Edward Jones

1) How critical skills will evolve

The next decade will be defined less by specific roles and more by new ways of organizing work. We are shifting from static jobs to a fluid, capability driven model where people match to problems, tasks, and outcomes. Teams will continually form and reform around value streams, drawing on flexible pools of capability instead of rigid hierarchy.

Work is also becoming more collective. Complex challenges require shared sensemaking, system thinking and rapid alignment. The teams that succeed will adopt common playbooks, make decisions quickly and adapt together.

As skill half lives shrink, learning becomes an everyday experience. Effective organizations will build systems where learning happens through the work itself—supported by nudges, guardrails, feedback and embedded data that help people improve continuously.

The capability families that matter most over the next decade include:

• Learning Agility: Rapid learning, unlearning, and relearning.

• AI Augmented Work: Responsible use of AI—prompt patterns, oversight, automation, safety reviews and ethics.

• Systems & Product Thinking: Understanding how value flows and applying iterative, hypothesis driven approaches.

• Data & Decision: Causal thinking, experimentation, statistics, probabilistic judgment and tradeoff framing.

• Teaming & Influence: Adaptive collaboration, storytelling, coaching and constructive conflict.

• Trust, Risk & Compliance: Treating privacy, security and responsible AI as everyday habits.

• Change Navigation: Helping teams form new habits that make change stick.

These capabilities form the foundation for a workforce that can keep pace with accelerating disruption.

2) From training to measurable capability building

The core shift is redefining progress. It can’t be “I took a course.” It must be “I can do this in the flow of work—and here’s evidence.” That starts with writing capabilities in behavioral terms and tying them to real artifacts. Rather than asking whether someone learned A/B testing, we look for a well designed experiment that influences a customer metric and meets ethical standards.

Assessment should resemble real work: simulations, work samples, decision memos, code reviews, customer journey artifacts and peer calibration. These distinguish true application from passive content consumption.

For capability building to stick, it must be built into operating rhythms. Retro, backlog reviews, standups and decision reviews are not just governance they are the daily practice environment where skills are reinforced.

"Skills must evolve like products— owned, measured, instrumented and continuously refined."

Investments should shift from events to outcomes: reduced cycle time, fewer defects and risk issues, improved customer value. Funding should follow measurable capability improvement, not seat time.

To scale, organizations must treat skills as a product. Assign owners, publish quarterly roadmaps, instrument telemetry and version capabilities based on evidence. Skills should evolve the way any product in the ecosystem does.

3) Balancing technical depth with human skills

As AI becomes embedded in workflows, technical expertise alone is no longer the differentiator. What matters is the judgment, safety and contextual understanding that surround technical execution. The strongest teams balance deep skill with human capabilities like ethical awareness, communication, tradeoff framing, and structured decision making.

In practice, this looks like pairing key roles in critical flows:

• A builder with technical depth.

• A navigator with product, risk, and ethics fluency.

• An integrator who translates change and communicates clearly.

Smaller teams may combine these responsibilities, but the underlying capabilities must still be present.

Deliberate practice is essential. Scenario drills, red team reviews, and post incident debriefs help teams build muscle memory for ambiguous situations where automation intersects with human judgment. Decision playbooks—clear assumptions, risk signals, rollback criteria—create consistency, especially under pressure.

4) Metrics that signal a future ready workforce

A future ready workforce is measurable, not aspirational. Leading indicators show adaptability:

• Skill liquidity: Speed and ease of redeploying talent into adjacent capabilities.

• Time to competence: How quickly people demonstrate proficiency in real work.

• Internal mobility velocity: How fluidly talent moves toward opportunity.

• Experimentation throughput: How often teams test, learn and reuse insights.

• AI augmentation adoption: AI integrated into SOPs with clear outcome improvements.

• Change engagement: Participation in reviews and adherence to new habits.

Lagging indicators show whether capability turns into performance:

• Cycle time and time to value. • Quality and yield, including rework and defect escape.

• Customer outcomes.

• Organizational risk posture.

Health indicators—manager enablement, skills coverage, learning ROI and skill decay rate—show whether the system can sustain itself.

5) Guidance for leaders beginning long term skills transformation

Start with the work, not a curriculum. Identify three to five value streams that matter—like onboarding or claims resolution—and map the capabilities that drive outcomes. Build rubrics for “what good looks like” and instrument the work before scaling.

Treat skills like a product: assign owners, set quarterly OKRs, release early drafts, gather evidence, and refine. Build a library of work samples and simulations to make expectations real.

Make learning part of the daily rhythm. Rituals like decision reviews, retro and peer demos are some of the strongest capability builders. Fund embedded coaches and use habit stacking to reinforce new behaviors.

Tie talent decisions to demonstrated capability. When mobility and promotions depend on evidence, leaders and associates engage differently. Reduce friction in internal moves to increase adaptability.

Keep governance lightweight but real—checklists, escalation paths, model cards, audit trails. Run capability risk reviews to stay ahead of emerging gaps.

Most importantly, communicate with proof. Share before and after metrics, show artifacts that demonstrate learning and tell stories of real outcomes. That’s what builds belief and momentum.