AI Adoption within the Enterprise 2021 – O’Reilly

In the course of the first weeks of February, we requested recipients of our Knowledge and AI Newsletters to take part in a survey on AI adoption within the enterprise. We have been enthusiastic about answering two questions. First, we wished to know how using AI grew previously yr. We have been additionally within the apply of AI: how builders work, what strategies and instruments they use, what their issues are, and what improvement practices are in place.

Essentially the most putting result’s the sheer variety of respondents. In our 2020 survey, which reached the identical viewers, we had 1,239 responses. This yr, we had a complete of 5,154. After eliminating 1,580 respondents who didn’t full the survey, we’re left with 3,574 responses—virtually 3 times as many as final yr. It’s potential that pandemic-induced boredom led extra folks to reply, however we doubt it. Whether or not they’re placing merchandise into manufacturing or simply kicking the tires, extra individuals are utilizing AI than ever earlier than.

Study quicker. Dig deeper. See farther.

Govt Abstract

• We had virtually 3 times as many responses as final yr, with related efforts at promotion. Extra individuals are working with AI.
• Prior to now, firm tradition has been essentially the most important barrier to AI adoption. Whereas it’s nonetheless a difficulty, tradition has dropped to fourth place.
• This yr, essentially the most important barrier to AI adoption is the shortage of expert folks and the problem of hiring. That scarcity has been predicted for a number of years; we’re lastly seeing it.
• The second-most important barrier was the provision of high quality knowledge. That realization is an indication that the sector is rising up.
• The share of respondents reporting “mature” practices has been roughly the identical for the previous couple of years. That isn’t stunning, given the rise within the variety of respondents: we suspect many organizations are simply starting their AI tasks.
• The retail business sector has the best share of mature practices; schooling has the bottom. However schooling additionally had the best share of respondents who have been “contemplating” AI.
• Comparatively few respondents are utilizing model management for knowledge and fashions. Instruments for versioning knowledge and fashions are nonetheless immature, however they’re vital for making AI outcomes reproducible and dependable.

Respondents

Of the three,574 respondents who accomplished this yr’s survey, 3,099 have been working with AI not directly: contemplating it, evaluating it, or placing merchandise into manufacturing. Of those respondents, it’s not a shock that the most important quantity are based mostly in the US (39%) and that roughly half have been from North America (47%). India had the second-most respondents (7%), whereas Asia (together with India) had 16% of the entire. Australia and New Zealand accounted for 3% of the entire, giving the Asia-Pacific (APAC) area 19%. Somewhat over 1 / 4 (26%) of respondents have been from Europe, led by Germany (4%). 7% of the respondents have been from South America, and a pair of% have been from Africa. Apart from Antarctica, there have been no continents with zero respondents, and a complete of 111 nations have been represented. These outcomes that curiosity and use of AI is worldwide and rising.

This yr’s outcomes match final yr’s knowledge nicely. However it’s equally essential to note what the info doesn’t say. Solely 0.2% of the respondents stated they have been from China. That clearly doesn’t replicate actuality; China is a frontrunner in AI and possibly has extra AI builders than some other nation, together with the US. Likewise, 1% of the respondents have been from Russia. Purely as a guess, we suspect that the variety of AI builders in Russia is barely smaller than the quantity within the US. These anomalies say way more about who the survey reached (subscribers to O’Reilly’s newsletters) than they are saying concerning the precise variety of AI builders in Russia and China.

The respondents represented a various vary of industries. Not surprisingly, computer systems, electronics, and know-how topped the charts, with 17% of the respondents. Monetary providers (15%), healthcare (9%), and schooling (8%) are the industries making the next-most important use of AI. We see comparatively little use of AI within the pharmaceutical and chemical industries (2%), although we count on that to alter sharply given the position of AI in growing the COVID-19 vaccine. Likewise, we see few respondents from the automotive business (2%), although we all know that AI is vital to new merchandise corresponding to autonomous autos.

3% of the respondents have been from the vitality business, and one other 1% from public utilities (which incorporates a part of the vitality sector). That’s a good quantity by itself, however we’ve to ask: Will AI play a job in rebuilding our frail and outdated vitality infrastructure, as occasions of the previous couple of years—not simply the Texas freeze or the California fires—have demonstrated? We count on that it’s going to, although it’s honest to ask whether or not AI techniques educated on normative knowledge can be sturdy within the face of “black swan” occasions. What is going to an AI system do when confronted with a uncommon state of affairs, one which isn’t well-represented in its coaching knowledge? That, in any case, is the issue dealing with the builders of autonomous autos. Driving a automotive safely is simple when the opposite visitors and pedestrians all play by the foundations. It’s solely troublesome when one thing sudden occurs. The identical is true of {the electrical} grid.

