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 had been interested by answering two questions. First, we needed to know how the usage of AI grew up to now 12 months. We had been additionally within the follow of AI: how builders work, what methods and instruments they use, what their considerations are, and what growth practices are in place.

Probably 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 12 months, 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—nearly thrice as many as final 12 months. 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 sooner. Dig deeper. See farther.

Govt Abstract

  • We had nearly thrice as many responses as final 12 months, with related efforts at promotion. Extra individuals are working with AI.
  • Previously, 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 12 months, essentially the most important barrier to AI adoption is the shortage of expert folks and the issue of hiring. That scarcity has been predicted for a number of years; we’re lastly seeing it.
  • The second-most important barrier was the supply of high quality information. That realization is an indication that the sector is rising up.
  • The proportion of respondents reporting “mature” practices has been roughly the identical for the previous couple of years. That isn’t shocking, given the rise within the variety of respondents: we suspect many organizations are simply starting their AI tasks.
  • The retail trade sector has the very best proportion of mature practices; training has the bottom. However training additionally had the very best proportion of respondents who had been “contemplating” AI.
  • Comparatively few respondents are utilizing model management for information and fashions. Instruments for versioning information and fashions are nonetheless immature, however they’re vital for making AI outcomes reproducible and dependable.


Of the three,574 respondents who accomplished this 12 months’s survey, 3,099 had been working with AI ultimately: contemplating it, evaluating it, or placing merchandise into manufacturing. Of those respondents, it’s not a shock that the most important quantity are primarily based in the US (39%) and that roughly half had been from North America (47%). India had the second-most respondents (7%), whereas Asia (together with India) had 16% of the full. Australia and New Zealand accounted for 3% of the full, giving the Asia-Pacific (APAC) area 19%. Just a little over 1 / 4 (26%) of respondents had been from Europe, led by Germany (4%). 7% of the respondents had been from South America, and a couple of% had been from Africa. Aside from Antarctica, there have been no continents with zero respondents, and a complete of 111 international locations had been represented. These outcomes that curiosity and use of AI is worldwide and rising.

This 12 months’s outcomes match final 12 months’s information nicely. However it’s equally necessary to note what the information doesn’t say. Solely 0.2% of the respondents mentioned they had been from China. That clearly doesn’t replicate actuality; China is a frontrunner in AI and doubtless has extra AI builders than every other nation, together with the US. Likewise, 1% of the respondents had 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.

Determine 1. Respondents working with AI by nation (prime 12)

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 training (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 function of AI in creating the COVID-19 vaccine. Likewise, we see few respondents from the automotive trade (2%), although we all know that AI is essential to new merchandise akin to autonomous autos.

3% of the respondents had been from the vitality trade, 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 got to ask: Will AI play a task 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’ll, although it’s truthful to ask whether or not AI techniques skilled on normative information might be strong 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 information? That, in spite of everything, 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 tough when one thing surprising 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 adjustments gained’t come rapidly. 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 mentioned that their trade was “Different,” and 14% had 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 record as choices. “Different” seems within the fourth place, simply behind healthcare. Sadly, we don’t know which industries are represented by that class—however it reveals that the unfold of AI has certainly turn into broad!

Determine 2. Industries utilizing AI


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

This 12 months, 35% of our respondents had been “evaluating” AI (trials and proof-of-concept tasks), additionally roughly the identical as final 12 months (33%). 13% of the respondents weren’t making use of AI or contemplating utilizing it; that is down from final 12 months’s quantity (15%), however once more, it’s not considerably completely 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 12 months’s respondents had. We suspect that final 12 months respondents who had been contemplating AI mentioned they had been both “evaluating” or “not utilizing” it.

Determine 3. AI follow maturity

Trying on the issues respondents confronted in AI adoption supplies one other method to gauge the general maturity of AI as a subject. Final 12 months, the key bottleneck holding again adoption was firm tradition (22%), adopted by the issue of figuring out applicable use circumstances (20%). This 12 months, cultural issues are in fourth place (14%) and discovering applicable use circumstances is in third (17%). That’s a really important change, notably for company tradition. Firms have accepted AI to a a lot larger diploma, though discovering applicable issues to resolve nonetheless stays a problem.

The most important issues on this 12 months’s survey are lack of expert folks and issue in hiring (19%) and information high quality (18%). It’s no shock that the demand for AI experience has exceeded the availability, however it’s necessary to understand that it’s now turn into the most important bar to wider adoption. The most important expertise gaps had been ML modelers and information scientists (52%), understanding enterprise use circumstances (49%), and information engineering (42%). The necessity for folks managing and sustaining computing infrastructure was comparatively low (24%), hinting that corporations are fixing their infrastructure necessities within the cloud.

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

Hyperparameter tuning (2%) wasn’t thought of an issue. It’s on the backside of the record—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 smart, provided that we don’t see heavy utilization of instruments for mannequin and information versioning. We’ll have a look at this later, however having the ability to reproduce experimental outcomes is vital to any science, and it’s a widely known drawback in AI.

Determine 4. Bottlenecks to AI adoption

Maturity by Continent

When trying on the geographic distribution of respondents with mature practices, we discovered nearly 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 larger variety of respondents within the “early adopter” or “exploring” phases. Asia has clearly caught up. There’s no important distinction between these three continents in our 2021 information.

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

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

Determine 5. Maturity by continent

Maturity by Business

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

Trying on the prime eight industries, monetary providers (38%), telecommunications (37%), and retail (40%) had the best proportion 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%) had been the laggards. Healthcare and life sciences, at 28%, had been within the center, as had been manufacturing (25%), protection (26%), and media (29%).

