How Enterprise AI Use Will Grow in 2021: Predictions from Our AI Experts
AI tackles new real-world IT uses every day across a broad range of businesses, manufacturing companies and industries, while constantly growing and evolving through fledgling innovations and possibilities around the globe.
Further fueling those creative fires are ongoing discussions and arguments about the promise of AI for mankind and business expansion, as well as contrasting and stark warnings about the potential hazards and outright threats of AI to privacy, human rights and societies.
Among IT executives, leaders, experts and supporters in the field of AI, the opinions and insights vary widely. In this AI 2021 predictions roundup for EnterpriseAI readers, we have gathered the thoughts and comments from a sampling of our experts who shared their thoughts with us on the AI marketplace and coming innovations over the next year. Their conversations are intriguing.
Chris MacFarland, the CEO of software-defined networking vendor, Masergy, said that in 2021, AI will get real.
AI-integrated networks that promise true autonomous networking with 100% set-it-and-forget-it environments that automatically ensure optimization and security are still a few years off, said MacFarland, “but in 2021, AI will enable companies to increase productivity and enhance application performance, creating better user experiences by two-fold. For example, AI will automatically adjust networks so employees will no longer confront long downtimes or need to call IT about systems not working.”
This breakthrough “will free IT teams from manual network recalibrations, increasing efficiency and productivity,” he said. “The cost-to-performance ratio of the network will improve as AI removes the daily heavy lifting that IT teams have been saddled with for years. This tangible value will drive more investment.”
Hillary Ashton, the chief product officer at data analytics vendor Teradata, said that AI will be helpful in 2021 for many companies as businesses look toward reopening and recouping sufficient revenue streams as the COVID-19 pandemic slowly releases its grip on the world.
“They’ll need to leverage smart technologies to gather key insights in real-time that allow them to do so,” said Ashton. “Adopting AI technologies can help guide companies to understand if their strategies to keep customers and employees safe are working, while continuing to foster growth. As companies recognize the unique abilities of AI to help ease corporate policy management and compliance, ensure safety and evolve customer experience, we'll see boosted rates of AI adoption across industries."
That will also involve using AI to boost safety and compliance measures inside offices, she said. “As companies look to eventually return in some form to the office, we'll see investments in AI rise across the board. AI-driven algorithms can scour meeting invites, email traffic, business travel and GPS data from employer-issued computers and cell phones to give businesses advance warnings of certain danger zones or to quickly halt a potential outbreak at a location. These technology-supported measures ensure that once employees return to the office, they are working in the safest possible environment."
Thomas Phelps, the CIO at enterprise content management vendor Laserfiche, said that 2021 will be notable as the year when AI will finally be integrated throughout every step of an organization’s day-to-day operations. “And it will become the way businesses create a competitive advantage, deliver new products and services, transform the back office and improve the customer experience,” said Phelps. “This will include using AI to help mitigate risk and optimize costs, such as predicting issues in supply chains and suggesting alternative suppliers.”
In addition, security technologies will see an increased use of AI this year, he said, which will be used more often to prevent activity and attacks from threat actors, including combating ransomware or the exfiltration of sensitive data. “AI facial recognition technologies in CCTV systems coupled with keycard systems, proximity devices and building diagrams will be used to quickly identify intruders in a building. Lastly, back office capabilities will extend to accounts payable to automatically capture complex invoice data, perform a three-way match, detect fraud, and predict the appropriate payment frequency to manage cash flow and optimize discounts.”
At data analytics company Incorta, CEO and co-founder Osama Elkady predicts that companies which extend AI into core operational areas in 2021 will gain competitive advantages. “The next wave of analytics for both B2B and B2C companies in 2021 will be in core operational areas, such as supply chains, accounting and HR. For example, the pandemic has particularly disrupted supply chains, making it difficult for retailers to transport products where they’re needed.”
AI can help there because it can intelligently analyze different sources of data, such as local infection rates from the CDC and internal inventory data, allowing businesses to predict where they will need more goods and prioritize delivery to those areas, said Elkady. “The companies that will gain a competitive edge in 2021 will be the ones that operationalize AI, embracing these areas that were once viewed as cost centers but allow organizations to innovate at scale.”
Dee Anna McPherson, the chief marketing officer for consumer conversation intelligence platform, Invoca, said that 2021 will be the year that brands use AI to solve customer problems in real time.
“Today, customers expect brands to understand who they are,” said McPherson. “Knowing who the customer is, if they’ve shopped with you before, and serving up the right information is becoming table stakes. Looking forward to 2021, I expect that we will see brands take this intelligence to the next level.”