We additionally count on AI to reshape agriculture (1% of respondents). As with vitality, AI-driven modifications gained’t come shortly. Nevertheless, we’ve seen a gradual stream of AI tasks in agriculture, with objectives starting from detecting crop illness to killing moths with small drones.

Lastly, 8% of respondents stated that their business was “Different,” and 14% have been grouped into “All Others.” “All Others” combines 12 industries that the survey listed as potential responses (together with automotive, pharmaceutical and chemical, and agriculture) however that didn’t have sufficient responses to indicate within the chart. “Different” is the wild card, comprising industries we didn’t listing as choices. “Different” seems within the fourth place, simply behind healthcare. Sadly, we don’t know which industries are represented by that class—nevertheless it reveals that the unfold of AI has certainly grow to be broad!

Maturity

Roughly one quarter of the respondents described their use of AI as “mature” (26%), which means that they’d revenue-bearing AI merchandise in manufacturing. That is virtually precisely in keeping with the outcomes from 2020, the place 25% of the respondents reported that they’d merchandise in manufacturing (“Mature” wasn’t a potential response within the 2020 survey.)

This yr, 35% of our respondents have been “evaluating” AI (trials and proof-of-concept tasks), additionally roughly the identical as final yr (33%). 13% of the respondents weren’t making use of AI or contemplating utilizing it; that is down from final yr’s quantity (15%), however once more, it’s not considerably totally different.

What will we make of the respondents who’re “contemplating” AI however haven’t but began any tasks (26%)? That’s not an choice final yr’s respondents had. We suspect that final yr respondents who have been contemplating AI stated they have been both “evaluating” or “not utilizing” it.

Wanting on the issues respondents confronted in AI adoption supplies one other technique to gauge the general maturity of AI as a area. Final yr, the foremost bottleneck holding again adoption was firm tradition (22%), adopted by the problem of figuring out acceptable use circumstances (20%). This yr, cultural issues are in fourth place (14%) and discovering acceptable use circumstances is in third (17%). That’s a really important change, notably for company tradition. Corporations have accepted AI to a a lot better diploma, though discovering acceptable issues to unravel nonetheless stays a problem.

The largest issues on this yr’s survey are lack of expert folks and problem in hiring (19%) and knowledge high quality (18%). It’s no shock that the demand for AI experience has exceeded the provision, nevertheless it’s essential to understand that it’s now grow to be the most important bar to wider adoption. The largest abilities gaps have been ML modelers and knowledge scientists (52%), understanding enterprise use circumstances (49%), and knowledge engineering (42%). The necessity for folks managing and sustaining computing infrastructure was comparatively low (24%), hinting that firms are fixing their infrastructure necessities within the cloud.

It’s gratifying to notice that organizations beginning to understand the significance of knowledge high quality (18%). We’ve recognized about “rubbish in, rubbish out” for a very long time; that goes double for AI. Dangerous knowledge yields dangerous outcomes at scale.

Hyperparameter tuning (2%) wasn’t thought of an issue. It’s on the backside of the listing—the place, we hope, it belongs. That will replicate the success of automated instruments for constructing fashions (AutoML, though as we’ll see later, most respondents aren’t utilizing them). It’s extra regarding that workflow reproducibility (3%) is in second-to-last place. This is sensible, provided that we don’t see heavy utilization of instruments for mannequin and knowledge versioning. We’ll take a look at this later, however having the ability to reproduce experimental outcomes is vital to any science, and it’s a widely known downside in AI.

Maturity by Continent

When wanting on the geographic distribution of respondents with mature practices, we discovered virtually no distinction between North America (27%), Asia (27%), and Europe (28%). In distinction, in our 2018 report, Asia was behind in mature practices, although it had a markedly greater variety of respondents within the “early adopter” or “exploring” levels. Asia has clearly caught up. There’s no important distinction between these three continents in our 2021 knowledge.

We discovered a smaller share of respondents with mature practices and the next share of respondents who have been “contemplating” AI in South America (20%), Oceania (Australia and New Zealand, 18%), and Africa (17%). Don’t underestimate AI’s future influence on any of those continents.

