As we enter 2022, we are living in a world that increasingly revolves around data, a movement that is being enabled and supported by technologies such as AI, IoT, and Quantum. The pandemic created a unique environment in which adoption of these technologies was viewed as essential by enterprises across a wide range of industries. Whether it's automotive, healthcare, or manufacturing we have seen more projects based off these transformative technologies move forward than seen in the past.
Individually AI and IoT can be valuable, but where we are seeing the greatest influence is when these technologies come together. Their confluence will turn what were simple solutions into complex, truly impactful offerings that will alter society and businesses for years to come.
And the continued advancements of each of these technologies will enhance the other. Omdia, our technology research brand, estimates that by 2030 there will be 75 billion IoT devices around the globe, which will provide an unimaginable amount of data to strengthen AI systems. At the same time, artificial intelligence allows this vast amount of data to be analyzed and acted upon with unprecedented speed.
As we move forward, organizations will be looking at how they can gain competitive advantages through leveraging quantum computing to move beyond the limitations that traditional architecture presents; and increasing investment in AI-driven automation. Whilst also ensuring they converge Subject Matter Experts with Data Scientists and Engineers throughout various levels of the process.
Whilst there is significant momentum in the space, Omdia predicts there will be key concerns that still need to be addressed across the next twelve months. These include interoperability with IT existing systems, industry-wide standardization of AI measurement and success; implementing standardized regulation and governance; and a greater drive to improve diversity within the sector.
Jenalea Howell | Vice President, AI & IoT Markets at Informa Tech
Our team of industry experts and analysts from Omdia, AI Business, and IoT World Today come together in this eBook, to give their predictions on the tech trends to shape the applied intelligence landscape in 2022.
#1 Connecting the next wave of IoT Devices
#2 Changing face of the IoT ecosystem
#3 Enterprises turn to IoT for sustainability and profits
#4 AI at the IoT edge
#5 Operationalizing AI
#6 AI vanishes to go mainstream
#7 AI growing up and learning accountability
#8 Merging tech
#9 Quantum computing moving beyond the hype
Navigate to any of the Trends using the links on this page or using the arrows on the right side of the page. If you wish to download the report, you can do so using the Contents menu in the top left corner.
While no single connectivity solution can meet the diverse needs and requirements of IoT solutions, the attributes of 5G should allow it to address most IoT use cases. Innovative aspects of the technology, most notably ultra-low latency, will allow it to address completely new classes of IoT use cases.”
At the core of any IoT solution is connectivity. Given the diversity of IoT applications in the market, it is not surprising a wide range of technologies, including various cellular protocols and numerous wired and short-range wireless solutions, are used in the market. As the size and sophistication of IoT deployments continue to grow, the demand for other innovative, alternative connectivity technologies will also increase.
In 2022 this will be exemplified by the growing influence of 5G in IoT. This initial growth will be fueled by applications such as 5G security cameras and 5G Automated Guided Vehicles (AGVs) for factories. However, this represents just the dawn of 5G in IoT. With its diverse attributes, 5G will eventually be able to address an assorted set of IoT use cases. This includes applications with high throughput demands, such as video surveillance, those requiring ultra-low latency, such as factory automation, and those with massive volumes, such as utilities and asset management.
Another IoT connectivity solution to monitor in 2022 is satellite. The introduction of smaller, low orbit satellites has made the economics of launching a satellite network more reasonable. Investors in turn have poured money into numerous start-ups focused in this area. While it remains to be seen if these dynamics translate into an explosion in satellite IoT connections, the technology’s unique ability to offer truly ubiquitous coverage opens the door for new cases that could fuel another of IoT growth.
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Given their unmatched resources, the role of cloud hyperscalers in IoT is only set to grow. They are becoming increasingly dominant in consideration and preference from B2B End Users and present a new “co-opetition” challenge for CSPs in the 5G Era”
The IoT ecosystem continues to evolve. While CSPs, system integrators, and traditional IoT platform providers are still critical pieces in the value chain, an undeniable development is the rising influence of cloud hyperscalers. Expect this to continue in 2022 as companies like Google and Amazon continue to invest resources to increase their presence and capabilities both horizontally and vertically in IoT. These efforts will in turn allow them to get more data into their clouds and to offer their IoT customers additional services, such as storage or database functionality.
