Click to learn more about author Asha Saxena.

AI’s rapid surge in value-add has left companies struggling to adopt this highly complex technology. Executives struggle to understand it. At a basic level, the terminology is confusing — machine learning, deep learning, computer vision, computational statistics, cognitive computing, natural language/speech processing, robotic process automation (RPA), AI, etc. The business use cases are unclear, and the experts are mostly in academia, have their own startups, or are at top tech companies such as the FANG (Facebook, Netflix, Amazon, Google). But with AI deployment in the corporate world coming to a crucial inflection point, many companies are still struggling to get their AI projects out of the proof of concept (POC) stage (meaning it is still a science lab experiment at many organizations). 

The adoption of using big data over the past half-decade has increased the need for a “translation between Data Science teams and business stakeholders,” according to Tim Gordon, a partner at Best Practice AI, in an article on Disruption Hub. Mr. Gordon suggests that corporations should consider appointing a Chief AI Officer (CAIO). He also suggests that partnering with an outsourced or Virtual Chief AI Officer could assist teams to “set strategy, support technology choices, and drive roll-out.”

Recently on CNBC’s “Halftime Report,” Mark Cuban spoke with Scott Wapner and said: “You know what’s interesting about AI? There are probably fewer than 10 companies on the market today that truly invest in and know how to purposely use artificial intelligence.” He went on to further say:

“We really have evolved into an [environment of] AI haves and have-nots, and part of the challenge/problem with all of this, is that small and up-and-coming companies are trying to use AI, but it’s challenging. Companies like Google, Facebook, Amazon, Apple, Netflix, and IBM can invest billions of dollars and afford to make mistakes and learn from their mistakes. Most companies cannot. The hardest part of AI is getting to a point where you know whether the results are valid or not.”

Mr. Cuban also mentioned:

“Netflix will keep on becoming smarter, and Amazon, when it comes to using data and selling products and services, where they use that data to know what to sell and how to sell it, it also helps guide them in business decisions. They won’t always be successful in every attempt, but the knowledge they have gained with artificial intelligence gives them a unique advantage. All the FANGS have an AI advantage in the AI haves and have-nots universe that people are not understanding.”

According to the Wall Street Journal, artificial intelligence has the potential to be transformative for businesses, but a recent study showed that only 25 percent of companies have an AI strategy beyond the IT department.

Some have made the argument that companies do not need to hire their first Chief Artificial Intelligence Officer. Kristian J. Hammond, a professor of computer science at Northwestern University, feels that the effective deployment of AI in the enterprise requires a focus on achieving business goals. He wrote in a Harvard Business Review article:

“Rushing towards an AI strategy and hiring someone with technical skills in AI to lead the charge might seem in tune with the current trends, but it ignores the reality that innovation initiatives only succeed when there is a solid understanding of actual business problems and goals. For AI to work in the enterprise, the goals of the enterprise must be the driving force.”

Enter the Role of the Chief AI Officer (CAIO) in the Enterprise

The Adoption Process of AI in Organizations

In 2018, William Falcon wrote an article titled Chief AI Officer — 7 Reasons Why Your Company Needs One, where he states:

“The adoption process goes something like this: An executive reads or is told multiple times about how AI can do X for their business. The CTO or CIO looks into it and concludes that AI can probably help the company save costs. However, the benefits, AI approach, and possible downsides may still be unclear.”

As an artificial intelligence keynote speaker, I have my own thoughts as to why creating or hiring for a Chief AI Officer role should be a consideration for mid-sized companies.

1. Your organization’s CTO or CIO might be an engineering expert, but maybe not AI specifically

Now, your CTO might be well versed in managing multiple types of software or your technology infrastructure stack, solving problems like cutting costs or approaching technical issues using the latest software engineering framework. However, it may take a while for this person or team to become an expert on the latest developments in artificial intelligence and how AI can help your company solve business goals.

