first predictions story on Feb. 1.

Now we are expanding that bounty of AI predictions riches with a second installment of 2021 predictions from our AI experts across industries, verticals and the world of IT.

Included are a wide range of opinions and insights from IT executives, leaders, experts and AI users in the field of AI, on everything from the AI marketplace to coming innovations over the next year. Don’t forget to read our earlier predictions roundup as well to get the full flavor of where AI could be heading in 2021.

Clement Farabel

Clement Farabet, vice president of AI infrastructure for GPU and AI chipmaker Nvidia, said he expects AI in 2021 to take on the role of a compiler. “As AI training algorithms get faster, more robust and with richer tooling, AI will become equivalent to a compiler — developers will organize their datasets as code, and use AI to compile them into models,” said Farabet. “The end state of this is a large ecosystem of tooling/platforms, just like today’s tools for regular software, to enable more and more non-experts to ‘program’ AIs. We’re partially there, but I think the end state will look very different than where we are today — think compilation in seconds to minutes instead of days of training. And we’ll have very efficient tools to organize data, like we do for code via Git today.

Charlie Boyle

Another Nvidia executive, Charlie Boyle, a vice president and general manager of Nvidia DGX systems, said he sees the issue of shadow AI coming to the forefront inside companies that are experimenting with AI. “Managing AI across an organization will be a hot-button internal issue if data science teams implement their own AI platforms and infrastructure without IT involvement,” said Boyle. “Avoiding shadow AI requires a centralized enterprise IT approach to infrastructure, tools and workflow, which ultimately enables faster, more successful deployments of AI applications.”

At graph database vendor Neo4j, lead data science product manager Alicia Frame said she expects 2021 to shorten the timespan from when AI breakthroughs arrive in academia to when they show up in real-world use inside enterprises. This trend will translate into faster innovation cycles where new techniques discovered by researchers are rapidly scaled up and democratized for the masses, said Frame.

Alicia Frame

“With the computation power to crunch data getting cheaper and easier to access, and serverless technology making it easier to develop and deploy code, we’ll see data scientists getting back to focusing on the basics: solving big problems more effectively than anyone else,” she said. “In 2021, we’ll see a growing appreciation for the relationships between our data points – and the use of those relationships to make better insights – becoming more mainstream. Skills like graph algorithms, embeddings, and using graph convolutional neural networks will come into maturity.”

Dipti Borkar

Dipti Borkar, the co-founder and chief product officer for data analytics vendor Ahana, expects AI to lean more on the promise of open source platforms to propel AI further over the year.

“We’ll see more data-driven companies leverage open source for analytics and AI in 2021,” said Borkar. “Open source analytics technologies like Presto and Apache Spark power AI platforms and are much more flexible and cost effective than their traditional enterprise data warehouse counterparts that rely on consolidating data in one place–a time-consuming and costly endeavor that usually requires vendor lock-in. We will see a rise in usage of analytic engines like Presto for AI applications because of its open nature - open source license, open format, open interfaces, and open cloud.”

Joanna Lowry-Duda

Joanna Lowry-Duda, a machine learning research scientist for text analytics vendor Luminoso, said she believes that a trend of democratization of AI will be ever more prevalent in 2021. “There will be more toolkits, pre-trained models and datasets available for general consumption,” she said. “I hope that this will result in more understanding, specifically from business users, of what problems machine learning can solve (and how well) but also what its limitations are.”

Christine Boles

At chipmaker Intel Corp., Christine Boles, the vice president of the internet of things group and general manager of the industrial solutions division, said she predicts the continuing acceleration of industrial transformation through the use of AI in 2021.

"The pandemic has greatly accelerated the need for companies to complete their Industry 4.0 transformations with solutions that allow them to have more flexibility, visibility and efficiency in their operations,” said Boles. “We'll see an acceleration of adoption of solutions that help address that need, ranging from AI including machine learning, machine vision and advanced analytics. As the economy bounces back, we'll continue to see investment in the foundational operational technology infrastructure with more IT capabilities to allow the broad ecosystem of players to deploy these solutions and we’ll see Industry 4.0 adoption significantly ramp up in 2021."

Lomax Ward

Lomax Ward, a co-founder and partner with venture capital firm Luminous Ventures, said he expects AI research to gain deeper insights. “It is possible in 2021 we will see more breakthroughs with impact on the same orders of magnitude as GPT-3 or DeepMind’s AlphaFold – giant leaps of historical importance that shape industries in a profound way,” said Ward. “Increasing number of organizations will continue the adoption of AI and we will see a transition from doing pilot studies to creating real organizational changes to integrate AI into their processes. The AI integration does not happen overnight, and more work is needed on the organizational level to achieve ‘organizational learning’ on top of machine learning.”

Rick Rider

At cloud software vendor Infor, Rick Rider, the vice president of product management, said he sees AI in 2021 transforming the hiring process.

“In the unpredictable job market of 2021, it will be critical for organizations to leverage AI to ensure they find the right candidate for the job,” said Rider. “AI will enable HR departments to become more proactive in their hiring and help them determine a candidate’s cultural fit by using data to measure the quality of a hire. Innovations such as intelligent screening software that automates resume screening, recruiter chat bots that engage candidates in real-time, and digitized interviews that help assess a candidate’s fit, will start becoming commonplace in HR departments. AI also holds great promise for creating more diverse and inclusive workplaces, given its ability to reduce biases and add objectivity into employment decision-making through AI-powered algorithms that will identify the unique qualities of candidates.”

