Originally published on Enterprise AI
As 2022 begins, the progress and continuing evolution of AI is in full swing across the worlds of industry, manufacturing, retail, banking and finance, healthcare and medicine and an expanding range of other fields.
In our first roundup of 2022 enterprise AI predictions from IT leaders, which was published Jan. 7, we received a wide range of predictions from industry experts who shared their thoughts with EnterpriseAI.
Today, in this article, we are publishing a second installment of 2022 predictions, which again have been edited for clarity and brevity, to give our readers additional insights into what may come in 2022 in enterprise AI and related technologies.
Vijay Tella, the CEO and co-founder at integration platform vendor Workato, said he sees 2022 being the year when the role of enterprise automation architects become one of the most important hires inside companies.
“An enterprise automation architect is an emerging role that will surely make waves in 2022,” said Tella. “Enterprise architects think about tasks at a higher, more nuanced level than just checking them off a list and that is where smart automation begins to seep in.”
In the past automation has so far only replicated human tasks, said Tella. “As it transitions from taskmaster to completing business critical process automations that span across groups, businesses need people who think at a systems level instead of just the task level to manage it all,” he said. “Those who do not create space for such a role risk facing challenges that will impact their ability to implement automations for their intended purpose and at scale.”
At cybersecurity vendor Shift5, CEO and co-founder Josh Lospinoso said he predicts that machine learning will power the next wave of cybersecurity innovation. “The security practices of patching vulnerabilities, requiring passwords and listing known signals of bad actors do not adequately enable organizations to keep up with the rapidly-evolving threat landscape,” said Lospinoso.
“Machine learning can help move the industry forward – particularly because it provides the ability to identify previously unknown bad activity. In 2022, we will see a surge of innovation as vendors apply machine learning to a range of persistent cybersecurity problems, such as phishing attacks, unusual network traffic and business email compromises.”
Andy Vitus, a partner at the early-stage software investment group, Scale Venture Partners, said he sees automation dominating the next wave of AI. “Opportunities in AI and machine learning will continue to grow – the percentage of Scale’s enterprise software investments that involve AI has doubled from roughly 20 percent to more than 40 percent in the last five years and will likely continue to increase over the next decade,” he said.
“The winners in the next wave of AI will be intelligent business software that completely automates simple tasks – like data entry – and augments complex tasks alongside people. In both cases, AI will remove the tedium from software and change the paradigm from ‘we work for the software’ to “the software works for us.”
Abhishek Singh, the CEO of AiDash, a satellite and AI-powered vegetation management provider, said he sees satellite AI technology growing in 2022. “It will be responsible for covering more ground than humans because humans are not physically capable of seeing things that AI can pick up, nor are we physically able to inspect or process things at the same rate. AI does not need a break, but humans do. In 2022, companies will double down on satellite AI to improve work speeds.”
Singh said he also sees 2022 as the year when AI is no longer just a buzzword. “People will have higher expectations for AI to produce real, concrete results,” he said.
In addition, this will be the year when companies take a broader approach to using and integrating AI, said Singh. “With AI that works in the background to produce results you can use, organizations do not need to hire a team of data scientists to crunch numbers or forego the use of impactful data because they are creating their own formulas and systems in error-prone Excel. Companies large and small will incorporate AI into all their processes in 2022.”
Rob Gibbon, Product Manager at open source software vendor Canonical, the publisher of Ubuntu Linux, said that as AI has come of age – due in large part to collaborative open source initiatives like the TensorFlow, Keras, PyTorch and MXNet deep learning projects – 2022 will see ever-broader adoption of machine learning and AI in places such as automated runtime application performance optimization, adaptive workload scheduling, performance estimation and planning, automation, diagnostics and human interaction.
“While the AI/ML adoption trend accelerates, shadow IT environments and ungoverned cloud running costs will increasingly become an unacceptable, untenable marker of bad business,” said Gibbon. “Organizations have become savvy – and discerning buyers are increasingly looking to move cost sensitive, high run-rate applications back on-premises as effective private cloud options gain currency.”
