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AI adoption, expansion, and the pandemic accelerant

In Software & Cloud Economics, Thought Leadership

The Covid-19 pandemic holds potential to be an accelerant to AI adoption for organizations round the world.


As the pandemic rages on people are increasingly trading in-person interactions to digital engagement. From socialization, to shopping, to entertainment, to banking, to investing, to church, to dating, to education – we are remixing our time, with a material shift from physical to digital.


As that continues and accelerates, it will become the new normal. Now, we can get depressed and view this through an Orwellian lens. And certainly, someone’s Big Brother is watching, but we can also put a positive lens on this once in a century occurrence.


For example, having the flexibility to work from home and be not just productive but impactful as individuals, teams, and corporations can translate into a better work/life balance or at least a better work/life blend.


There are other examples we can think of, but the point is that as we increasingly find ourselves in the digital world, we will leave an ever increasing collection of digital breadcrumbs. Those digital breadcrumbs become the input for organizations to better understand us and our needs in a more impactful way. And as an increasing number of companies embracing a customer obsession tenet, that can be a good thing. AI is the means to make sense of all that data!


What are we seeing as far as adoption of AI goes? A ZDNet article in 2019 covering a recent survey of 3,000 CIOs by IT Analyst Gartner cited that “companies implementing these technologies has grown by 270 percent in the past four years” .[1]


The article went on to point out that an estimated 37% of the companies surveyed embraced or implemented AI in one form or another and in varying degrees.

Similarly, in December of 2019, O’Reilly fielded a survey of close to 1,400 (1,388) respondents, the results of which were published in March of 2020. [2]


The results showed that 85% of the respondent’s organizations were “evaluating AI or using it in production”. Fifty percent of the respondents in the O’Reilly survey indicated that their organizations use of AI was “mature” as it was being used in either analysis or production.


The survey was international with 50% of respondents from the US, 23% from Western Europe, and 15% from Asia.


Many other surveys can be cited with similar findings. Although there is variability in the results when comparing survey results, directionally we can accept that usage of AI is increasing with no slowdown in sight. There are some challenges including skill set, executive support, and being able to demonstrate ROI objectively.

But all of those will to dissipate as organizations become more comfortable with the tools, and as the tools become more approachable and easier to adopt and use.


Clearly adoption is happening, but where is AI being used and what have the outcomes been?


A survey by McKinsey and Company published in November of 2019 [3] offers some insights into those questions. This survey, similar to the Gartner survey, highlights a 25% year-over-year increase in adoption of AI.


Additionally, the survey highlights the promising news that “A majority of executives whose companies have adopted AI report that it has provided an uptick in revenue in the business areas where it is used, and 44 percent say AI has reduced costs”.


To better understand the usage patterns, the survey took the general category of “AI” and broke it out into 9 technology areas. In turn, respondents indicated their usage of the 9 areas for their company that in-turn were aggregated to provide an industry map.


This data provides great clarity as to what AI technologies are being embraced, but also serves as a reminded as to how dynamic this technology area truly is.


 Figure 1 AI Technology Usage By Industry - McKinsey


 Figure 1 AI Technology Usage By Industry - McKinsey

There are many interesting use cases for specific companies that we can look at. For example, innovators like DeepIntent use AI to target pharmaceutical advertising to people who have certain publicly available and observable demographics, that correspond to particular maladies.


This has benefits for the pharmaceutical company that can be more efficient and targeted with their advertising – particularly important in these times of increased government and societal pressure and calls for transparency and drug price reductions.


It also holds benefits for the user, as it may prompt them to check into a particular condition they may be showing early symptoms of or make them a better educated user of the medicine that they may end up having to take.


When it comes to the underlying technology to support your AI initiatives, the hyperscale cloud providers factor prominently. In the early days of AI, the required investment was extremely high and was a barrier-to-entry for many companies and organizations, particularly SME (Small to Medium Enterprises).


In the early days AI was the purview of big companies, with big IT budgets and big Data Science teams. Over time the hyperscale providers have “democratized” AI services, making them available to practically any company or entity of any size.


In the January 2020 Magic Quadrant for AI Developer Services published by Gartner, Amazon Web Services, Microsoft, Google, and IBM landed in the Leaders Quadrant with AWS in a lead position. In the case of AWS, as well as the other leaders, there is the realization that they have to offer a variety of services and layers to support the diversity of skills and objectives for customers.



For example, AWS offers the ML stack shown to the right. For organizations that have depth in their Data Science team, the ML Frameworks and Infrastructure on the bottom of the stack offer rich services with granular control.

But for organizations that don’t have that Data Science depth or ones that have the team but who want to enable broader usage, the ML Services layer will be most useful.



Finally for organizations that simply want to inject AI capabilities (for example image recognition) into an existing or new application, AWS offers an ever expanding set of AI Services that can be employed with API (Application Programing Interface) calls from applications.


To be clear, organizations can choose to use any and all of these layers as they embrace and expand usage of AI within their organization.


And although we discuss AWS here, the other hyperscale cloud providers and specialized vendors in the MQ have similar capabilities and models.


Assuming the new normal, is one where people leave an ever-richer set of digital breadcrumbs, organizations have the opportunity to put that dynamic to good use benefiting all. It is an opportunity to understand customers, as people, as individuals.


We have the opportunity to engage with them in an authentic and meaningful way even in a digital context. Intelligent companies and brands will recognize and embrace this opportunity leveraging AI. But AI can be daunting, and the skill gap is a material factor that came up in the various surveys cited above.


That is where an objective proven technology partner, versant in all the major cloud providers and their respective AI capabilities becomes invaluable. That partner can deliver the strategic perspective, and augment your skills, filling in any skill or deployment gaps along the way, allowing you to chart a course that effectively applies AI to drive maximum value for years to come!


We’ll help you understand the work required to move your applications to the cloud, and the cost savings in licenses that you can achieve


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