Doctorate-level AI researchers speak and attend AI conferences, read and contribute to academic journals, and host private research events from other recognized scholars. If we look at a 2018 McKinsey & Company article called Artificial intelligence: The time to act is now, the authors point out that there is no definitive AI technology stack standard as of now. But they note:

“To bring some clarity to the seemingly chaotic supply landscape, we divided the machine learning (ML) and DL technology stack into nine layers, across services, training, platform, interface, and hardware.”

And in 2020, the layers could look quite different depending on the industry, applications, deployment, and uses.

2. The C-suite needs a trusted expert who can deploy AI to create new business opportunities

A lot of corporations fail to fully take advantage of artificial intelligence because the C-suite often doesn’t understand AI capabilities or the impact this new technology system can have on the business. You need to hire or consult with an expert who understands the technology and understands how to solve business problems with it. More importantly, during this time of COVID-19 crisis shutdowns, organizations should not let the C-suite’s lack of expertise in the computer science field hinder them from investing in big AI-driven changes that have huge potential upsides for business outcomes. For example, Andrew NG, Baidu’s Chief Scientist, adjunct professor at Stanford University, and a top AI expert, makes a case for the need for a Chief AI Officer. This is why it’s important that the AI expert has a strong academic background along with the business experience to solve business problems using AI.  

3. An expert CAIO can add a crucial perspective and guidance to the C-suite

AI and machine learning are changing the way companies optimize processes and even launch new products, but since it is still a relatively new field, I doubt your CTO or CEO has all the expertise or bandwidth needed to see new approaches or business opportunities that are available now because of AI advances. A smart CAIO will be able to advise on identifying business problems that could benefit from implementing an AI solution. Need an example? An AI software company called Clearview AI scrapped billions of photos from social media to build a facial recognition app that can ID anyone and is having wild success selling it to law enforcement agencies and billionaires.

4. Data is an asset that can create potential revenue streams

It is 2020. By now, companies should be are aware that their data is hugely valuable. I have written about it several times, including this piece titled What is Data Value and Should it Be Viewed as a Corporate Asset? According to a recent blog post by Cognizant, businesses are feeling the pressure to more fully embrace artificial intelligence, beginning with an upgraded data foundation.

Modern AI-ready Data Architecture has helped transform organizations and industries. Let’s look at the example of the personal-finance credit score company Credit Karma, which is now turning to machine learning to make sense of hundreds of billions of data points to deliver personalized insights and recommendations to individual members at scale. They are hoping a new ML-based service called “Stories” will shift the perception of Credit Karma beyond that of just a credit score app by delivering personalized insights and recommendations to its 100 million U.S. members that are connected to the company’s range of auxiliary services such as loan and credit offers.

However, many businesses are straining under the weight of an unprepared data foundation that has thus far limited the technology’s achievements. Between 60 percent and 73 percent of all data in an enterprise goes unused for analytics, according to Forrester Research. Of course, not all data is initially AI-ready (either poorly structured or incomplete) for deep learning training models. This means having a scalable data and analytics foundation, so businesses can treat data as an asset and support AI deep learning to deliver insights, operate with precision, and achieve the outcomes that drive competitiveness.

If your organization has a lot of data, and you want to create value from that data, one of the things you might consider is building up an AI team. Having a CAIO can marry the analytical and business skills required to supercharge your data monetization strategy.    

5. Having a Chief AI officer could be a good way to connect to the academic AI research community and recent graduates

Big tech companies such as Google and Facebook have recruited and hired leading academic AI researchers (which could be expensive, but we’ll get to that later), such as:

• Yann Lecun, the head of Facebook AI, a deep learning pioneer, also heads NYU’s Computational Intelligence, Learning, Vision, and Robotics (CILVR) group.
• Fei Fei Li, head of Stanford AI Lab (SAIL), also heads up Google Cloud AI.
• In 2018, Facebook raided CMU’s AI lab, which resulted in many professors and students joining Facebook part-time.

