Structured AI Maturity Accelerator
AI systems don't implement themselves. The People domain determines whether an organization's workforce has the literacy, skill, and cultural readiness to work with AI productively and responsibly — at every level, from the executive team to the frontline.
It is the domain that asks: do our people know how to work with AI, and do they want to?
People maturity is assessed across five focus areas that together determine whether the organization's workforce can support, advance, and responsibly govern AI adoption.
AI literacy is not just for data scientists. Executives who commission AI projects, managers who oversee AI-assisted workflows, and frontline employees who interact with AI systems all need a foundational understanding suited to their role. Literacy programs are structured, role-specific, and updated as AI capabilities evolve.
Talent strategy addresses both building and buying capability. It includes hiring for AI-adjacent roles, upskilling existing employees whose roles are being transformed by AI, and building retention structures that keep AI expertise in the organization rather than cycling through contract workers.
Effective AI outcomes require collaboration between people who understand the technology and people who understand the business problem. Cross-functional team structures, shared language, and defined collaboration practices make that possible rather than accidental.
AI changes how people work. Change management ensures those transitions are planned, communicated, and supported — not imposed. It addresses resistance, identifies the people most affected by AI-driven change, and provides tools for navigating the transition without losing productivity or trust.
Responsible AI (RAI) is not the exclusive concern of the legal or ethics team. Everyone who works with AI — who uses its outputs, who designs its workflows, who approves its deployment — needs to understand what responsible use means in practice and what to do when something looks wrong.
Every AI system is designed, deployed, monitored, and interpreted by people. Organizations that invest heavily in AI technology but neglect the People domain consistently underperform those that invest in both. The organizations that get the most from AI are the ones that have prepared their people to work with it.
What the People domain looks like at each of the six SIMA360 capability levels.
AI is viewed as a technology problem, not a people one. There are no training programs, no AI literacy initiatives, and no structured talent strategy. A few individuals may be self-educating. The organization as a whole is unprepared to work alongside AI systems.
Ad-hoc learning is occurring. Internal champions are advocating for AI literacy but without organizational support. Some individuals have completed self-directed courses. There is no structured program, no defined competency model, and no investment in change management.
Training exists for specific roles involved in AI projects. Early cross-functional collaboration is beginning. Responsible AI (RAI) has not yet entered training curricula. Change management is acknowledged but not resourced or structured.
A structured AI literacy program is in place. A defined talent strategy for AI covers hiring, reskilling, and retention. Change management processes are documented and staffed. Responsible AI principles are introduced in training programs.
Enterprise-wide AI competency is building. Continuous learning is embedded in the culture. Responsible AI is central to training rather than a footnote. Cross-functional teams working on AI have the skills they need. Leadership actively models AI literacy.
AI literacy is a defining organizational characteristic. The organization develops and shares AI education externally. Talent recognized for AI expertise attracts further expertise. The culture is AI-ready — adaptive, curious, and responsible by default.
AI literacy programs target data scientists and engineers but exclude business stakeholders.
Leaders commission AI projects they don't understand and can't evaluate. Frontline employees resist AI tools they weren't prepared to use.
Talent strategy focuses on hiring without addressing reskilling for existing employees.
A two-tier workforce emerges: AI-capable new hires and an existing workforce that feels displaced and disengaged.
Change management is treated as a communication exercise rather than a structured process.
AI adoption encounters sustained resistance that wasn't anticipated because no one mapped who would be most affected.
Cross-functional AI teams exist on paper but the collaboration structures to make them work don't.
Technical teams build things business teams don't understand. Business teams ask for things technical teams can't safely build.
Responsible AI training is a one-hour module delivered once at onboarding.
Employees know the principles exist but don't know how to apply them when an ambiguous situation arises.
Measures your current People maturity level — assessing AI literacy, talent strategy, change management readiness, and RAI awareness across the organization.
Structures improvement cycles for building organizational AI capability — from closing specific skills gaps to developing a long-term workforce readiness strategy.
Provides AI literacy curriculum frameworks, change management planning templates, cross-functional team charters, and RAI training resources.
Is the People domain's primary delivery mechanism — structured training programs that build AI literacy, responsible AI competency, and practitioner capability at every level of the organization.
SIMA-Probe measures your People maturity level and identifies the literacy and capability gaps most likely to limit your AI outcomes.