Feature
eBook
Designing an AI-Native Contact Center
Having someone in the role of a chief AI officer (CAIO) — someone who can mobilize cross-functional leadership, integrate emerging tech into core workflows and hard-wire responsible governance — will be critical for building an AI-enabled business.
When companies hire a CAIO, the primary goal is to drive adoption of AI across the entire organization in a way that supports business outcomes, whether that’s increasing revenue or achieving operational efficiency. It's a cross-functional role spanning business strategy, project management, orchestration at scale and technical implementation, and the CAIO collaborates closely with the chief technology officer (CTO) and chief data officer (CDO).
“Bringing in a CAIO can help accelerate AI adoption, but I encourage CEOs to first consider if what they really need is to bring more AI skills to the existing c-suite,” said Daniele Grassi, CEO of General Assembly.
Table of Contents
- Is the Chief AI Officer Role Permanent — or a Transitional Fix?
- What a CAIO Is Actually Responsible For
- Which Industries Benefit Most From a CAIO
- How to Measure Whether a Chief AI Officer Is Working
- The Biggest Mistakes Companies Make When Appointing a CAIO
Is the Chief AI Officer Role Permanent — or a Transitional Fix?
According to Grassi, the role of a chief AI officer is likely temporary; the technology is very new and companies are just starting to figure out how it works for them. His company's own research found that today, less than half of executives say their companies offer leadership-specific AI training.
“In the long term, the role of the CAIO might not exist,” he cautioned.
Executives need to ensure they're ready to continue driving adoption of AI, align AI investments with business outcomes and make decisions that help the organization prepare for the future. “The only way to do so," said Grassi, "is to upskill."
Related Article: Why AI Training Is Failing — and What Hands-On Learning Gets Right
What a CAIO Is Actually Responsible For
The core remit of the chief AI officer includes developing AI roadmaps, scaling and governance, said Amy Loomis, group vice president, workplace solutions at IDC.
- Own the enterprise AI strategy and roadmap and be accountable for value realization and risk management across the portfolio of AI use cases.
- Scale AI beyond pilots by aligning business units, data/machine learning platforms, model lifecycle and change management — backed by clear authority and budget at the C-suite level when AI is a competitive lever.
- Embed responsible/secure AI governance — transparency/explainability, security & resilience, compliance & privacy and human-in-the-loop oversight — in every initiative
“The CAIO is the business owner for AI-enabled value, orchestrating cross-functional execution and governance,” Loomis explained. “Some organizations may keep AI under CIO/CTO, but those pursuing AI for market differentiation should elevate a dedicated CAIO.”
Other Common Questions About CAIOs
A CAIO concentrates accountability in a single executive role, whereas steering committees and CoEs distribute responsibility across functions. Committees are effective for coordination, but they rarely have the authority to resolve trade-offs between speed, risk and value at scale.
Direct reporting to the CEO is often critical, particularly in organizations where AI cuts across product, operations, legal and data teams. When the role reports lower in the hierarchy, AI decisions tend to fragment or stall.
At minimum, the CAIO should influence:
- AI platform selection
- Vendor strategy
- Funding for cross-functional initiatives
Without budget visibility or control, the role often becomes advisory rather than operational.
The CAIO's responsibilities may eventually dissolve into existing roles, similar to how digital leadership evolved over time. The long-term value of the role lies in accelerating that transition, not preserving the title itself.
Which Industries Benefit Most From a CAIO
Virtually all industries can benefit from having a CAIO, according to Josh Meier, senior generative AI lab author at Pluralsight.
- For product-based companies, like Amazon, AI it critical for inventory management, logistics, quality control and, soon, likely for warehouse automated.
- For service-based companies, AI adds value in areas such as document management, sales and revenue forecasting and sentiment analysis.
“While the applications differ, the strategic advantage remains consistent across both models,” said.
Digital native businesses (DNBs), added Loomis, are at the forefront of GenAI and can benefit most from a chief AI officer who can convert rapid experimentation into durable product and revenue impacts. In regulated and data-intensive sectors, she explained — like finance, healthcare, telecom — unified governance, formal policies and oversight to manage risk and compliance are a must. “A CAIO is a natural locus for this accountability.”
Organizations that may gain the most value from a CAIO are those with complex operations, like healthcare or manufacturing, noted Grassi. “A CAIO would also be valuable for a consulting or professional services firm looking to productize AI expertise or launch new revenue streams driven by AI."
How to Measure Whether a Chief AI Officer Is Working
Both Loomis and Grassi agreed that the chief AI officer's success should be measured in terms of business outcomes:
- Financial Impact: Revenue from AI-enhanced products and services, conversion/uplift, cost-to-serve reductions, time-to-market acceleration.
- Adoption & Scale: Percentage of workforce using governed AI, number of use cases in production with SLAs.
- Speed to Expertise & Onboarding: Use of AI tools to ensure effective onboarding of new hires into newly defined AI roles as well as upskilling existing employees via embedded AI capabilities.
- Risk & Trust: Policy coverage, model incidents, audit findings and compliance posture against your governance framework.
"Implementing AI isn’t a one-and-done project; it requires constant upskilling, technology upgrades and cross-functional collaboration,” said Grassi.
Related Article: Overwhelmed By AI? How to Make AI Training Practical & Impactful
The Biggest Mistakes Companies Make When Appointing a CAIO
The most common mistake, said Meier, is appointing a chief AI officer without granting real authority or budget control.
“To be effective, the CAIO must have the ability to evaluate, oversee and regulate all AI initiatives within the organization,” he explained. Their goals should be aligned with business outcomes while prioritizing transparency, bias mitigation and compliance. “Without these powers, the role risks becoming symbolic rather than strategic."
From Grassi’s perspective, the biggest pitfall in appointing a CAIO is that it isolates AI into a standalone function that only creates more bottlenecks and red tape. “This is why the CAIO must report directly to the CEO and have the authority and mandate to drive true transformation."
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Having unrealistic expectations based on where your organization stands from a data maturity, talent and foundational infrastructure standpoint is also a common pitfall, he added. “A CAIO can’t make magic when you don’t have the right foundation in place."