It takes multiple players to deliver an IoT solution, but the Omdia IoT Enterprise Survey shows that enterprises overwhelmingly view the hyperscale cloud providers as their key IoT solution vendor, ahead of MNOs, SIs, or “pure play” IoT platform vendors, for example.
One area the hyperscalers have shown increased involvement is connectivity. Examples of this include acquisitions of satellite communications networks, the introduction of services to support 5G MPN and MEC solutions (e.g. AWS Outposts), and release of proprietary technologies such as Amazon Sidewalk. Omdia expects cloud hyperscalers will continue to explore alternative connectivity options and push into areas traditionally controlled by CSPs.
"IoT provides enterprises with the tools not only to commit to environmentally sustainable practices but to turn these efforts into profits and savings.”
The science is clear, climate change is real; extreme weather and environmental degradation pose a clear threat to our way of life. Fortunately, governments, enterprises, and consumers can take steps to help mitigate and even reverse the threat of climate change. Most major enterprises have set goals or timelines for achieving carbon neutrality and some for when their practices will be carbon negative. While changing long-standing practices can be disruptive and uncomfortable, it also presents an opportunity to adopt new and better practices. The Internet of Things (IoT) is increasingly seen by enterprises as an important tool for firstly measuring their impact on the environment and then taking the necessary measures to establish and implement better practices. A 2021 Omdia Enterprise IoT survey demonstrated 92% of enterprises implementing IoT solutions are expecting to see improved sustainability. This increased sustainability will help drive cost savings as well as brand equity.
Connected sensors can help measure carbon emissions, energy consumption, water, and resource runoff, and wastage. This helps enterprises understand their environmental impact in real-time.
Key areas to watch: buildings being refurbished with smarter lighting and temperature controls, smarter agriculture practices that reduce water consumption, better fleet/vehicle monitoring, and a movement to electrification with a focus on renewable energy sources.
Training requires more flexibility and power than does inference. Decoupling training and inference is a way of finessing the trade-off between flexibility and optimization by leveraging expensive, server-class CPUs for training, while reserving highly-efficient NPUs for inferencing.”
Machine learning (ML)-based analytics is expanding from cloud/data center- and mobile-based applications to the IoT edge. ML entails data-driven model development that provides a greater ability to classify and predict compared to traditional rules-based models. Fundamentally, this enables more granular, personalized, and context-specific analysis based on more complex sensor data scenarios, such as using more of a sensor’s data stream than would otherwise be practical; analyzing based on sensor fusion from multiple sensor types; and enabling analysis of more complex types of data, such as audio, images, and video.
Edge IoT inferencing for statistical ML and some simpler forms of neural network-based ML use cases can be accomplished effectively in software on current MCUs; however, the trend is to leverage some type of hardware acceleration.
There is a diversity of hardware technologies and architectures in use to accelerate ML workloads at the IoT edge.
While this diversity of approaches will continue over the next several years, a key approach will be to integrate an ML co-processor into an IoT edge device system-on-chip (SoC).
ROI proof points, best practices in AI strategy, organization, technology, and data governance from early adopters will help propel more fast followers to operationalize AI in 2022.”
Omdia’s AI Readiness Barometer – 3Q21 provided a snapshot of how AI-ready enterprises are across the globe. More than 500 companies across several industries participated.
The majority of respondents are in the second of four readiness phases. 52% of respondents rated as AI competent, indicating they are building formative strategies and organizations to operationalize AI, but are likely less ready in the technology-operations and data categories. This segment largely represents AI fast followers (as opposed to early adopters). Many will move into the AI proficient stage in 2022.
The key to shifting more organizations to advanced phases is improving readiness in AI technology-operations and data practices.
68% of respondents rated in the advanced phases for strategy
55% of respondents rated in the advanced phases for organization
73% of respondents rated in the formative phases for technology-operations
84% of respondents rated in the formative phases for data practices
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Throughout 2022 Omdia anticipates a flood of similar embedded (“invisible”) AI outcomes to enter the market through existing user-facing software products.”
Artificial intelligence (AI) has certainly reached critical awareness within mainstream culture. However, the complexities of putting AI into practice in the enterprise have limited its reach. This has created a sizable rift between companies with and without the requisite data science and data management expertise. That rift, however, may have been a mirage.
