AI change fails when it is treated as a communications exercise or a training rollout. We design the sponsorship chain, manager enablement, and team-level rituals that make new ways of working stick. Resistance is expected; we surface it early and build responses into the plan rather than reacting after usage has already collapsed.
How we typically help
- ·Sponsor alignment and leadership mobilisation
- ·Change impact and resistance analysis
- ·Manager enablement and team engagement
Outcomes we work toward
- ·Leaders who know how to sponsor AI adoption in their functions
- ·Managers equipped to coach teams through real workflow change
- ·Higher sustained usage because behaviour change is designed in, not hoped for
How engagements typically run
Often paired with rollout or governance work: we embed change leads, run targeted enablement, and tune interventions as adoption data comes back.
Example engagements
Representative situations where organisations apply this service — patterns we see across sectors and geographies.
- ·Run a manager playbook for “AI + performance conversations” so team leads know how to set expectations without over-promising automation.
- ·Map resistance by function — e.g. legal fearing hallucinated citations vs. sales fearing slower workflows — and tailor interventions.
- ·Co-design team rituals (huddles, QA checklists) so responsible use is socialised peer-to-peer, not only top-down.
Indonesia and ASEAN context
Consumer goods manufacturer with plants in Java and Sumatra
New SOP assistants are introduced at head office, but plant supervisors default to WhatsApp and paper checklists. We align plant managers and union stewards on what changes in daily briefings, build short Bahasa-first micro-learning for line leads, and tie adoption metrics to safety and quality outcomes so the change is framed as job protection and consistency — not surveillance.