It has been said that this new wave of exponential technologies will threaten a lot of jobs, both blue and white-collar ones. But if on one hand many roles will disappear, on the other hand in the very short-term we are observing new people coming out from the crowd to lead this revolution and set the pace.
These are the people who really understand both the technicalities of the problems as well as have a clear view of the business implications of the new technologies and can easily plan how to embed those new capabilities in enterprise contexts.
Hence, I am going to briefly present three of them, i.e., the Chief Data Officer (CDO), the Chief Artificial Intelligence Officer (CAIO) and the Chief Robotics Officer (CRO). Sad to be said, I never heard about a ‘Chief of Data Science’, but for some strange reasons, the role is usually called either ‘Head of Data Science’ or ‘Chief Analytics Officer’ (as if data scientist won’t deserve someone at C-level to lead their efforts).
Let’s see then who they are and what they would be useful for.
I. The Chief Data Officer (CDO)
Apparently, it is a new role born in a lighter form straight after the financial crisis springing from the need to have a central figure to deal with technology, regulation and reporting.
Therefore, the CDO is basically the guy who acts as a liaison between the CTO (the tech guy) and the CAO/Head of Data Science (data guy) and takes care of data quality and data management.
Actually, its final goal is to guarantee that everyone can get access to the right data in virtually no time.
In that sense, a CDO is the guy in charge of ‘democratizing data’ within the company.
It is not a static role, and it evolved from simply being a facilitator to being a data governor, with the tasks of defining data management policies and business priorities, shaping not only the data strategy, but also the frameworks, procedures, and tools. In other words, he is a kind of ‘Chief of Data Engineers’ (if we agree on the distinctions between data scientists, who actually deal with modelling, and data engineers, who deal with data preparation and data flow).
“The difference between a CIO and CDO (apart from the words data and information…) is best described using the bucket and water analogy. The CIO is responsible for the bucket, ensuring that it is complete without any holes in it, the bucket is the right size with just a little bit of spare room but not too much and its all in a safe place.
The CDO is responsible for the liquid you put in the bucket, ensuring that it is the right liquid, the right amount and that’s not contaminated. The CDO is also responsible for what happens to the liquid, and making the clean vital liquid is available for the business to slake its thirst.” (Caroline Carruthers, Chief Data Officer Network Rail, and Peter Jackson, Head of Data Southern Water)
I don’t want to get into what a Chief Information Officer does and how he differs from a CDO, a CTO, a CRO, or any other roles, but if you want to know more I highly recommend Julie Steele’s free ebook (see here).
Interestingly enough, the role of the CDO as we described it is both vertical and horizontal. It spans indeed across the entire organization even though the CDO still needs to report to someone else in the organizational chart. Who the CDO reports to will be largely determined by the organization he is operating in. Furthermore, it is also relevant to highlight that a CDO can be found more likely in larger organizations rather than small startups. The latter type is indeed usually set up to be data-driven (with a forward-looking approach) and therefore the CDO function is already embedded in the role who designs the technological infrastructure/data pipeline.
It is also true that not every company has a CDO, so how do you decide to eventually get one? Well, simply out of internal necessity, strict incoming regulation, and because all your business intelligence projects are failing because of data issues. If you have any of these problems, you might need someone who pushes the “fail-fast” principle as the data approach to be adopted throughout the entire organization, who considers data as a company asset and wants to set the fundamentals to allow fast trial and error experimentations. And above all, someone who is centrally liable and accountable for anything about data.
A CDO is then the end-to-end data workflow responsible and it oversees the entire data value chain.
Finally, if the CDO will do his job in a proper way, you’ll be able to see two different outcomes: first of all, the board will stop asking for quality data and will have clear in mind what every team is doing. Second, and most importantly, a good CDO aims to create an organization where a CDO has no reasons to exist.
It is counterintuitive, but basically, a CDO will do a great job when the company won’t need a CDO anymore because every line of business will be responsible and liable for their own data.
A good CDO aims to create an organization where a CDO has no reasons to exist.
In order to reach his final goal, he needs to prove from the beginning that not investing in higher data quality and frictionless data transfer might be a source of inefficiency in business operations, resulting in non-optimized IT operations and making compliance as well as analytics much less effective.