Thanks to the use of AI to automate the creation of AI (i.e. AutoML) and advancements in AI itself (e.g. large-scale models GPT-3 and Microsoft Turing-NLG), enterprises are starting to see -- or not see, as it were -- AI directly embedded line of business processes. Salesforce, SAP, Oracle, and other application providers now enable companies to literally flip a switch to enable contextual AI outcomes without the need for data and AI experts. With Salesforce Einstein Discovery, Salesforce flow users can, for instance, turn on the ability to predict the product a customer is most likely to buy next.
Throughout 2022 Omdia anticipates a flood of similar embedded (“invisible”) AI outcomes to enter the market through existing user-facing software products. And yet, for many enterprise buyers, these solutions will do little but call attention to any shortcomings in the underlying data -- issues impacting quality, validity, bias, inclusiveness, and a host of other risk factors that demand an internal investment in data and data science expertise. Over time, Omdia expects this increased scrutiny coupled with growing AI regulatory pressure will ultimately improve these solutions, driving further automation and elevating the need for data proficiency among potential buyers.
We need to hold our AI and our organizations accountable by making the right choices every day. What we choose matters, no matter how small these choices appear. They create the future for us, our children, and generations to come. We need to act responsibly.”
2022 will see more enterprises embracing ethical AI and actively governing their AI implementations. This will be supported by expanded offerings in tooling, products and services: from bias detection and explainability, impact and risk analysis, and algorithmic registers; to audits, certifications; and end-to-end solutions for ML assurance, continuous monitoring, reporting, remediation and proactive disclosure. Because trust is fundamental for unlocking and scaling AI’s potential. “Without trust there is no use for AI”, says Mikko Rusama, Chief Digital Officer of the City of Helsinki.
Trust is the glue that holds society together and enables business, commerce, and innovation. Trust -- and its twin, transparency -- are also a competitive differentiator, inspiring loyalty, referrals, consumer confidence and return business. Internally, trust and transparency ensure employee support and innovation. (And it is really employees, not executives, who decide the faith of technology adoption.) So, ethics and governance are not nice-to-haves, they are no longer optional; and trust, accountability, and profit are not mutually exclusive.
The Internet of Things (IoT) involves the aggregate connections of billions of devices, resulting in connected buildings, smart cities, smart factories, connected retailers and connected vehicles.”
In addition to the trend of emerging tech is another market dynamic called merging tech. This is where the benefits of distinct technologies combine to dramatically increase the value of both.
The Internet of Things (IoT) involves the aggregate connections of billions of devices, resulting in connected buildings, smart cities, smart factories, connected retailers and connected vehicles. Sensors in such areas produce an unimaginable amount of data, essentially too much for humans to analyze, comprehend and leverage.
A merger with Artificial Intelligence, which can be used to analyze and leverage mass amounts of data with speed, then compounds and increases the value of the merged technologies. Beyond that is quantum computing, which will take computational capabilities to yet another level.
While the Quantum Computing market is maturing, pilots and deployments will not magically appear overnight. In fact, a large portion of organizations are in the education phase and are building their knowledge about QC technology, its benefits, and deployment challenges.”
The quantum computing market is slowly entering a phase of commercial development and notable technological achievements. While the road to achieving fault-tolerant quantum machines is challenging and will take time to traverse, smaller technological achievements are occurring frequently, which, taken in aggregate, point toward a continuous movement toward greater technological capability with QCs. Among the notable trends seen in the market that Omdia expects to continue in 2022 are:
Greater amounts of financial investment into the market from both government funding schemes, private funds, and most recently, the public equity markets.
Increased focus on near-term QC use cases, with specific pilot programs using quantum computers, simulators, and hybrid approaches, and the launch of QKD products.
Expanding partnerships between research centers/centers of excellence and QC hardware and software vendors.
Quantum use cases will continue to expand, as both academic researchers and industry professionals identify ways in which QC can address specific challenges that cannot be overcome via traditional computing methods. Based on vendor and end-user accounts of pilot programs and resource allocations Omdia has already identified 60 use cases, and it’s clear that organizations are allocating resources to a wide range of potential use cases.
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Enter Quantum
This year we’re launching a brand new site, dedicated to Quantum Computing. Covering the latest news, trends, and topics of interest, it will be your go-to source for info on this emerging tech.