Lastly, the share of respondents “evaluating” AI was virtually the identical on every continent, various solely from 31% (South America) to 36% (Oceania).

Maturity by Business

Whereas AI maturity doesn’t rely strongly on geography, we see a unique image if we take a look at maturity by business.

Wanting on the prime eight industries, monetary providers (38%), telecommunications (37%), and retail (40%) had the best share of respondents reporting mature practices. And whereas it had by far the best variety of respondents, computer systems, electronics, and know-how was in fourth place, with 35% of respondents reporting mature practices. Schooling (10%) and authorities (16%) have been the laggards. Healthcare and life sciences, at 28%, have been within the center, as have been manufacturing (25%), protection (26%), and media (29%).

Alternatively, if we take a look at industries which are contemplating AI, we discover that schooling is the chief (48%). Respondents working in authorities and manufacturing appear to be considerably additional alongside, with 49% and 47% evaluating AI, which means that they’ve pilot or proof-of-concept tasks in progress.

This will simply be a trick of the numbers: each group provides as much as 100%, so if there are fewer “mature” practices in a single group, the share of “evaluating” and “contemplating” practices needs to be greater. However there’s additionally an actual sign: respondents in these industries could not take into account their practices “mature,” however every of those business sectors had over 100 respondents, and schooling had virtually 250. Manufacturing must automate many processes (from meeting to inspection and extra); authorities has been as challenged as any business by the worldwide pandemic, and has at all times wanted methods to “do extra with much less”; and schooling has been experimenting with know-how for numerous years now. There’s a actual need to do extra with AI in these fields. It’s price stating that instructional and governmental purposes of AI regularly increase moral questions—and some of the essential points for the following few years can be seeing how these organizations reply to moral issues.

The Apply of AI

Now that we’ve mentioned the place mature practices are discovered, each geographically and by business, let’s see what a mature apply seems like. What do these organizations have in widespread? How are they totally different from organizations which are evaluating or contemplating AI?

Strategies

First, 82% of the respondents are utilizing supervised studying, and 67% are utilizing deep studying. Deep studying is a set of algorithms which are widespread to virtually all AI approaches, so this overlap isn’t stunning. (Members may present a number of solutions.) 58% claimed to be utilizing unsupervised studying.

After unsupervised studying, there was a big drop-off. Human-in-the-loop, information graphs, reinforcement studying, simulation, and planning and reasoning all noticed utilization beneath 40%. Surprisingly, pure language processing wasn’t within the image in any respect. (A really small variety of respondents wrote in “pure language processing” as a response, however they have been solely a small share of the entire.) That is important and positively price watching over the following few months. In the previous couple of years, there have been many breakthroughs in NLP and NLU (pure language understanding): everybody within the business has examine GPT-3, and lots of distributors are betting closely on utilizing AI to automate customer support name facilities and related purposes. This survey means that these purposes nonetheless haven’t moved into apply.

We requested an analogous query to respondents who have been contemplating or evaluating using AI (60% of the entire). Whereas the chances have been decrease, the applied sciences appeared in the identical order, with only a few variations. This means that respondents who’re nonetheless evaluating AI are experimenting with fewer applied sciences than respondents with mature practices. That means (moderately sufficient) that respondents are selecting to “begin easy” and restrict the strategies that they experiment with.

Knowledge

We additionally requested what varieties of knowledge our “mature” respondents are utilizing. Most (83%) are utilizing structured knowledge (logfiles, time sequence knowledge, geospatial knowledge). 71% are utilizing textual content knowledge—that isn’t in step with the variety of respondents who reported utilizing NLP, except “textual content” is getting used generically to incorporate any knowledge that may be represented as textual content (e.g., kind knowledge). 52% of the respondents reported utilizing pictures and video. That appears low relative to the quantity of analysis we examine AI and pc imaginative and prescient. Maybe it’s not stunning although: there’s no motive for enterprise use circumstances to be in sync with tutorial analysis. We’d count on most enterprise purposes to contain structured knowledge, kind knowledge, or textual content knowledge of some form. Comparatively few respondents (23%) are working with audio, which stays very difficult.

Once more, we requested an analogous query to respondents who have been evaluating or contemplating AI, and once more, we acquired related outcomes, although the share of respondents for any given reply was considerably smaller (4–5%).

