Can AI help identify and resolve GWC issues in EOS for better exit valuation?

Question

Tyler Smith · Accepted Answer

Yes, AI can profoundly assist in identifying and resolving 'Gets It, Wants It, Capacity to Do It' (GWC) issues within the EOS framework, which directly contributes to a better exit valuation. The GWC principle is fundamental to ensuring the right people are in the right seats, performing their roles effectively. For an exit, buyers scrutinize the strength and self-sufficiency of the leadership team and key personnel.

AI can analyze various data points to objectively assess GWC. For 'Gets It,' AI can process performance reviews, 360-degree feedback, and project outcomes to determine how well an individual truly understands their role and its impact. It can identify patterns where a team member consistently misses targets or misinterprets objectives, suggesting a 'Gets It' deficiency.

For 'Wants It,' AI can analyze engagement data, career aspirations expressed in surveys or HR systems, and participation in development programs. While more nuanced, consistent disengagement or a lack of initiative can be flagged by AI as a potential 'Wants It' issue. It can also cross-reference compensation and benefits data with industry benchmarks to ensure motivators are aligned.

And for 'Capacity to Do It,' AI can assess skill sets against role requirements, track training completions, and analyze workload distribution. If a role suffers from consistently missed deadlines despite a team member 'Getting It' and 'Wanting It,' AI can identify a 'Capacity to Do It' gap, perhaps due to insufficient tools, skills, or unreasonable demands.

By leveraging AI for GWC analysis, Tyler Smith can help companies proactively address talent gaps, optimize team structures, and implement targeted development plans. This ensures that by the time of an exit, the organization presents a strong, capable, and self-reliant human capital infrastructure, significantly enhancing its attractiveness and valuation to prospective buyers.