This innovation sprint kickoff provided a powerful launch to ELE’s new Executive Innovation Sprint model — a peer-powered framework for co-creating solutions that matter.
“When senior leaders co-create across industries, they don’t just solve problems — they build the muscles of agile leadership together.” — Dirk Tussing
The sprint kicked off with an important exercise: defining the precise area of opportunity in high-performance coaching where AI could deliver the most value.
During this kick-off on Zoom, two problems were identified as a potential opportunity to experiment:
- Post-Training Coaching
- Job Performance Coaching
For this kick-off sprint, the group voted and ELE would like to dive deeper into using AI to improve Post-Training Coaching.
To deeply understand the chosen challenge, two separate breakout groups then developed detailed problem definitions, which solidified the scope of the sprint.
Cindy Miller’s Breakout Room’s Problem Definition Post-Training Coaching
In our current environment, post-training coaching is not happening. As evidenced by XYZ, we face the impact of behavior change not occurring and a low ROI on training. The lack of post-training coaching is due to several potential root causes: managers do not know their employees are attending training, managers do not know what is being taught in the training, there is no set expectation around post-training coaching, managers do not know what to coach, managers do not know what good coaching looks like, or managers do not know how to do the behaviors themselves—so they feel unable to provide feedback or coaching. Therefore, we have the opportunity to provide context to managers about what is being taught in training and to provide examples of what to coach on post training, among other support.
Matthew Eade’s Breakout Room’s Problem Definition Post-Training Coaching
In our current environment, post-learning coaching is inconsistent and heavily dependent on manager availability, as evidenced by engagement surveys, customer feedback KPIs, and high turnover rates. The impact is that employees—especially new hires in large, distributed teams and healthcare environments—struggle to apply newly acquired skills, leading to lower performance and missed opportunities for growth. The root cause is limited manager bandwidth and conflicting priorities, which prevent on-the-job feedback and support. Therefore, there is an opportunity to explore scalable, context-driven solutions—such as AI-powered coaching tools—that can provide real-time, personalized feedback and bridge the coaching gap when managers are unavailable..
At the October 20th Talent Development Conference, this idea took root. The spirit of agility came alive at the October 31st virtual event, which provided a powerful launch to ELE’s new Executive Innovation Sprint model — a peer-powered experience designed to help leaders practice innovation, not just talk about it.
In true ELE fashion, this first sprint brought together senior leaders from across industries to tackle a shared challenge. After thoughtful discussion and a live vote, the group narrowed the focus from Performance Coaching to Post-Learning Coaching—exploring how AI can make feedback continuous, scalable, and deeply human.
Defining the Challenge: Post-Learning Coaching
The first sprint tackled the performance gap that happens after learning: why employees so rarely apply what they’ve learned once the workshop ends.
“Define the problem well, or the solution won’t be usable.” — Deepa Kartha
Using a defendable-problem framework, participants surfaced a shared reality:
- Post-learning coaching is inconsistent and depends too much on manager availability.
- Limited time and unclear expectations erode follow-through.
- The opportunity? Use AI to extend coaching capacity and deliver personalized, real-time feedback when managers can’t.
“Learning ROI is lost when post-learning coaching doesn’t happen.” — Nicole DeFalco
The conversation reframed AI as a performance enabler, not a threat — a way to make feedback timely, scalable, and rooted in real behavior change.
🔍 Key Takeaways for Talent Development Leaders
- Innovation is a leadership skill. Sprints enable leaders to practice agility by solving real problems together across various industries.
- AI amplifies and scales coaching. Viewed as a performance enabler, AI helps make feedback timely and human.
- Post-learning coaching drives results. Training builds knowledge; coaching ensures it sticks and delivers impact.
- Short sprints, faster progress. In 2–3 weeks, executives design, test, and refine solutions that scale.
- Insights become playbooks. Each sprint’s results feed the ELE Idea Exchange—where members share, adapt, and apply what works.
⚙️ Practical Actions to Apply Now
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Run a 2–3 week mini-sprint inside your organization to tackle a specific challenge like improving post-learning application.
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Audit your feedback flow. Identify where and why coaching stops between training and performance.
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Pilot AI coaching tools. Test digital nudges or conversational AI to provide scalable, real-time feedback.
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Measure what matters. Track engagement and behavior change before and after coaching interventions.
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Capture and share learnings. Document insights to strengthen your organization’s innovation and coaching culture.
The High-Performance Coaching with AI innovation sprint reminded us that technology doesn’t replace leaders — it amplifies them.
When we define problems clearly, collaborate across boundaries, and use AI to make coaching continuous, learning becomes performance. The next phase will be a 2–3 week experiment centered on AI-supported Post-Learning Coaching, testing how scalable, data-driven feedback can accelerate learning transfer and performance.
As these sprints unfold, insights, toolkits, and outcomes will be shared on the ELE Idea Exchange—so every member can learn, adapt, and apply what works inside their own organization. Because at ELE, innovation isn’t theory — it’s a shared practice that turns insight into action.