In addition, AI will be pivotal for real-time supply and demand matching, said Rider. “As the incredible supply chain disruptions of 2020 unfolded, it became clear that managing real-time supply and demand matching and forecasting were no longer tasks humans can take on alone. It’s no longer reasonable to expect a supply chain leader to predict when one country’s market will suddenly close and another’s will open, or account for ever-shifting materials and costs — especially as government restrictions on transportation and travel change rapidly. In 2021, we will see supply chain managers accelerating their adoption of AI to augment workers’ instincts and experiences and provide them with intelligent insights into changing market conditions, letting them accurately forecast supply and demand in real-time.”

Ramprakash Ramamoorthy

Ramprakash Ramamoorthy, director of AI research at cloud software vendor Zoho, said he sees AI continuing to help businesses morph and survive through the pandemic in 2021. “This pandemic year has been a ‘come-to-AI’ moment for many businesses,” said Ramamoorthy. “Now that companies are able to see the various ways AI can assist in business processes, the demand for these tools is high. In 2021, AI will be more commonly embedded in business tools, electronics, cars, and across all areas of business. Businesses will see AI embedded into workflows, analytics, communication tools, and others. We'll see AI augmenting tasks to run more efficiently and effectively.”

George Young

George Young, the global managing director of professional IT services firm Kalypso, said he sees artificial intelligence becoming less artificial in 2021. The use of AI will become standard for addressing the challenges of COVID-19, work from home, supply chain disruptions and more, said Young, and will help companies find new ways to continue operations effectively from products to plants and to end users.

“However, without considering how humans will interact with and leverage these new autonomous systems, AI will fail,” added Young. “In 2021, enterprises will take a human-centered approach to AI initiatives, understanding user needs and values, then adapting AI designs and models accordingly, which will in turn, improve adoption. Enterprises must put the same focus on people and culture as the technology itself for AI to be successful. Organizational change management teams will be critical for driving digital transformation and AI forward by bringing people along for the change journey and setting the organization up for measurable results. Proper change management is the most important – yet overlooked – aspect of any digital transformation initiative.”

Florian Douetteau

To Florian Douetteau, the CEO and co-founder of data science software platform vendor Dataiku, AI experimentation will become more strategic in 2021.

“Experimentation takes place throughout the entire model development process – usually every important decision or assumption comes with at least some experiment or previous research to justify those decisions,” said Douetteau. “Experimentation can take many shapes, from building full-fledged predictive ML models to doing statistical tests or charting data. Trying all combinations of every possible hyperparameter, feature handling, etc., quickly becomes untraceable. Therefore, we’ll begin to see organizations define a time and/or computation budget for experiments as well as an acceptability threshold for usefulness of the model.”

Mike Leach

A major factor that will influence AI use in 2021 are specific remote work challenges due to the COVID-19 pandemic, said Mike Leach, solution portfolio lead for client AI, professional VR and remote/rack workstation solutions at Lenovo. Typical datasets used by data scientists and other AI researchers are typically large files of 100GB or more, which can create internet connection/bandwidth bottlenecks when trying to upload or download them to and from any work-from-home environment, said Leach. In addition, many datasets cannot be removed from a company’s site, especially in certain industries like healthcare, he said.

As a result, Leach said he sees 2021 as the year of small data.

“Small data will allow businesses to become more agile, taking segments of data and gleaning instant insights that allow data and business analysts to deliver the real-time insights needed to make informed business decisions,” he said. “To achieve this, companies will need to invest in smarter technology solutions such as powerful mobile and desktop workstations to gain an agile and predictive view of their world.”

Ryohei Fujimaki

At data science automation vendor dotData, founder and CEO Ryohei Fujimaki said he sees more AI use in manufacturing in 2021. “We expect to see growing momentum in the adoption of AI and ML automation technologies in the manufacturing industry, as more organizations look to AI to streamline processes, lower operating costs and accelerate time to value,” said Fujimaki. “A big factor driving this momentum is the projected manufacturing workforce shortage due to skilled employees’ looming retirements. AI and automation are key technologies that can address this gap while increasing operational efficiency, improving quality and boosting productivity. The key areas where AI will make the most impact are monitoring and quality control, predictive maintenance, supply chain optimization and robotic process automation and bot programming.”

Sastry Malladi

Sastry Malladi, the CTO of cloud-native applications vendor FogHorn, said he sees AI bring used in 2021 to help solve logistics challenges for many businesses.

“Today, many warehouse and logistics operations are under pressure to significantly reduce order-to-delivery timelines, driven by increasing consumer demand and expectations” due to the pandemic, said Malladi. “In 2021, warehouses will pair the low-latency processing power of the edge with the mobility of handheld devices to enable real-time operational insights on mobile devices unrestricted from fixed locations or even cloud connectivity. This flexibility ensures warehouse workers are kept in the loop of all internal operations and changes at all times and without having to alter their current daily routines. In turn, mobile edge solutions can enable workers to more instantaneously share information and insights across the warehouse, ensuring that every worker is on the same page at all times.”

At the same time, AI capabilities at the edge will help organizations transform video data from IoT connected sensors into actionable insights in real-time, he said. “Edge AI will play an essential role in evaluating and delivering heightened data quality and effectiveness, as edge-enabled solutions will perform real-time analysis of voluminous data streams and identify only the most valuable insights for further processing. We will see increasing adoption of edge AI technology as early adopters reap the benefits of real-time streaming analytics.”