Danger

Once we requested respondents with mature practices what dangers they checked for, 71% stated “sudden outcomes or predictions.” Interpretability, mannequin degradation over time, privateness, and equity additionally ranked excessive (over 50%), although it’s disappointing that solely 52% of the respondents chosen this feature. Safety can be a priority, at 42%. AI raises essential new safety points, together with the potential for poisoned knowledge sources and reverse engineering fashions to extract non-public info.

It’s laborious to interpret these outcomes with out understanding precisely what purposes are being developed. Privateness, safety, equity, and security are essential issues for each software of AI, nevertheless it’s additionally essential to understand that not all purposes are the identical. A farming software that detects crop illness doesn’t have the identical type of dangers as an software that’s approving or denying loans. Security is a a lot greater concern for autonomous autos than for personalised buying bots. Nevertheless, do we actually imagine that these dangers don’t should be addressed for almost half of all tasks?

Instruments

Respondents with mature practices clearly had their favourite instruments: scikit-learn, TensorFlow, PyTorch, and Keras every scored over 45%, with scikit-learn and TensorFlow the leaders (each with 65%). A second group of instruments, together with Amazon’s SageMaker (25%), Microsoft’s Azure ML Studio (21%), and Google’s Cloud ML Engine (18%), clustered round 20%, together with Spark NLP and spaCy.

When requested which instruments they deliberate to include over the approaching 12 months, roughly half of the respondents answered mannequin monitoring (57%) and mannequin visualization (49%). Fashions grow to be stale for a lot of causes, not the least of which is modifications in human conduct, modifications for which the mannequin itself could also be accountable. The power to observe a mannequin’s efficiency and detect when it has grow to be “stale” can be more and more essential as companies develop extra reliant on AI and in flip demand that AI tasks show their worth.

Responses from those that have been evaluating or contemplating AI have been related, however with some fascinating variations: scikit-learn moved from first place to 3rd (48%). The second group was led by merchandise from cloud distributors that incorporate AutoML: Microsoft Azure ML Studio (29%), Google Cloud ML Engine (25%), and Amazon SageMaker (23%). These merchandise have been considerably extra fashionable than they have been amongst “mature” customers. The distinction isn’t enormous, however it’s putting. Liable to over-overinterpreting, customers who’re newer to AI are extra inclined to make use of vendor-specific packages, extra inclined to make use of AutoML in one in all its incarnations, and considerably extra inclined to go along with Microsoft or Google moderately than Amazon. It’s additionally potential that scikit-learn has much less model recognition amongst those that are comparatively new to AI in comparison with packages from organizations like Google or Fb.

When requested particularly about AutoML merchandise, 51% of “mature” respondents stated they weren’t utilizing AutoML in any respect. 22% use Amazon SageMaker; 16% use Microsoft Azure AutoML; 14% use Google Cloud AutoML; and different instruments have been all underneath 10%. Amongst customers who’re evaluating or contemplating AI, solely 40% stated they weren’t utilizing AutoML in any respect—and the Google, Microsoft, and Amazon packages have been all however tied (27–28%). AutoML isn’t but an enormous a part of the image, nevertheless it seems to be gaining traction amongst customers who’re nonetheless contemplating or experimenting with AI. And it’s potential that we’ll see elevated use of AutoML instruments amongst mature customers, of whom 45% indicated that they’d be incorporating instruments for automated mannequin search and hyperparameter tuning (in a phrase, AutoML) within the coming but.

Deployment and Monitoring

An AI venture means nothing if it may’t be deployed; even tasks which are solely supposed for inside use want some type of deployment. Our survey confirmed that AI deployment continues to be largely unknown territory, dominated by homegrown advert hoc processes. The three most important instruments for deploying AI all had roughly 20% adoption: MLflow (22%), TensorFlow Prolonged, a.okay.a. TFX (20%), and Kubeflow (18%). Three merchandise from smaller startups—Domino, Seldon, and Cortex—had roughly 4% adoption. However essentially the most frequent reply to this query was “not one of the above” (46%). Since this query was solely requested of respondents with “mature” AI practices (i.e., respondents who’ve AI merchandise in manufacturing), we will solely assume that they’ve constructed their very own instruments and pipelines for deployment and monitoring. Given the numerous varieties that an AI venture can take, and that AI deployment continues to be one thing of a darkish artwork, it isn’t stunning that AI builders and operations groups are solely beginning to undertake third-party instruments for deployment.

