HR & Talent Insights from our NYC Conference

I hope you're doing well.  We held our first user conference in NYC, and about 60 people from our East Coast user community attended.  The user community represented industry diversity across Technology, Insurance, Healthcare, Biopharma, Fintech, Banks, Financial Institutions, Telecom, Petrochemicals, and Retail. We packed a lot of sessions, presentations, and panels into the day. We wanted it to be a learning day rather than a typical conference. All the attendees said the discussions were useful and practical. Here are my key learnings from the conference across the themes.  We also released an innovative Site Selection Feature in the platform (available for you to test in your platform)

Work Redesign (4 key learnings)

• AI value shows up only when work is decomposed into tasks and reassembled intentionally into workflows. The real leverage is at the task and workflow levels.
• Administrative friction is the fastest place to start. Removing burden (especially in complex environments like healthcare) unlocks capacity without compromising core judgment work.
• Redesign must focus on enterprise flow, not individual productivity. Isolated automation does little unless workflows across teams are stitched together.
• Governance is not optional. In high-stakes work, “just because we can automate” is not the right lens—human oversight and guardrails define responsible redesign.

Talent Acquisition (4 key learnings)

• AI accelerates throughput, but recruiting remains human-led. Screening and shortlisting can scale with AI, but final decisions must preserve human judgment.
• Candidate authenticity is becoming a signal challenge. As candidates increasingly use AI tools, TA needs better validation mechanisms without over-automating.
• Transparency builds trust. Clear communication about AI use and preserving a human final interaction improves the candidate experience.
• Dual-speed TA is emerging. Some roles require tactical efficiency, others require strategic advisory—test-and-learn approaches are outperforming rigid models.

Strategic Workforce Planning (4 key learnings)

• Planning must shift from headcount to capability modeling. AI impact lies beneath the surface at the task and skill levels, not just in job categories.
• Early signals matter. Increasing skills density, more IC hiring, and slowing hiring in highly automatable areas are directional indicators.
• Experimentation is part of planning. “No experiment, no readiness”—pilot programs are the new forecasting tool.
• Long-term pipelines beat reactive hiring. University partnerships and ecosystem investments are becoming core SWP strategies.

Reskilling & Development (4 key learnings)

• Skills architecture is now a continuous infrastructure. It requires ongoing validation, governance, and refinement—not a one-time project.
• Clarity on what counts as a skill is foundational. Without shared definitions, learning investments scatter.
• Internal validation strengthens credibility. Surveys and real job-to-skill mapping exercises anchor models in lived work.
• Future readiness is a mindset shift. Critical thinking, learning agility, and the ability to review AI outputs are becoming baseline capabilities.

Hope these takeaways add value.  We hope to have more user conferences across the regions, and we will keep you posted