How HR Leads in an AI-First Company

AI has officially moved past experimentation.

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Over the last year, most organizations focused on piloting tools, running isolated use cases, and figuring out what AI could do. Now, the conversation has shifted. The real question is no longer “Should we use AI?” It’s “How do we embed AI into our business strategy in a way that drives measurable impact?”

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In this episode of Pulse, Logan Rivenes sits down with Melissa Lemberg, Vice President of Digital Transformation and AI Strategy at LogicMonitor, to explore what it actually means to become an AI-first company — and why HR must be at the center of that transformation.

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Melissa brings nearly 30 years of experience leading human-centered transformation across complex organizations, including Fortune 100 companies. Her perspective blends digital strategy, operational excellence, and change management — all anchored in measurable business outcomes.

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This conversation is especially relevant for HR leaders in private equity-backed companies, where expectations are high, value creation timelines are tight, and every initiative must tie back to metrics that matter.

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AI Strategy Is Business Strategy

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One of the clearest themes from the discussion is this: AI should not exist as a standalone strategy.

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Melissa emphasizes that AI initiatives must be grounded in company-wide metrics, priorities, and values. Instead of building an “AI strategy” in isolation, leadership teams should ask:

• What are the metrics that matter most to our company?
• What are our growth and efficiency targets?
• Where can AI act as a lever to move those outcomes?

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In PE-backed environments, that often means focusing on metrics like revenue per employee, EBITDA, product velocity, or go-to-market efficiency.

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Rather than adopting AI for novelty or experimentation, the question becomes: Where can we insert the right tools, processes, and methodologies to accelerate performance in ways that directly impact those business metrics?

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For example:

• In go-to-market teams, AI can support forecasting, account planning, relationship mapping, and cross-sell or upsell analysis.
• In product and engineering, AI-assisted coding tools and security automation can accelerate development cycles and free up time for innovation.
• In customer-facing roles, AI can reduce manual tasks so employees spend more time building trust and strengthening relationships.

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The point is not to automate everything. The point is to align AI adoption with the outcomes the business is already accountable for delivering.

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Human-in-the-Loop Is Non-Negotiable

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A defining principle in Melissa’s approach is “human-in-the-loop” AI.

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While automation and efficiency are powerful, not every task should be handed over to technology. Leadership teams must clearly distinguish between:

• Repetitive, manual, low-value tasks that can be automated.
• Human-driven activities that require judgment, trust, empathy, and strategic thinking.

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For instance, an account executive can use AI to surface insights, prepare talking points, and analyze data — but relationship-building, nuanced conversations, and trust remain human strengths.

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This requires a thoughtful review of roles and workflows:

• What tasks are slowing people down?
• Where are friction points?
• Where are systems breaking down?
• Where are employees spending time that doesn’t require uniquely human skills?

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Melissa notes that effective transformation starts with observation and dialogue — conducting stakeholder interviews, mapping journeys, understanding emotional friction, and identifying where automation meaningfully improves outcomes.

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The goal is not “lift and shift” automation — simply taking existing processes and digitizing them. Instead, it’s about redesigning workflows around outcomes and then enhancing them with AI.

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Big T vs. Little t Transformation

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Melissa introduces an important distinction: transformation with a “little t” versus a “big T.”

• Little t transformation might mean introducing a new tool, upgrading a workflow, or automating a specific process.
• Big T transformation involves rethinking core systems, redefining priorities, or fundamentally changing how work gets done across the enterprise.

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AI can power both.

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But leaders must be clear about which type of transformation they’re pursuing. Small improvements can create meaningful gains. Large-scale change requires more intentional planning, communication, and governance.

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For HR leaders, understanding the scope of change determines how they approach enablement, communication, and adoption.

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Measuring AI Beyond Headcount

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There’s a common narrative that AI impact is measured primarily through headcount reduction. Melissa pushes back on that simplification.

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Instead, she emphasizes the importance of benchmarks.

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Before implementing AI:

• Establish where you are today.
• Understand current productivity, cycle times, and performance metrics.
• Define the outcomes you want to improve.