One of the reasons that the top tech companies do this (besides having doctorate-level researchers) is because it gives them direct access to AI graduate students. This, in turn, allows companies the ability to develop partnerships with leading AI labs to assist with solving complex business problems (real-world machine learning problems and applications). In exchange, this allows graduate students the ability to publish their research project results in open-source AI communities and journals, such as Google Research Fellows. Google’s research fellows help them fuel advancements, such as conducting fundamental research and influencing product development, which has the opportunity to impact technology used by billions of people every day.

Outsourced Virtual Chief AI Officer vs. a Full-Time In-House CAIO

Now that we’ve established that there is a demand or need for a Chief AI position, an article published in SearchCIO Techtarget titled “Should your company hire a Chief AI Officer?” noted that many IT executives at a recent deep learning summit all agreed that leading companies need strong executive support to take advantage of AI opportunities. Whether this is managed by a federated team or centralized office headed by a Chief AI Officer (CAIO) is dependent on the decision of the boards. The article mentions Pavan Arora, who was named Chief AI Officer at Aramark in May, following a three-year stint as Chief Data Officer and Director at IBM Watson.

He states, “Part of the reason [for having an AI Chief in the C-suite] is rebranding the power of a data, not just on governance but on the value data generates.” He continues, “If you want to turn data off from an expense into an asset, you need a CAIO.” Arora’s company Aramark uses AI to reduce waste within the food it delivers to hotels, hospitals, and prisons. As an example, he helped create a forecasting algorithm that uses historical data and the calendar to figure out how many burgers to defrost. The result was a significant reduction in food waste.

Does it make more sense to partner with a fractional outsourced Chief AI officer instead of hiring a full-time CAIO?  

Why do some companies look to major technology and management consulting partners such as Bain & Company, BCG, Deloitte, Accenture, Cognizant, IBM, etc.? Why has New York Governor Andrew Cuomo tapped high-powered McKinsey & Company consultants to develop a science-based plan for the safe economic reopening of NYC? Why do startups sometimes hire outsourced CFO consultants on a part-time or fractional basis instead of hiring a full-time CFO?

Many times, it comes down to the value and cost benefits. A full-time CFO can cost $200K plus possible stock options or equity shares, and maybe the startup does not need someone full-time at that moment but has a particular project such as getting their VC due diligence materials in order to raise Series A funding. So, for just a fraction of the cost of a full-time CFO, a startup can get flexible, part-time expertise from a CFO consulting firm on-demand.

The same goes for launching technology, digital transformations, ML, and AI projects as well.

According to reporting from Reuters, McKinsey & Company is producing models on coronavirus testing, infections, and other key data points that, along with other research and expert opinions, will help underpin decisions on how and when to reopen the region’s economy. 

Establishing a successful, digitally mature analytics function requires investment in talent, data, leadership, tools, platform, and culture. AI is far more specialized than companies expect, more like a neurosurgeon than a general practitioner. That means practitioners need to know where to apply it and what it’s good at (and not good at) to maximize results. And so, as one thinks about the possible role of a Chief AI Officer (CAIO) or partnering with an AI consultancy firm, will it become as necessary as the Chief Data Officer (CDO)? Florian Douetteau, the CEO of Dataiku, wrote a Medium article in 2019 titled Will Chief AI Officer (CAIO) Be the New C-Suite Kid on the Block? He stated, “Because AI in the future enterprise would be ubiquitous across roles and departments, it’s not immediately clear what exactly a CAIO would ultimately own or be responsible for day-to-day.”

He instead suggests that companies should think about how AI and data could be applied to automate structured, high-frequency tasks or processes. To unlock value, examine core business processes. What pays off most for a bank isn’t the same for a retailer or manufacturer. Focus is critical. Let’s dive into a few operational business functions that could be areas for a specific and useful AI initiative use case.  

