2027 Agenda

• Designing practical skills frameworks that align with business strategy
• Ensuring adoption across HR, talent, and learning functions
• Integrating skills architecture into talent marketplaces and development programs
• Overcoming organizational resistance and complexity
• Driving measurable workforce agility through skills-based thinking

• Aligning leadership development with digital and AI transformation
• Preparing executives to make strategic decisions in complex environments
• Incorporating new competencies such as data literacy and digital fluency
• Integrating real-world business challenges into leadership programs
• Measuring the impact of leadership development on strategic outcomes

In December 2025, Mars closed the largest packaged-food combination in a decade, bringing two companies with distinct operating rhythms, leadership frameworks, and learning infrastructures under one roof. Thirteen months in, the learning function sits at the center of a question no integration playbook fully answers: culture is what leadership decks promise will carry over, and learning is the only mechanism that actually moves it. This session is a candid account of what has worked, what hasn’t, and what a 115-year-old principles-based culture can and can’t teach to people who didn’t grow up in it.

Session Details

• Sequencing capability building against integration milestones when two organizations run on different cadences
• Distinguishing what transfers through formal learning, what only transfers through managers, and what doesn’t transfer at all
• Consolidating two learning architectures — competing frameworks, overlapping vendors, incompatible data — without stalling delivery
• Deciding what to standardize globally and where established local practice should be left intact
• What he would sequence differently starting over

• Applying experimentation frameworks to evaluate learning interventions
• Testing different content formats and delivery models
• Using data to continuously improve program effectiveness
• Balancing experimentation with operational scalability
• Building a culture of evidence-based learning design

• Leveraging AI to deliver adaptive, personalized learning journeys
• Using real-time skills data to guide employee development paths
• Integrating learning recommendations directly into the flow of work
• Balancing automation with human oversight and coaching
• Measuring engagement and capability improvement at scale

• Combining traditional executive coaching with AI-powered insights
• Expanding coaching access to mid-level managers and emerging leaders
• Leveraging digital tools to deliver continuous feedback and development
• Balancing technology with the human element of leadership growth
• Evaluating effectiveness through behavioral and performance outcomes

• Embedding learning directly within workflows and productivity tools
• Delivering just-in-time knowledge and support for employees
• Reducing time away from work while improving capability development
• Integrating performance support with learning ecosystems
• Measuring the impact on productivity and operational efficiency

Most analytics conversations assume a dedicated data team, mature tooling, and budget to match. For mid-sized organizations, the reality is leaner: scattered data, limited headcount, and the same pressure to prove impact. This panel brings together L&D leaders building credible, data-informed practices without enterprise-scale infrastructure — and shows what’s actually achievable with the data you already have.

• Identifying skill gaps and development needs without a formal people-analytics function
• Aligning learning investment with business priorities — and proving the link to leadership
• Starting small: the handful of metrics that genuinely move decisions
• Making use of existing sources (LMS, HRIS, performance data) before buying new tools
• Building executive confidence in L&D’s numbers when resources are tight

Skills risk used to look like a shortage on next year’s hiring plan. When roles that did not exist a year ago are being filled from inside the company, and the work in existing roles is being redesigned around AI, the harder problem is spotting the gap before anyone has a name for the job. Pallavi Sebastian leads talent and learning for Salesforce’s global workforce after a career spent largely on the business side, running sales, operations and enablement organizations. In this fireside chat she shares how Salesforce reads skills risk by starting from the work rather than the job title, which signals have actually proved predictive, and how capability planning became something business leaders own rather than something L&D reports on.

• Starting from the work, not the job: breaking roles into tasks to see where capability is thinning before it shows up as a hiring gap
• What a high volume of internal moves, including into roles that did not exist a year ago, reveals about where the next gaps will open
• Which early-warning signals have held up (manager observation, workforce data, business performance) and why none is enough on its own
• Decision rights: where AI assembles the picture and where leaders still make the call on talent and reskilling
• Moving capability planning out of the L&D calendar and into the business operating rhythm, and what business leaders now own
• The open questions: what Salesforce has not solved yet, and what it is testing next

• Equipping managers to become frontline enablers of learning and development
• Embedding development conversations into everyday management practices
• Providing tools and resources that support manager-led learning
• Overcoming common barriers to manager engagement in development programs
• Measuring the impact on team performance and employee growth

• Positioning learning as a strategic lever in enterprise transformation initiatives
• Aligning development programs with evolving business capabilities
• Partnering with business leaders to drive adoption and engagement
• Supporting workforce readiness during large-scale change efforts
• Demonstrating how learning accelerates transformation outcomes

Executives in every industry are asking sharper questions of L&D’s numbers, and a polished impact story no longer carries the room on its own. Moody’s offers an unusually demanding test case: its leaders assess risk, methodology and data quality for a living, and they read learning data the same way. In this case study, Paul Tiesler shares how Moody’s manages learning as a portfolio of investments, how it reports evidence so it holds up under scrutiny, and the decisions to scale, restructure or retire programs that followed. Delegates will leave with a portfolio-review approach and reporting principles they can apply whatever their industry or size.

• The questions skeptical executives ask of L&D data, and how to answer them before they are asked
• Reporting methodology, confidence and limits openly, and why “we don’t know yet” can build more credibility than a polished result
• Running a regular portfolio review: criteria any function can adapt for sorting programs into performing, on watch, and due for a rethink
• What Moody’s scaled, restructured or retired as a result, and how those conversations went with business owners
• Which elements transfer to any organization, and which depend on how your own executives make decisions