Versioning

Supply management has lengthy been a typical apply in software program improvement. There are a lot of well-known instruments used to construct supply code repositories.

We’re assured that AI tasks use supply code repositories corresponding to Git or GitHub; that’s a typical apply for all software program builders. Nevertheless, AI brings with it a unique set of issues. In AI techniques, the coaching knowledge is as essential as, if no more essential than, the supply code. So is the mannequin constructed from the coaching knowledge: the mannequin displays the coaching knowledge and hyperparameters, along with the supply code itself, and could also be the results of a whole bunch of experiments.

Our survey reveals that AI builders are solely beginning to use instruments for knowledge and mannequin versioning. For knowledge versioning, 35% of the respondents are utilizing homegrown instruments, whereas 46% responded “not one of the above,” which we take to imply they’re utilizing nothing greater than a database. 9% are utilizing DVC, 8% are utilizing instruments from Weights & Biases, and 5% are utilizing Pachyderm.

Instruments for mannequin and experiment monitoring have been used extra regularly, though the outcomes are essentially the identical. 29% are utilizing homegrown instruments, whereas 34% stated “not one of the above.” The main instruments have been MLflow (27%) and Kubeflow (18%), with Weights & Biases at 8%.

Respondents who’re contemplating or evaluating AI are even much less doubtless to make use of knowledge versioning instruments: 59% stated “not one of the above,” whereas solely 26% are utilizing homegrown instruments. Weights & Biases was the preferred third-party resolution (12%). When requested about mannequin and experiment monitoring, 44% stated “not one of the above,” whereas 21% are utilizing homegrown instruments. It’s fascinating, although, that on this group, MLflow (25%) and Kubeflow (21%) ranked above homegrown instruments.

Though the instruments accessible for versioning fashions and knowledge are nonetheless rudimentary, it’s disturbing that so many practices, together with people who have AI merchandise in manufacturing, aren’t utilizing them. You may’t reproduce outcomes should you can’t reproduce the info and the fashions that generated the outcomes. We’ve stated {that a} quarter of respondents thought of their AI apply mature—nevertheless it’s unclear what maturity means if it doesn’t embrace reproducibility.

The Backside Line

Prior to now two years, the viewers for AI has grown, nevertheless it hasn’t modified a lot: Roughly the identical share of respondents take into account themselves to be a part of a “mature” apply; the identical industries are represented, and at roughly the identical ranges; and the geographical distribution of our respondents has modified little.

We don’t know whether or not to be gratified or discouraged that solely 50% of the respondents listed privateness or ethics as a threat they have been involved about. With out knowledge from prior years, it’s laborious to inform whether or not that is an enchancment or a step backward. However it’s troublesome to imagine that there are such a lot of AI purposes for which privateness, ethics, and safety aren’t important dangers.

Device utilization didn’t current any large surprises: the sector is dominated by scikit-learn, TensorFlow, PyTorch, and Keras, although there’s a wholesome ecosystem of open supply, commercially licensed, and cloud native instruments. AutoML has but to make large inroads, however respondents representing much less mature practices appear to be leaning towards automated instruments and are much less doubtless to make use of scikit-learn.

The variety of respondents who aren’t addressing knowledge or mannequin versioning was an unwelcome shock. These practices ought to be foundational: central to growing AI merchandise which have verifiable, repeatable outcomes. Whereas we acknowledge that versioning instruments acceptable to AI purposes are nonetheless of their early levels, the variety of contributors who checked “not one of the above” was revealing—notably since “the above” included homegrown instruments. You may’t have reproducible outcomes should you don’t have reproducible knowledge and fashions. Interval.

Prior to now yr, AI within the enterprise has grown; the sheer variety of respondents will inform you that. However has it matured? Many new groups are coming into the sector, whereas the share of respondents who’ve deployed purposes has remained roughly fixed. In lots of respects, this means success: 25% of an even bigger quantity is greater than 25% of a smaller quantity. However is software deployment the fitting metric for maturity? Enterprise AI gained’t actually have matured till improvement and operations teams can have interaction in practices like steady deployment, till outcomes are repeatable (not less than in a statistical sense), and till ethics, security, privateness, and safety are major moderately than secondary issues. Mature AI? Sure, enterprise AI has been maturing. However it’s time to set the bar for maturity greater.