Artificial Intelligence | News | Insights | AiThority
[bsfp-cryptocurrency style=”widget-18″ align=”marquee” columns=”6″ coins=”selected” coins-count=”6″ coins-selected=”BTC,ETH,XRP,LTC,EOS,ADA,XLM,NEO,LTC,EOS,XEM,DASH,USDT,BNB,QTUM,XVG,ONT,ZEC,STEEM” currency=”USD” title=”Cryptocurrency Widget” show_title=”0″ icon=”” scheme=”light” bs-show-desktop=”1″ bs-show-tablet=”1″ bs-show-phone=”1″ custom-css-class=”” custom-id=”” css=”.vc_custom_1523079266073{margin-bottom: 0px !important;padding-top: 0px !important;padding-bottom: 0px !important;}”]

AI Will Have a Record Year in 2022, but Not the Way You Think

Artificial intelligence is being used in an increasing number of industries and business categories, leading to a record year of revenues and investment in 2021. That trend will only accelerate in the coming year as the race to apply the technology to new industries and new purposes within leading industries leads to record numbers in venture capital and acquisitions. 

It’s difficult to overstate the importance of AI as a systematic force; according to a Statista report, the AI industry generated some $35B in revenue in 2021, and is likely to increase that by 50 percent or more in 2022. On the other hand, it’s quite easy to overstate its effect in any single application. That it is a considerable part of the future of any software-based business is inarguable, but its power comes from being almost universally helpful, not a miracle solution for a particular problem. 

Recommended: Hot Startups: 12 European Startups win Impact Shakers Award 2021

Three Fronts to the AI Melee 

As such, interested parties are looking beyond the hype and realizing the actual limits and potentials presented by the technology as a whole. The opportunities for advancing it fall into three general categories. 

Data, which one might think of as the raw material for machine learning systems, is arguably the most important and one where we will see increasingly sophisticated competition and investment. Software has always been limited by the “garbage in, garbage out” idea and AI is no exception — and its designers are learning how much data is needed to avoid that trap. The US and China in particular have begun siloing their precious data stores and companies are hoarding wherever possible; the EU, with its tighter regulations on collection and storage of data, will likely lag in this respect due to fear of violating frameworks like the GDPR. 

Intellectual property in the form of actual models or machine learning engines and platforms is the next most important.

One way to track this is in numbers of patents filed, a metric that puts US companies like IBM, Google, Microsoft, and Intel well out in front, with Korea’s Samsung and the EU’s Siemens behind. However, this picture is incomplete due to China’s unique approach to IP, and the shrewd observer will assume that, as in other measures, the country is likely competitive on the level of the US even if it’s difficult to reliably quantify its presence. 

The last area of the competition is funding, which is, of course, important but hard to summarize. AI startups attracted some $36 billion in venture capital in 2020, and that number was surpassed within the first six months of 2021. This growth is impressive, but so is the investment by governments and research institutions that fund basic research and open-source projects often cited or iterated on in private research. China especially has dedicated tens and likely over a hundred billion towards AI research through direct funding or through corporations under government influence. Whatever the real numbers, they will only increase in the near and medium-term. 

Top AI and Analytics Update: Sisense Unveils Sisense Notebooks

From Dipping a Toe to Plunging In 

According to a Deloitte survey, half of all companies asked were bringing AI processes into their operations, and this number is likely to increase to 75 percent by 2023. But increasingly these companies are realizing that AI is a game-changer in a broad, systematic sense rather than a magic bullet. 

Related Posts
1 of 1,816

Leader industries like communications, entertainment, financial services, and healthcare have experimented with and ultimately integrated AI in ways that don’t attempt to reinvent the wheel. Instead, they are finding it provides reliably improvements to user experience and productivity, with things like better recommendations, smart document handling, and automated compliance. 

This low-flash success means companies feel safe building a portfolio of AI-powered features or services and keeping only those that produce results. The promise of AI revolutionizing any given process or industry is rarely true, but the other side of the coin is any company that fails to integrate helpful AI-based improvements in an inclusive and smart way will be left in the dust. 

Following these leaders are more conservative industries like logistics, manufacturing, and energy. Having observed the initial flailing settle down into more predictable improvements, they too are now ready to bring in the AI tools that have graduated from experimental to essential. Expect big contracts between enterprise AI leaders and legacy industries looking to check another box on their digital transformation roadmap. 

Breaking Down Barriers Between Innovators and Incumbents 

One area of the industry that will evolve over the next year is M&A. Until fairly recently startups and large companies like pharmaceuticals have operated in close coordination but ultimately independently. That will soon change as they both hit the soft limits imposed by lack of access to either data or new IP. 

To take biotech as an example, large companies like Pfizer have made some gains by using fairly basic AI to activate decades of data they’ve accumulated. Meanwhile, the startups have also made limited gains by applying cutting-edge AI to the modest stores of data they can access. While some data-sharing and IP-licensing agreements have been hammered out, we can expect those relationships to deepen in 2022 with an increase in strategic investments and outright acquisitions by large IP-starved corporations. 

On the other hand, we may see a decline in international deals and data-sharing as the value of certain AI processes and databases are recognized from a national security and IP protection perspective. 

AI and Leadership 

The last trend worth mentioning here is that there may well be a shift away from AI itself and towards improving how it is used and integrated. CEOs and CTOs will not just be using AI at a high level to help manage their companies and make smarter risk assessments, but they will have to make choices as to how AI is brought on in the first place. 

Expect an increase in usage of AI-adjacent services that help deploy and track the effects of AI services and products. Almost recursively, many of these will themselves employ AI — after all, machine learning models are good at teasing out insights from chaotic datasets like social networks and employee activity. 

In other words, the largest shift will be not simply towards “more AI” but towards quantifying, understanding, and streamlining its use. Having graduated from “nice to have” to “must-have,” it’s time for leadership to get serious about how this new tech can be closely integrated with existing business processes. We’re ready to contribute its expertise as 2022’s paradigm shifts take place. 

[To share your insights with us, please write to]


Comments are closed.