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Do enterprises need a chief AI officer (CAIO) to shepherd their forward progress in a world of increasing automation? Sandy Carter, tech veteran and author of Extreme Innovation, said that “new roles like the CAIO will become essential for leveraging the big data coming into companies from all angles.” Andrew Ng, Stanford computer science professor and cofounder of online learning provider Coursera, has also been a proponent of assigning an executive to transform data into value by making sure AI is applied across all data silos. But with the role still nascent, many experts continue to debate this topic. A quick public search on LinkedIn shows that only a dozen professionals currently hold the title.

Every leader must understand how AI will affect an organization, both internally from an organizational standpoint and monetarily from a financial performance standpoint. AI will impact every part of a company’s business, creating unprecedented ways to innovate and evolve, according to a 2017 PwC study. Given how complex AI and data management can be, specialist leaders with the right expertise will be needed to determine the potential impact of new technologies and how to incorporate them.

A CAIO could be that specialist leader. They would have the following responsibilities:

  • Collecting and centralizing data across multiple departmental silos in an enterprise.
  • Transforming data into value with AI across all the areas possible.
  • Being a dedicated agent for attracting and retaining AI talent.
  • Working with the rest of the C-suite and senior leadership to manage organizational and infrastructural changes that may follow.

That said, major technology initiatives are successful only when an organization deeply understands and prioritizes the actual business problems and goals it wants to solve with emerging solutions like AI and automation. “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,” says Kristian J. Hammond, chief scientist at natural language generation maker Narrative Science.


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An organization must also have the infrastructure, data, budget, and talent to enable a CAIO to execute their AI vision, with the change led by a collaborative leadership team, not just by a single individual. Currently, business line owners with enough influence and resources are the primary drivers for these initiatives and innovation efforts, but the effects are siloed to their own team and product line. According to a recent McKinsey study of over 3,000 executives internationally, successful AI implementations have strong executive leadership support across the board, with nearly twice the C-suite support as companies that have not integrated AI technology.

Before committing to hire a CAIO, answer these questions to gauge your AI readiness:

1. Where does your company currently stand with data and analytics?

Oftentimes, business leaders think their company’s data quality is fine when it is actually inconsistent, incomplete, biased, distributed across different systems, and even incorrect. “Garbage in, garbage out” is especially true with data, which is the foundation of your company’s strategy, initiatives, and priorities. The quality of your company’s data and analytics infrastructure, as well as the culture around analytics, is critical for any AI initiative.

2. Does the executive team have a true understanding of where the technology is today?

Having even a general grasp of the capabilities, potential gains, and how it could be relevant to your business is a competitive advantage. There are constantly new developments in this dynamic field, and if the executive team is behind on understanding the technology today, it’s going to be even more difficult to get your leadership on the same page to set and achieve realistic goals and milestones.

3. Are they committed to AI initiatives with sufficient resources, both financial and human?

Implementing enterprise-wide transformation is significantly harder than most non-technical executives estimate. The organization needs to be willing to experiment and have the risk appetite to do so. Several business leaders mentioned running 20 to 30 experiments to ensure a few would deliver. Amazon runs hundreds of experiments and its handful of winners — AWS, Prime, Alexa — pays for them all.

4. Does the company have access to domain experts, and are they willing to invest in the right team?

Having the right team, from the executive to the engineering level, is the foundation for solving all the other challenges with enterprise AI implementation. This can be done not only through direct hiring, but also through partnerships, joint programs, investments, and even acquisitions. If the right team and people are involved, the right things can then be done for the organization.

It will be natural for the CAIO to search for areas of the organization to apply AI, but those applications would effectively be premature if you haven’t properly assessed your current state of the organization and established strong prerequisites for success.

As Neil Jacobstein from the Singularity University think tank advises, “It’s very important to match the speed of the technology with the nimbleness of the teams. And having a centralized AI guru at the top, where everybody has to ask questions of that person, is unlikely to be as fast and effective as having a decentralized organization with powerful teams.”

Marlene Jia is the chief revenue officer at Topbots, a strategy and research firm in applied artificial intelligence and machine learning.

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