AI and Human Resources: In the HR department of enterprises, an area for AI deployment might be to help create a hiring plan to expand a workforce (as well as an education plan for current employees), geared to an AI future first organization. This would entail retooling existing business knowledge with some AI skills, which would also mean finding the optimal blend of technical and non-technical profiles within the company.

It also means hiring for hyper-specialized roles specific to the industry or type of work the company is doing. For example, in banking, it might be finding a specialist to develop an artificial intelligence-driven machine learning solution to flag potential fraud by analyzing scanned images of handwritten checks. IBM is a great example of using AI solutions in an HR department to predict which employees will leave a job with 95 percent accuracy. Read IBM’s report on the business case for AI in HR.    

AI and Operations: In this scenario, the CAIO would be most responsible for the execution and coordination of efficient project delivery. That means a big focus on production and operationalization. In order to realize real business value from data projects, machine learning models must not sit on the shelf; they need to be operationalized. Operationalization simply means deploying a machine learning model for use across the organization. As Florian Douetteau mentions:

“One can reasonably imagine that in the enterprise of the future where it’s not just about one, two, or even three models, but 1,000 (or 10,000) models running at the same time, managing delivery could be the critical task for a C-level position.”

AI and Product: Imagine a company that is completely centered around building efficiency and cutting costs with AI. What does that leave for the CAIO? Invention. This version of the CAIO would be at the forefront of innovation, exploring potential new paths or uses for AI technologies that are maybe outside the scope of the company’s otherwise day-to-day operations.  

An in-house or outsourced CAIO could work with the CEO, CTO, and CIO to spearhead new creative solutions or improvements to existing products by applying machine learning or AI to all aspects of the business. Strategic initiatives could include: 

• Finding opportunities internally to improve the customer experience across a company’s products and solutions with machine learning.
• Evangelizing an AI-first approach to clients and developing new business partnerships within their machine learning initiatives.
• Reducing capital expenditures throughout the business by locating cost centers and minimizing their impact on machine learning.
• Building a cohesive Data Strategy that lacks silos and enables AI and machine learning into one cohesive blend of technologies to build new visionary insights.
• Locating/acquiring and developing strong Data Science and AI-specialized talent in the industry.

Conclusion

The current COVID-19 crisis is pushing companies to rapidly operate in new ways, and system resilience is being tested as never before. As businesses juggle a range of new systems, priorities, and challenges ― business continuity risks, sudden changes in volume, real-time decision-making, workforce productivity, security risks ― leaders must act quickly to address immediate system resilience issues and lay a foundation for the future.

One way is by automating routine tasks with human + machine models, where everyone is a knowledge worker. This can also help to serve businesses both now and in the future (to position them for growth post-COVID-19). 2020 is clearly an inflection point. It’s the start of a decade where digital technology is now expected to be embedded in every aspect of companies, governments, and people’s lives. Your ability to deliver on the promise of new innovative digital technologies may be a big deciding factor in what sets your company apart from your competition going forward. 

A recent Accenture report, (which surveyed 8,300 companies across 20 industries and 20 countries), found that only the leaders (the top 10 percent) feel they are realizing the full value from technology investments and are growing revenue at more than double (2x) the rate of laggards. Recently, CIO reported that a publicly-traded company Service Now is going all-in on AI and analytics in a concerted effort to broaden its base beyond its core market of IT service management. In the last few months, it made two AI company acquisitions to make it more of an AI/automation platform (adding predictive analytics and automated workflows) for businesses and hired its first Chief AI Officer Vijay Narayanan, who previously served as head of Data Science at Microsoft where he led their Advanced Technology Group in accelerating AI innovation throughout their entire portfolio.

They also partnered with Deloitte and Accenture to develop industry-specific solutions on their platform, beginning with applications for banking and telecommunications. So here’s the question I have for companies who are looking to accelerate their digital transformation to drive their business strategy using data, AI, cloud, and new innovative technologies — is it better to partner with an on-demand virtual Chief AI Officer consultant or hire a full-time in-house one?