10 top Artificial Intelligence (AI) trends in 2021
Blog: The Enterprise Project - Enterprise Technology
Pre-pandemic, artificial intelligence was already poised for huge growth in 2020. Back in September 2019, IDC predicted that spending on AI technologies would grow more than two and a half times to $97.9 billion by 2023. Since then, COVID-19 has only increased the potential value of AI to the enterprise. According to McKinsey’s State of AI survey published in November 2020, half of respondents say their organizations have adopted AI in at least one function.
“As the grip of the pandemic continues to affect the ability of the enterprise to operate, AI in many guises will become increasingly important as businesses seek to understand their COVID- affected data sets and continue to automate day-to-day tasks,” says Wayne Butterfield, director of ISG Automation, a unit of global technology research and advisory firm ISG.
Also, IT operations faced a lot of challenges and stress in 2020 given all of the shifts toward work-from-home capabilities, and that will most likely continue in 2021. AI plays here, too: “With businesses more digitally connected than ever before,” says Dan Simion, vice president of AI and analytics at Capgemini North America, “AI can ensure that they stay operational.”
[ Check out our primer on 10 key artificial intelligence terms for IT and business leaders: Cheat sheet: AI glossary. ]
AI trends 2021: What’s happening in the enterprise
However, the focus of AI adoption will not be simply to improve the efficiency or effectiveness of operations. “There has been a visible shift towards leveraging AI to improve stakeholder experience owing to the pandemic,” says Alisha Mittal, practice director with management consultancy and research firm Everest Group.
The AI trends expected in 2021 that IT leaders should monitor include the following:
1. AI talent will remain tight
Talent supply is expected to be a key issue accompanying the accelerated adoption of AI going into 2021. “Enterprises have started realizing the importance of democratizing AI to address this persistent AI talent gap,” Mittal says.
Just as CIOs have worked to make data accessible to non-technical users, they will need to make sure AI is usable by a wider set of users. “Successful implementation of AI democratization requires focus on key aspects of data, technology, and learning strategy, supported by a decentralized governance model,” says Mittal. “Enterprises must also focus on contextualization, change management, and governance.”
2. AI fuels self-directed IT
In 2021, we will see more AI solutions that can detect and remediate common IT problems on their own, predicts Simion of CapGemini. “These solutions will self-correct and self-heal any malfunctions or issues in a proactive way, reducing the downtime of a system or critical application,” Simion says. “This will allow teams to allocate their resources to the complex and higher-priority projects they should be focusing on.”
3. AI structures unstructured data
In the year ahead, enterprises will leverage machine vision and natural language processing (NLP) to facilitate the structuring of unstructured data such as images or emails, says ISG’s Butterfield. The goal? To create data that robotic process automation (RPA) technology can more readily use to automate transactional activity in the enterprise.
“We have seen a rise in RPA, which is the fastest-growing area of software adoption in the last 24 months. But RPA has its limitations – predominantly in that it can only process structured data,” Butterfield explains. “Using AI to complete the complex task of understanding unstructured data and then provide a defined output such as a customer’s intention will enable RPA to complete the action.”
[ New data, outdated storage approach? Read also: 5 ways cloud storage and data services enable the future of development in the AI age. ]
4. IT pushes AI at a larger scale
“In 2020, we continued to observe significant AI adoption within IT organizations,” says Simion. “In 2021, I expect organizations to start to see the benefits of executing their AI and ML models – not only getting them into production, but also pushing them to scale.” One of the advantages of AI is that it can achieve ROI in real time, Simion notes, so this could be the year many organizations see their AI efforts begin to pay off.
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5. More AI becomes explainable
As compared to black-box AI, look for models to become more transparent. “There will be a bigger focus on explainability,” says Dave Lucas, senior director of product at customer data hub Tealium. “Being able to clearly articulate to a layperson how each individual characteristic or data point contributes to the end prediction or result of the model.” As more and more data regulations surface, AI trust will be pivotal. (See our primer, What is explainable AI? )
6. AIOps gets big
The complexity of IT systems has been exponentially increasing for the past several years. Forrester recently noted that vendors have responded with platform solutions that combine several once-siloed monitoring disciplines – such as infrastructure, application, and networking. As mentioned in our recent primer on the topic, an AIOps solution enables “IT operations and other teams to improve key processes, tasks, and decision-making through improved analysis of the volumes and categories of data coming its way.”
Forrester advises IT leaders to look for AIOps providers who can empower cross-team collaboration through data correlation, provide end-to-end digital experience, and integrate seamlessly into the whole IT operations management toolchain.
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7. Augmented processes enter the picture
Data and AI are key to competitive advantage and will be part of a bigger strategy for process automation and innovation. “Within that strategy, data ecosystems are scalable, governed, lean, and provide timely data from heterogeneous sources, but at the same time need to provide playgrounds and adapt fast to foster innovation,” says Ana Maloberti, big data engineer with Globant. “Companies are going a step further in optimization with augmented processes, both within business and development.”
Augmented coding tools, which are Globant’s main focus, optimize software development processes using AI, aiming for benefits including improved collaboration and wider collective intelligence. “The main challenge in making the most of this technology is a cultural one,” Maloberti says. “Fostering a data-driven organizational mindset first and growing out of experimental stages of AI are needed to create a sustainable and robust delivery model.”
8. Voice- and language-driven intelligence takes off
The increase in remote working will drive greater adoption of NLP and automated speech recognition (ASR) capabilities, particularly in customer contact centers, predicts ISG’s Butterfield. “Historically, less than five percent of all customer contacts are routinely checked for quality and agent feedback,” Butterfield says. “With a lack of one-to-one coaching at the moment of support – a given in an office environment – enterprises will need to use AI to complete checks on agent quality, customer intent understanding, and to ensure continued compliance.”
9. AI and cloud become symbiotic
“Artificial intelligence is going to play a significant part in broader adoption of cloud solutions,” says Rico Burnett, director of client innovation at legal services provider Exigent. “The monitoring and management of cloud resources and the vast amounts of data that will be generated will be supercharged through the deployment of artificial intelligence.”
10. AI ethics and standards come into focus
“In 2020, international partnerships like global Partnership on AI have moved from ideas to reality,” says Natalie Cartwright, co-founder and COO of AI banking platform Finn AI. “In 2021, they will deliver expertise and alignment on how to ensure that we leverage AI against major global problems, ensure inclusion and diversity, and stimulate innovation and economic growth.” Algorithm fairness and transparency of data are just two of the issues in the spotlight as AI ethics becomes more important to organizations across industries and society as a whole.
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