II. The Chief Artificial Intelligence Officer (CAIO)
If the CDO is somehow an already consolidated role, the CAIO is nothing more than a mere industry hypothesis (not sure I have seen one yet, although the strong ongoing discussions between AI experts and sector players — see here and here for two opposite views on the topic). Moreover, the creation of this new role highlights the emergence of two different schools of thought of enterprise AI, i.e., centralized vs decentralized AI implementation, and clear cost-benefit analysis to understand which approach will work better is still missing.
My two cents are that elevating AI to be represented at the board level means to really become an AI-driven company and embed AI into every product and process within your organization — and I bet not everyone is ready for that.
So, let’s try to sketch at a glance the most common themes to consider when talking about a CAIO:
- Responsibilities (what he does): a CAIO is someone who should be able to connect the dots and apply AI across data and functional silos (this is Andrew Ng’s view, by the way). If you also want to have a deeper look at what a CAIO job description would look like, check out here the article by Tarun Gangwani;
- Relevance (should you hire a CAIO?): you only need to do it if you understand that AI is no longer a competitive advantage to your business but rather a part of your core product and business processes;
- Skills (how do you pick the right guy?): first and more important, a CAIO has to be a ‘guiding light’ within the AI community because he will be one of your decisive assets to win the AI talent war. This means that he needs to be highly respected and trusted, which is something that comes only with a strong understanding of foundational technologies and data infrastructure. Finally, being a cross-function activity, he needs to have the right balance between willingness to risk and experiment to foster innovation and attention to product and company needs (he needs to support different lines of business);
- Risks (is a smart move hiring a CAIO?): there are two main risks, which are i) the misalignment between technology and business focus (you tend to put more attention on technology rather than business needs), and ii) every problem will be tackled with AI tools, which might not be that efficient (this type of guys are super trained and will be highly paid, so it is natural they will try to apply AI to everything).
Where do I stand on that? Well, my view is that a CAIO is something which makes sense, even though only temporarily. It is an essential position to allow a smooth transition for companies who strive for becoming AI-driven firms, but I don’t see the role to be any different from what a smart tech CEO of the future should do (of course, supported by the right lower management team). However, for the next decade having a centralized function with the task of using AI to support the business lines (50% of the time) and foster innovation internally (50% of the time) it sounds extremely appealing to me.
In spite of all the predictions I can make, the reality is that the relevance of a CAIO will be determined by how we will end up approaching AI, i.e., whether it will be eventually considered a mere instrument (AI-as-a-tool) or rather a proper business unit (AI-as-a-function).
III. The Chief Robotics Officer (CRO)
We moved from the CDO role, which has been around for a few years now, to the CAIO one, which is close to being embedded in organizational charts. But the Chief Robotics Officer is a completely different story.
Even if someone is speaking about the importance of it (check out this report if you like), it is really not clear what his tasks would be and what kind of benefits would bring to a company, and envisaging this role requires a huge leap of imagination and optimism about the future of work (and business).
In a few words, what a CRO will be supposed to take care of is managing the automated workforce of the company. To use Gartner’s words, ‘he will oversee the blending of human and robotic workers’. He will be responsible for the overall automatization of workflows and to integrate them smoothly into the normal design process and daily activities.
I am not sure I get the importance of this holistic approach to enterprise automation, although I recognize the relevance of having a central figure who will actively keep track and communicate to employees all the changes made in transforming a manual activity/process into an automated one.
Another interesting point is who the CRO will report to, which is of course shaped by his real functions and goals. If robotics is deeply rooted in the company and allows to create or access new markets, a CRO might directly report to the CEO. If his goal is instead to automatize internal processes to achieve higher efficiency, he will likely report to the COO or to a strategic CxO (varying on industry and vertical).
My hypothesis is that this is going to be a strategic role (and not a technical one, as you might infer from the name) which, as the CAIO, might have a positive impact in the short term (especially in managing the costs of adopting early robotics technologies) but no reason to exist in the longer term. It is easier to think about it in physical product industries rather than digital products or services companies, but automation will likely happen in a faster way in the latter, so we will end up having a Chief of Physical Robotics Officer (to manage the supply chain workflow) as well as a Chief of Digital Robotics Officer (to manage instead the automation of processes and activities).