Metrics to Impact: How HR Can Use AI to Speak the Board's Language

As CHROs take on a larger role in the boardroom, their ability to communicate workforce strategy with clarity has become increasingly important. Nearly 70% of companies have seen greater CHRO involvement with boards in the past three years according to a 2025 Conference Board survey, and it's up to HR leaders to efficiently translate workforce data into impact.

Across the 16 HRX dimensions — from recruitment and compliance to talent management and leadership development — AI is increasingly augmenting the HR function. To demonstrate how HR professionals are putting AI into practice, SHRM reached out to members to share their experiences. This installment of our AI Use Case series explores how one HR leader is using AI to communicate more effectively with executive leadership and the board.

This article is an example of the Talent-Risk Briefings for the Board use case. Discover 137 other AI use cases by visiting SHRM’s AI Field Manual for Employers: 138 Use Cases for AI at Work.

Speaking the Board's Language

Presenting workforce data to a board of directors often requires a different approach than presenting to employees or managers. Board members don't want operational detail — they want strategic insight, concise recommendations, and a clear understanding of business impact. 

For Kim Boulahanis, vice president of HR at ContinuumCloud, a behavioral healthcare software company, AI has become a useful partner in preparing for those conversations.

Her company recently implemented an enterprise agreement with Claude, giving employees access to an approved AI platform. While Boulahanis initially used the technology for tactical work such as improving job descriptions and developing interview questions, she quickly realized it could help elevate another aspect of her role: executive communication.  

Here’s how Boulahanis used AI as a strategic partner as she prepared board presentations.

The AI Use Case: Talent-Risk Briefings for the Board 

Rather than asking Claude to simply generate presentation slides, Boulahanis starts by giving the AI assistant the raw material and the reasoning behind it. She shares workforce statistics — including turnover rates, retention trends, and other workforce metrics — along with her own interpretation of what the numbers mean and the story she wants the presentation to tell.

Context is key. Before asking Claude to analyze or present the information, she explains who will be in the room, what takeaway she wants the audience to leave with, and any tension or concern the presentation needs to address. 

"What are the best areas for me to focus on in a board presentation?" she asks Claude. "What matters? Is it attrition? Is it statistics on turnover by department or by manager? Do they care about that level of detail? Are there recommendations that I should make?"

The process has also surfaced ideas she might not have considered on her own. The first time she shared turnover and retention metrics, for example, Claude suggested different ways to visualize the changes. The recommendation prompted Boulahanis to rethink not just how she displayed the numbers, but how she communicated the significance of the change itself. 

Claude can also help her anticipate the conversation that will follow. As she works through the data, the tool sometimes raises questions about the information or its implications, prompting Boulahanis to consider what the board might ask before she enters the room.

That preparation has changed the nature of the board conversation. Rather than spending as much time answering questions that clarify the underlying data, Boulahanis says the discussion increasingly moves toward strategic vision — what the numbers mean for where the organization is headed. 

The process has also reduced the time she spends on presentation mechanics. Claude can build templates and tailor how information is presented based on the audience, freeing Boulahanis to spend more of her preparation time on the narrative and strategy behind the presentation rather than formatting slides.

She uses a similar approach when preparing materials for executive leadership, tailoring the information and messaging to the people in the room. It’s a process that has allowed Boulahanis to augment her judgment or strategic perspective rather than completely replace it. 

What Worked

Boulahanis’ approach has reduced the time she spends on presentation mechanics, she said. Claude can build templates and tailor how information is presented based on the audience, freeing Boulahanis to spend more of her preparation time on the narrative and strategy behind the presentation rather than formatting slides. 

Claude also helped her organize complex workforce information into concise executive narratives — an important advantage when communicating with board members who expect clear business insights rather than lengthy HR reports. Instead of second-guessing whether she's highlighting the workforce metrics executives care about most, Boulahanis used AI to pressure-test her own thinking before presenting.

"I put in what I want to convey along with my data, and then I get very succinct slides that they absolutely love,” she explained, adding that this process has helped her think more strategically about the story the data is telling. She spends less time refining presentation language and more time focusing on the strategic decisions behind the data. 

What Didn't Work

Boulahanis says one of the biggest lessons she's learned is that AI doesn't automatically understand an organization's context. Because large language models draw from a broad range of organizations, their recommendations may reflect priorities that don't fit into a privately held healthcare software company. 

When Claude suggests something that doesn't align with her business, she doesn't simply accept the recommendation or abandon the tool. Instead, she keeps the conversation going.

"I don't necessarily see that point of view," she tells Claude before explaining why her organization's situation or scenario is different. That additional context often leads to more useful recommendations. 

"Sometimes it's nice to be directionally correct, and then you have to adapt based on the environment and what you're experiencing," she said. 

Human Judgment Still Leads Strategy

Although Boulahanis has embraced AI, she draws a clear line between assistance and decision-making. 

"I like to use the phrase 'AI enablement,' not just AI," she said. "I don't believe in AI replacing people. I think it's a wonderful enhancement to the quality of things that you can deliver." 

At the same time, Boulahanis emphasized that successful adoption requires more than enthusiasm. Organizations need governance and secure platforms before AI becomes part of everyday work. 

"You should start with a strategy," she said. "But you also need the proper policies in place and training." 

Advice for HR Leaders

For HR professionals interested in using AI to improve executive communication, Boulahanis offered several lessons:

• Start with the business question. Use AI to explore what executives or board members are likely to care about — not simply to create slides.
• Provide organizational context. Explain your company's priorities, challenges, and goals before asking AI for recommendations.
• Challenge AI's assumptions. If a recommendation doesn't fit your organization, continue the conversation rather than accepting the first answer.
• Let AI refine the message — not determine the strategy. HR leaders still provide the judgment, business knowledge, and organizational context that AI lacks.
• Build governance alongside adoption. AI should be introduced with clear policies, training, and executive alignment from the outset. 

For Boulahanis, the technology's greatest value is helping HR communicate workforce priorities in a way that resonates with the people making an organization's most important decisions.