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Then, measure the impact over time.

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For engineering teams, that might mean tracking how coding tools change velocity. For HR, it could mean measuring improvements in time-to-hire or recruiter productivity when AI assists with sourcing and screening — always with human oversight.

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The key is ongoing tracking and accountability. AI investments must have clear ROI cases behind them. Adoption alone is not success. Impact is.

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Why HR Must Lead

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AI introduces uncertainty. Employees worry about job displacement, skill relevance, and shifting expectations.

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That’s where HR becomes central.

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Melissa argues that strong engagement from HR and executive leadership — including CEO and board-level alignment — is essential to building trust and cultural clarity around AI.

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HR plays a critical role in:

• Defining what “AI-first” actually means.
• Leading transparent communication.
• Outlining expectations by role and level.
• Designing enablement strategies.
• Aligning incentives and performance assessments.

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Early expectations might focus on curiosity and experimentation. Over time, expectations become more prescriptive: adoption levels, workflow integration, and measurable efficiency gains.

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But employees need clarity. They need to understand:

• What does success look like in my role?
• How will AI impact my responsibilities?
• How am I being supported?

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Without communication, fear fills the gap.

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Building an AI Champion Network

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One of the most compelling parts of the conversation is Melissa’s description of an AI champion network.

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Rather than relying solely on IT or a centralized transformation team, LogicMonitor cultivated a cross-functional group of AI champions embedded within business units.

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These champions:

• Identify automation opportunities within their teams.
• Propose use cases tied to measurable outcomes.
• Encourage experimentation.
• Share best practices.
• Help scale AI fluency across the organization.

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Importantly, they are required to measure business impact. Proposals must answer: Why are we building this? What outcome will it drive?

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This approach extends IT’s reach without overwhelming it. IT remains critical for governance, security, and infrastructure — but champions bring domain expertise and proximity to real workflows.

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The result is structured experimentation rather than chaotic tool usage.

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Governance and Responsible AI

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Adoption without governance creates risk.

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Melissa highlights the importance of partnership with IT to:

• Ensure secure data practices.
• Follow security protocols.
• Establish AI governance models.
• Evaluate ROI before building new agents or tools.

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Responsible AI is not optional. It is foundational.

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Practical Advice for HR Leaders

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For HR leaders without a dedicated transformation team, Melissa offers clear starting points:

1. Understand the business deeply.
2. Know which metrics matter most.
3. Create an AI suitability heat map.
4. Identify where AI could most meaningfully impact performance.
5. Partner with business leaders.
6. Align on specific experiments tied to measurable outcomes.
7. Start small but strategic.
8. For example, use AI in talent acquisition to improve time-to-hire — always with a human in the loop.
9. Communicate expectations clearly.
10. Employees need to know what good looks like.
11. Invest in enablement.
12. Teach people how to use tools effectively. Curiosity alone is not enough.

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From Adoption to Impact

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LogicMonitor saw high AI adoption rates early on by giving employees access to tools and encouraging experimentation.

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But adoption is just the beginning.

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The next phase is articulation:

• How should tools be used?
• At what stage of the process?
• For what outcomes?
• Against which metrics?

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High adoption without alignment does not create business impact. Structured, measurable adoption does.

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The Next 12–18 Months of AI at Work

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Looking ahead, Melissa predicts that AI will become embedded into every role and function.

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The “human plus digital” model will become more prescriptive. Leaders, managers, and individual contributors will each have clearer expectations around how AI integrates into their work.

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Companies will refine how AI drives metrics at the function level, and how those metrics ladder up to enterprise goals.

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AI is not a temporary wave. It is becoming a core component of digital transformation.

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Final Takeaway

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AI is not about replacing people.

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It is about equipping people to be smarter, faster, and more effective — while aligning their work to measurable business outcomes.

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For HR leaders, the opportunity is enormous.

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By understanding the business, leading transparent communication, building enablement frameworks, and partnering across leadership and IT, HR can move from reactive support to strategic leadership in an AI-first company.

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AI is here to stay. The question is not whether HR will be involved.

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It’s whether HR will lead.