George Young, the global managing director at digital transformation vendor Kalypso said he sees 2022 as the year when autonomous automation will make or break manufacturing.
“Autonomous systems understand business processes as they exist within the organization and make decisions based on its understanding of the real-time situations and environments,” said Young. “Organizations that do not tackle high-priority business problems with autonomous automation now will see their competitors leave them in their wake.”
To make this happen, workforce acceptance will be a critical component of autonomous automation’s success, added Young. “Manufacturing leaders must realize that autonomous automation is not about reallocating people. It is about reallocating people’s time. With more time to make strategic decisions, manufacturing leaders can look beyond the factory floor and focus their time and energy on optimizing other business priorities. The more that companies leverage autonomous automation, the more satisfied their workforce will be.”
Agiloft’s Colin Earl, the founder and CTO of the contract lifecycle management vendor, said he expects conversational AI to become a widespread practice in 2022.
“Modern conversational AI technology will allow us to start having conversations with our contracts, providing enterprises with actionable data, faster,” said Earl.
“Employees can verbally ask the AI questions and the AI bot will respond immediately with a recommendation, while simultaneously pulling up all relevant data in the enterprise’s database of documents. For example, someone can ask the bot which contracts have specific data about a recent sale, new hire or lawsuit and the bot will pull that data out based on that context. This can even go a step further where employees can ask the AI the most common clause language to use for creating a new contract.”
At cloud data orchestration software vendor Alluxio founder and CEO Haoyuan (H.Y.) Li said that he sees 2022 as a year where AI and deep learning platforms are already part of the mainstream.
“Just like we currently see a plethora of fully-integrated managed services based on Apache Spark and Presto, in 2022 we will see vertical integrations emerging based on the likes of PyTorch and TensorFlow,” he said. “MLOps for pipeline automation and management will become essential, further lowering the barriers and accelerating the adoption of AI and ML.”
Andy Hock, the vice president of product at AI accelerator vendor Cerebras Systems, said that this is the year when even more enterprises will leap into world-class AI computing to accelerate their research and business. “With this, the need for faster, more power efficient, and purpose-built AI compute will continue to grow rapidly along with applications, models, and datasets,” said Hock.
“Companies leveraging AI as a key strategy for their business growth will need faster time-to-solution from their AI computing infrastructure, more scalability and broader accessibility through diverse consumption models.”
Erez Barak, the vice president of product development for continuous intelligence platform vendor Sumo Logic, said that for AI and machine learning to continue to grow and thrive within enterprises, observability need to be bulked up to keep pace. “Once AI is fully in use, teams need to be able to answer the question ‘how do we know if this is working correctly?’” said Barak.
“To do this, it means extending their existing observability approaches to cover their AI and ML capabilities. Today, there is a big gap there and that creates risk as organizations cannot manage those assets properly. AI has been sitting on the sidelines of observability for a while now, but we are seeing more systems starting to monitor AI’s use in production systems over the last two years, and this will increase in line with more adoption of AI.”
The work happening inside [the] OpenTelemetry [open source project] provides insight into how this will develop, said Barak. “The open source community will work on how observability into AI models function over time.”
Scott Zoldi, the chief analytics officer at analytic decisioning platform FICO, said that after years of trying to live up to boundless hype, AI was humbled in the court of public opinion in 2021.
“Fortunately, data scientists are recognizing the malleability of AI’s decisioning powers and have created compensating controls, which include Auditable AI and Humble AI,” said Zoldi. In 2022, “Auditable AI and Humble AI will be more widely used as they move organizations toward the gold standard of developing AI systems that are trustworthy and safe for everyone. Ultimately, both will help build more trust in the decisions generated by AI systems—the ultimate catalyst to help this technology bounce back to achieve mainstream trust.”