How organizations are rewiring to capture value” (March 2025) — with key findings, implications for leaders (especially for your work in enterprise transformation and innovation), and coaching-led reflections.
Key Findings
- AI adoption is widespread—but value capture is still early
- 78 % of respondents report their organisations use AI in at least one business function (up from 72 % in early 2024).
- Generative AI (gen AI) usage has surged: 71 % say their organisation regularly uses gen AI in at least one function, up from 65 % in early 2024.
- Yet, fewer than 20 % of organisations say they track KPIs for their gen AI solutions; less than a third report following most of the “12 adoption-and-scaling best practices.”
- Enterprise-wide bottom-line impact remains limited: only ~17 % of respondents say that 5 % or more of EBIT in the past 12 months is attributable to gen AI use.
- The differentiator: structural rewiring over just tools
- Among 25 organisational attributes tested, fundamental workflow redesign (i.e., changing how work gets done, not just automating old processes) has the biggest correlation with EBIT impact from gen AI.
- CEO (or C-suite) oversight of AI governance is strongly associated with higher value capture; larger organisations show stronger correlation.
- Organisations are centralising risk, compliance, and data governance elements (often via a Centre of Excellence), while tech talent and adoption tend to follow a hybrid model (part central, part distributed).
- Workforce, talent and risk are front of mind
- Large organisations are more likely to have hired AI-compliance and AI-ethics specialists (13 % and 6 % of respondents respectively).
- Reskilling of existing employees is underway: many organisations report shifting employees to new activities enabled by time saved via automation or gen AI. However, 38 % predict little or no change in workforce size over the next 3 years.
- Risk mitigation is accelerating: 47 % of respondents say their organisations have experienced at least one negative consequence from gen AI (vs 44 % early 2024), and more organisations report actively managing risks around inaccuracy, cybersecurity, IP infringement.
- Larger organisations (> US$500 M revenue) are moving faster
- These firms are more likely to follow best practices for adoption & scaling, more likely to embed gen AI across multiple functions, and more likely to report value. Smaller firms lag.
Implications for Leaders & Transformation Practitioners (for your context at Agile Agilist)
- Focus on process-and-workflow redesign, not just model adoption. As you work with enterprises moving from Scrum to Kanban, or adjusting operating models (like your Innovation Framework or SAFe-aligned value streams), the key is rewiring the work around the AI capability, not just layering AI on top of old processes.
- Ensure executive sponsorship and governance. In your coaching of executive programmes (SPC, ASPC), emphasise the need for top-level ownership of AI governance and strategy, not relegating AI initiatives solely to IT or digital teams.
- Equip the workforce for transition. As you build training for Innovation Master level or integrate digital/transformation within ASCO value streams, embed not just the tool-usage skills but the mindset shift, role redesign, and new operating rhythms that gen AI demands.
- Embed measurement early. One of the core gaps: tracking well-defined KPIs for AI solutions. In your consulting engagements (digital transformation, product operating model), you can help clients establish meaningful metrics (e.g., cycle-time reduction, decision-accuracy improvement, human-machine collaboration ratios) alongside adoption.
- Manage risk consciously. Especially for your large client base (IBM, PWC, US-AirForce etc), governance, ethics, data privacy, bias, and explainability will all be critical for scaling AI in highly regulated environments.
- Tailor approach by size and scale. For smaller organisations or PODs transitioning from Scrum → Kanban, the path may be more incremental; while for large enterprises, the “rewiring” must happen at scale, and they can afford to deploy more distributed models and advanced governance.
Coaching Reflection
- When you coach transformation leaders, one question to ask: “What workflow in your organisation would you not worry about if gen AI solved it tomorrow?” If they struggle to answer, that’s a signal the organisation hasn’t yet identified the real redesign opportunity.
- Encourage leaders to shift conversation from “Where can we plug in AI?” to “How should our way of working fundamentally change because AI is now embedded?”
- Remind them: tools matter far less than operating model. Like the electrification example we discussed, AI is not just a power-source plug but a full systems redesign.
- Use the “12 best practices” list from McKinsey (establishing a dedicated team; embedding gen AI into business processes; role-based training; tracking KPIs; fostering trust; etc.) as a coaching checklist for readiness and maturity assessment.
Final Thought
The report confirms what you already emphasise in your framework: Intentional change > technology hype.
Adoption of AI is necessary, but rewiring organisational structure, workflows, talent, governance, and metrics is what unlocks value. For your role in shaping innovation frameworks, enterprise transformations, and SAFe-aligned practices, this underscores: it’s not enough to add AI — you must embed AI thoughtfully.
If you like, I can prepare a slide deck summary of this report (3-5 slides) with clickable links and major graphs (adapted for your Innovation Master or executive-coaching toolkit). Would you like me to build that?
