AI Adoption within the Enterprise 2021 – O’Reilly


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

Essentially the most hanging 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 thrice 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 sooner. Dig deeper. See farther.


Govt Abstract

  • We had virtually thrice as many responses as final yr, with related efforts at promotion. Extra individuals are working with AI.
  • Prior to now, firm tradition has been probably the most important barrier to AI adoption. Whereas it’s nonetheless a problem, tradition has dropped to fourth place.
  • This yr, probably 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 knowledge. That realization is an indication that the sphere is rising up.
  • The proportion 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 important 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 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 america (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%. Just a little 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 international locations have been represented. These outcomes that curiosity and use of AI is worldwide and rising.

This yr’s outcomes match final yr’s knowledge effectively. But it surely’s equally necessary to note what the information doesn’t say. Solely 0.2% of the respondents mentioned they have been from China. That clearly doesn’t replicate actuality; China is a pacesetter 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.

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

The respondents represented a various vary of industries. Not surprisingly, computer systems, electronics, and expertise topped the charts, with 17% of the respondents. Monetary companies (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 function of AI in creating the COVID-19 vaccine. Likewise, we see few respondents from the automotive business (2%), although we all know that AI is essential to new merchandise reminiscent of autonomous autos.

3% of the respondents have been from the power business, and one other 1% from public utilities (which incorporates a part of the power sector). That’s a decent quantity by itself, however we’ve to ask: Will AI play a job in rebuilding our frail and outdated power 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 skilled on normative knowledge shall be strong within the face of “black swan” occasions. What’s going to an AI system do when confronted with a uncommon scenario, one which isn’t well-represented in its coaching knowledge? That, in any case, is the issue going through the builders of autonomous autos. Driving a automobile safely is simple when the opposite site 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 power, AI-driven modifications received’t come shortly. Nevertheless, we’ve seen a gradual stream of AI tasks in agriculture, with targets starting from detecting crop illness to killing moths with small drones.

Lastly, 8% of respondents mentioned 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 point out 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—but it surely exhibits that the unfold of AI has certainly develop into broad!

Determine 2. Industries utilizing AI

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 step 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 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 yr’s respondents had. We suspect that final yr respondents who have been contemplating AI mentioned they have been both “evaluating” or “not utilizing” it.

Determine 3. AI apply maturity

Wanting on the issues respondents confronted in AI adoption supplies one other approach to gauge the general maturity of AI as a subject. Final yr, the foremost bottleneck holding again adoption was firm tradition (22%), adopted by the issue of figuring out applicable use instances (20%). This yr, cultural issues are in fourth place (14%) and discovering applicable use instances 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 applicable 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, but it surely’s necessary to comprehend that it’s now develop into the largest bar to wider adoption. The largest abilities gaps have been ML modelers and knowledge scientists (52%), understanding enterprise use instances (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-about 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 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 with the ability to reproduce experimental outcomes is important to any science, and it’s a widely known drawback in AI.

Determine 4. Bottlenecks to AI adoption

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 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 knowledge.

We discovered a smaller share of respondents with mature practices and a better 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 affect 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).

Determine 5. Maturity by continent

Maturity by Business

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

Wanting on the high eight industries, monetary companies (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 expertise was in fourth place, with 35% of respondents reporting mature practices. Training (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%).

Then again, if we take a look at industries which can be 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 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 needs to be larger. However there’s additionally an actual sign: respondents in these industries could not contemplate 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 expertise for numerous years now. There’s a actual need to do extra with AI in these fields. It’s value mentioning that academic and governmental functions of AI incessantly increase moral questions—and one of the crucial necessary points for the following few years shall be seeing how these organizations reply to moral issues.

Determine 6. Maturity by business (%)

The Observe 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 appears like. What do these organizations have in frequent? How are they completely different from organizations which can be evaluating or contemplating AI?

Methods

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 frequent to virtually all AI approaches, so this overlap isn’t stunning. (Contributors may 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 have been solely a small share of the entire.) That is important and positively value 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 functions. This survey means that these functions nonetheless haven’t moved into apply.

We requested the same query to respondents who have been contemplating or evaluating the usage of AI (60% of the entire). Whereas the odds 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 methods that they experiment with.

Determine 7. AI applied sciences utilized in mature practices

Information

We additionally requested what sorts 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 per the variety of respondents who reported utilizing NLP, until “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 laptop imaginative and prescient. Maybe it’s not stunning although: there’s no motive for enterprise use instances to be in sync with educational analysis. We’d count on most enterprise functions to contain structured knowledge, kind knowledge, or textual content knowledge of some sort. Comparatively few respondents (23%) are working with audio, which stays very difficult.

Once more, we requested the same query to respondents who have 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. Information varieties utilized in mature practices

Threat

Once we requested respondents with mature practices what dangers they checked for, 71% mentioned “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 necessary new safety points, together with the potential of poisoned knowledge sources and reverse engineering fashions to extract non-public info.

It’s laborious to interpret these outcomes with out realizing precisely what functions are being developed. Privateness, safety, equity, and security are necessary issues for each software of AI, but it surely’s additionally necessary to comprehend that not all functions 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 larger concern for autonomous autos than for customized procuring 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 improvement

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

Determine 10. Instruments utilized by mature practices

Responses from those that have been evaluating or contemplating AI have 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 have been considerably extra in style than they have been amongst “mature” customers. The distinction isn’t big, however it’s hanging. Susceptible 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 among its incarnations, and considerably extra inclined to go together 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 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 have 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 have been all however tied (27–28%). AutoML isn’t but an enormous a part of the image, but it surely 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 mission means nothing if it will probably’t be deployed; even tasks which can be solely supposed for inside use want some type of deployment. Our survey confirmed that AI deployment remains 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.ok.a. TFX (20%), and Kubeflow (18%). Three merchandise from smaller startups—Domino, Seldon, and Cortex—had roughly 4% adoption. However probably 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 various kinds that an AI mission can take, and that AI deployment remains 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.

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

Versioning

Supply management has lengthy been an ordinary apply in software program improvement. There are numerous well-known instruments used to construct supply code repositories.

We’re assured that AI tasks use supply code repositories reminiscent of Git or GitHub; that’s an ordinary apply for all software program builders. Nevertheless, AI brings with it a special set of issues. In AI techniques, the coaching knowledge is as necessary as, if no more necessary 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 lots of of experiments.

Our survey exhibits 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.

Determine 12. Automated instruments used for knowledge versioning

Instruments for mannequin and experiment monitoring have been used extra incessantly, though the outcomes are basically the identical. 29% are utilizing homegrown instruments, whereas 34% mentioned “not one of the above.” The main instruments have 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 knowledge versioning instruments: 59% mentioned “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% 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 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 possibly can’t reproduce outcomes for those who can’t reproduce the information and the fashions that generated the outcomes. We’ve mentioned {that a} quarter of respondents thought-about their AI apply mature—but it surely’s unclear what maturity means if it doesn’t embody reproducibility.

The Backside Line

Prior to now two years, the viewers for AI has grown, but it surely hasn’t modified a lot: Roughly the identical share of respondents contemplate 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 danger 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. But it surely’s troublesome 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 sphere 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 knowledge or mannequin versioning was an unwelcome shock. These practices needs to 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 members who checked “not one of the above” was revealing—notably since “the above” included homegrown instruments. You possibly can’t have reproducible outcomes for those who 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 getting into the sphere, 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 proper metric for maturity? Enterprise AI received’t actually have matured till improvement and operations teams can interact in practices like steady deployment, till outcomes are repeatable (at the least 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. But it surely’s time to set the bar for maturity larger.



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