As enterprises move from artificial intelligence (AI) pilots to large-scale deployment, behavioural friction among employees and middle management could emerge as one of the biggest barriers to successful AI adoption, according to Terragni Consulting.
Organisations are investing heavily in AI platforms, pilot programmes and employee training, but the biggest challenge in scaling AI may not lie in the technology itself.
According to Terragni Consulting, the human behaviour and engagement-focused consulting firm, the challenge may instead lie in whether people are willing and able to change the way they work.
Terragni Consulting said organisations often approach AI rollout as a technology deployment exercise, with training and successful pilots viewed as indicators of adoption readiness.
However, moving from a successful pilot to enterprise-wide adoption can expose a less visible challenge: behavioural friction.
“AI transformation is fundamentally behavioural” is the central proposition behind Terragni Consulting’s latest AI adoption framework, Vector. The framework focuses on human factors that can slow down AI adoption within organisations.
According to Terragni Consulting, resistance to AI rarely appears as outright rejection. Instead, it can manifest through delayed approvals, pilot fatigue, over-governance, repeated escalation, excessive validation and performative compliance.
By the time such resistance becomes operationally visible, AI adoption may already have slowed.
The Middle-Management Paradox
One of the critical areas identified by Terragni Consulting is middle management.
Middle managers play an important role in translating organisational strategy into execution, shaping team behaviour, determining how quickly new processes are adopted and creating an environment in which employees can experiment with new technologies.
At the same time, AI can potentially challenge the structures around which middle-management roles have traditionally been built, including authority, expertise, control and relevance.
This creates what Terragni Consulting describes as a “middle-management paradox”. The people expected to accelerate AI adoption may themselves experience uncertainty about what AI means for their roles and influence.
As a result, resistance may become psychological before becoming operational, Terragni Consulting said.
Three Hidden Frictions Behind AI Adoption
The Terragni Consulting framework identifies three broad forms of friction that can influence whether employees embrace new AI-enabled ways of working.
Cognitive friction: Employees may not fully understand how AI changes their roles or responsibilities.
Identity friction: Employees may question what happens to their expertise, value or professional identity if AI performs parts of their work faster.
Context friction: Even when employees are willing to change, organisational systems, incentives and workflows may continue rewarding established behaviour.
Together, these factors can create a gap between AI availability and actual AI adoption, according to Terragni Consulting.
From Technology Readiness to Behavioural Readiness
Terragni Consulting’s approach proposes that organisations should assess behavioural readiness alongside technological readiness.
Its Vector Solution Framework is structured around three stages: Diagnose, Map and Accelerate.
The first stage identifies behavioural barriers such as readiness, managerial resistance, workflow friction, adoption anxiety, role ambiguity and incentive misalignment.
The second stage maps these issues using tools such as AI readiness heatmaps, behavioural risk dashboards, resistance maps and adoption archetypes.
The final stage prioritises interventions designed to make AI adoption more measurable, actionable and trackable.
The approach is aimed at organisations seeking to move beyond experimentation, including those scaling generative AI internally, moving successful pilots into enterprise-wide deployment, redesigning operating models or increasing utilisation of AI tools, Terragni Consulting said.
The Next Challenge May Be AI Adoption, Not Access
The shift has implications for how organisations measure AI success.
A successful technology deployment does not necessarily mean employees have changed their behaviour.
Similarly, completing an AI training programme does not automatically mean employees will integrate AI into their everyday workflows.
For businesses, the next phase of AI transformation may therefore require a broader question: not simply whether an organisation has implemented AI, but whether its people, processes and incentives are enabling employees to use it.
“Most firms optimise systems. We optimise human adoption,” is the positioning behind Terragni Consulting’s Vector proposition.
As AI moves deeper into organisational decision-making and everyday workflows, the ability to understand and address human behaviour could become as important to transformation outcomes as the technology itself.
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