Alternatively, if we have a look at industries which can be contemplating AI, we discover that training 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 may occasionally 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 must be larger. However there’s additionally an actual sign: respondents in these industries might not contemplate their practices “mature,” however every of those trade sectors had over 100 respondents, and training had nearly 250. Manufacturing must automate many processes (from meeting to inspection and extra); authorities has been as challenged as any trade by the worldwide pandemic, and has at all times wanted methods to “do extra with much less”; and training has been experimenting with know-how for a lot of years now. There’s a actual want to do extra with AI in these fields. It’s price mentioning that academic and governmental functions of AI regularly increase moral questions—and probably the most necessary points for the following few years might be seeing how these organizations reply to moral issues.

Determine 6. Maturity by trade (p.c)

The Apply of AI

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


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

After unsupervised studying, there was a major drop-off. Human-in-the-loop, data graphs, reinforcement studying, simulation, and planning and reasoning all noticed utilization under 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 had been solely a small proportion of the full.) 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 trade has examine GPT-3, and plenty of distributors are betting closely on utilizing AI to automate customer support name facilities and related functions. This survey means that these functions nonetheless haven’t moved into follow.

We requested an analogous query to respondents who had been contemplating or evaluating the usage of AI (60% of the full). Whereas the odds had 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 implies (fairly sufficient) that respondents are selecting to “begin easy” and restrict the methods that they experiment with.

Determine 7. AI applied sciences utilized in mature practices


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

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

Determine 8. Knowledge sorts utilized in mature practices


After we requested respondents with mature practices what dangers they checked for, 71% mentioned “surprising 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 also be a priority, at 42%. AI raises necessary new safety points, together with the potential of poisoned information sources and reverse engineering fashions to extract personal info.

It’s arduous to interpret these outcomes with out realizing precisely what functions are being developed. Privateness, safety, equity, and security are necessary considerations for each software of AI, however it’s additionally necessary to understand that not all functions are the identical. A farming software that detects crop illness doesn’t have the identical sort 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 consider that these dangers don’t must be addressed for practically half of all tasks?

Determine 9. Dangers checked for throughout growth


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 turn into stale for a lot of causes, not the least of which is adjustments in human habits, adjustments for which the mannequin itself could also be accountable. The flexibility to watch a mannequin’s efficiency and detect when it has turn into “stale” might be more and more necessary as companies develop extra reliant on AI and in flip demand that AI tasks exhibit their worth.

Determine 10. Instruments utilized by mature practices

Responses from those that had been evaluating or contemplating AI had been related, however with some attention-grabbing 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 had been considerably extra in style than they had been amongst “mature” customers. The distinction isn’t enormous, however it’s putting. Vulnerable 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 considered one of its incarnations, and considerably extra inclined to go along with Microsoft or Google relatively 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 mentioned 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 had been all below 10%. Amongst customers who’re evaluating or contemplating AI, solely 40% mentioned they weren’t utilizing AutoML in any respect—and the Google, Microsoft, and Amazon packages had been all however tied (27–28%). AutoML isn’t but an enormous a part of the image, however 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 possibly’t be deployed; even tasks which can be solely meant for inner use want some sort of deployment. Our survey confirmed that AI deployment continues to be largely unknown territory, dominated by homegrown advert hoc processes. The three most vital 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 are able to solely assume that they’ve constructed their very own instruments and pipelines for deployment and monitoring. Given the numerous kinds that an AI venture can take, and that AI deployment continues to be one thing of a darkish artwork, it isn’t shocking that AI builders and operations groups are solely beginning to undertake third-party instruments for deployment.

Determine 11. Automated instruments utilized in mature practices for deployment
and monitoring


Supply management has lengthy been a regular follow in software program growth. There are various well-known instruments used to construct supply code repositories.

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

Our survey reveals that AI builders are solely beginning to use instruments for information and mannequin versioning. For information 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.

Determine 12. Automated instruments used for information versioning

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

Determine 13. Automated instruments used for mannequin and experiment monitoring

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

Though the instruments out there for versioning fashions and information 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 if you happen to can’t reproduce the information and the fashions that generated the outcomes. We’ve mentioned {that a} quarter of respondents thought of their AI follow mature—however it’s unclear what maturity means if it doesn’t embrace reproducibility.

The Backside Line

Previously two years, the viewers for AI has grown, however it hasn’t modified a lot: Roughly the identical proportion of respondents contemplate themselves to be a part of a “mature” follow; 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 danger they had been involved about. With out information from prior years, it’s arduous to inform whether or not that is an enchancment or a step backward. However it’s tough to consider that there are such a lot of AI functions 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 seemingly to make use of scikit-learn.

The variety of respondents who aren’t addressing information or mannequin versioning was an unwelcome shock. These practices must be foundational: central to creating AI merchandise which have verifiable, repeatable outcomes. Whereas we acknowledge that versioning instruments applicable to AI functions are nonetheless of their early phases, the variety of individuals who checked “not one of the above” was revealing—notably since “the above” included homegrown instruments. You may’t have reproducible outcomes if you happen to don’t have reproducible information and fashions. Interval.

Previously 12 months, AI within the enterprise has grown; the sheer variety of respondents will let you know that. However has it matured? Many new groups are coming into the sector, whereas the share of respondents who’ve deployed functions 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 precise metric for maturity? Enterprise AI gained’t actually have matured till growth and operations teams can interact in practices like steady deployment, till outcomes are repeatable (at the very least in a statistical sense), and till ethics, security, privateness, and safety are main relatively than secondary considerations. Mature AI? Sure, enterprise AI has been maturing. However it’s time to set the bar for maturity larger.


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