AI will also drive other important services for customers, she said. “Brands can also use this data to solve customer problems in real time. AI, machine learning, and automation enable brands to answer questions before they’re asked or to serve up solutions the minute the customer calls. Imagine if a sales rep could see that a promotion code wasn’t applied to an order and have the refund ready to go before they answered a customer call; or if a service rep knew that a customer dropped off the website and then picked up the phone to call them back when there was a breakdown in the purchase processing. That’s future-proofing the customer experience.”
Kimberly Gilbert, manager of technical commercial engineering for industrial and enterprise-grade AI technology vendor Beyond Limits, said she sees cognitive AI technologies gaining rapid growth and improvements to become more agile, flexible and intelligent this year. “By unifying machine learning techniques with encoded human knowledge, cognitive AI advancements will allow users to add to and edit its knowledge base once deployed, and as they do so, the systems will become significantly more flexible and intelligent as they learn by interacting with more domain experts, problems, and data.”
Eventually, she said, “AI systems will be able to identify if decision-makers implemented or declined its recommended actions, if the action taken did what it was supposed to do, and if the system was able to learn from that remediation action.”
Gilbert said she also expects that traditional AI technologies will become more advanced to solve challenges posed by increasingly complex planning and implementation methods within the utilities industry. “One of these challenges, for instance, is the problem utilities face in managing the many tradeoffs that need to be considered simultaneously while running operations, such as turbine cycling, load balance, asset and equipment constraints,” she said. “When traditional AI and machine learning solutions are no longer enough, utilities operators will turn to new technologies like cognitive AI, which is able to provide operators with a global perspective of the plant and plan to make informed decisions when considering numerous tradeoffs, goals, and constraints at the same time.”
Wilson Pang, the CTO of machine learning data training vendor Appen, said he sees 2021 as the year when deeper internal business collaboration will be needed to meet the demands required by AI and data.
“As organizations continue to invest in AI and scale their deployments, collaboration between business decision makers and technologists must become more effective as they partner to determine which AI use cases within the business will deliver the best ROI,” said Pang. “Today, the greatest hurdles to deploying AI with confidence are gaining broad agreement that the projects are vital and feasible; ensuring the organization has the right data and can effectively manage the data pipeline in support of the projects; and building the right organizational structure to accomplish the project goals.”
To make these transitions, “businesses, especially those with maturing AI programs, will focus on educating non-technical employees about AI in order to foster collaboration and decentralize how AI projects are designed and developed,” said Pang. “This will enable companies to target projects with the most potential for delivering the desired business benefits, while giving due consideration to what data will be used for projects and how data models will be trained to ensure their success.”
Jim McGowan, senior vice president of cloud partnerships with machine learning model vendor ElectrifAi, said he believes 2021 will be the year when companies take major steps away from seeing AI as a science experiment and start envisioning it as a way to provide concrete results that improve financial and business performance.
“We are going to move from where companies are simply satisfied to be doing 'something' to demanding that their return on investment from AI begins to improve,” said McGowan. “This is a tectonic shift. We are going to see the c-suite asking its AI teams when AI will start driving their businesses, start helping their employees do their jobs and start providing concrete business results. We are going to see more alignment with core business objectives.0
At professional services network KPMG, Traci Gusher, the company’s national leader for artificial intelligence and enterprise innovation, said a big challenge that will linger in 2021, complicating the AI plans of many companies, is the ongoing AI skills gap among potential employees.
“It’s been difficult for organizations to hire the talent needed to deploy AI and reap all the benefits, with half of industry insiders reporting this challenge,” said Gusher. “What’s more, many organizations have accelerated digital transformation initiatives by a matter of months or years – but there is a discrepancy in available talent and training opportunities to support these initiatives. Due to increased demand, we predict that companies will offer more upskilling initiatives and incentives for employees to learn new skills, as well as work to build data and AI literacy across all levels of the organization.”
At the same time, the COVID-19 pandemic has also presented opportunities for organizations to prioritize these needs and help employees develop new AI skills in their rapid transition to remote work, she said. “2021 is about education – both operating in a new normal and catching up to the expedited digital initiatives.”
Marshall Choy, vice president of product for AI platform vendor SambaNova Systems, said he predicts that 2021 will solidify the convergence of training and inference in AI systems.
“Conducting training and interference on a single common platform unites both hardware and software, and creates a much more efficient, fluid system and streamlines operations,” said Choy. “In a common platform, one can blur the lines between training and inference and do specific editing of a model or training in real-time as opposed to going back and forth.”
He said he also sees the next generation of general purpose applications expanding beyond machine learning. “Computing needs to adapt to fundamentally shift forward,” he said. “While AI and ML are a hot area of interest in research, they do not fully encompass all workloads. There will continue to be a broad range of application types that are both deterministic and probabilistic. AI, ML and HPC all have different computations so a singular one-trick-pony type of infrastructure may not be able to serve all those needs for all the different types of applications out there. The evolution of hardware is not only adapting to ML, but adapting to a new world of computer requirements.”