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Successfully managing people and change for a new generation.
Trusted by 40+ global companies, with over 1000+ engagements completed scoring an NPS score of 32.
ADO
Simplifying AI Adoption for Organizations.
American Express
Accenture
Shell
CIBC
Shopify
Bank of America
Enbridge
Royal Bank
Presidents Choice
Sobeys
Walmart
Rogers
Scotia Bank
Mackenzie
State of Arizona
Pepsi
Telus
Loblaws
Electric Safety Association
T-Mobile
CBC
Toronto Dominion Bank
Direct Energy
Cuna Mutual Group
These are the top 8 AI adoption challenges we tackle for startups, small and large organizations:
Lack of trust and explainability:
Teams don’t trust what they can’t explain.
Without simple ways to check accuracy, understand how AI arrived at an answer, and know when not to use it, AI feels risky and gets sidelined.
AI policies that don’t turn into practice:
AI policies often live as PDFs that no one reads. If governance isn’t wired into workflows, tools, approvals, and coaching, people will default to convenience over compliance even with the best intentions.
Fear of job loss and loss of value:
AI triggers real anxieties: “Will I still matter?” “Will this make my role smaller or more mechanical?” These fears surface as “concerns about process” or “timing” but are fundamentally emotional and identity-based.
No owners for adoption & governance:
When ownership is fragmented, no one designs the end-to-end experience of using AI safely and effectively. AI becomes a set of local experiments rather than an enterprise capability.
Unclear business value and fuzzy objectives:
Leaders say “we need AI” but can’t clearly define the decisions it should support, the outcomes it should improve, or how success will be measured. When business value is vague, AI becomes a cost centre rather than a growth engine.
Power shifts and political resistance:
AI changes who holds expertise, who owns forecasting, and who gets to make decisions.
It can reduce the span of control or de-center “gut feel” leaders. Resistance is rarely about the model; it’s about power, identity, and status.
No shared AI story:
Without a narrative, why AI, why now, and what it means for people, AI feels like another initiative from “the centre.”
A story is what connects governance, behaviour, and value into something people can believe in and act on.
Teams stuck in pilot churn:
Early pilots generate excitement, but scaling stalls.
Some teams sprint ahead, others freeze, legacy systems push back, and no one curates the portfolio of experiments into a coherent roadmap.
ADO
Uses behavioural intelligence to assess, understand,
and fix the user, team, and governance challenges that create friction in AI adoption and deliver poor outcomes.
Core aspects of behavioural intelligence:
Instead of focusing solely on tools or training, ADO uncovers the human, cultural, and workflow frictions that create the 8 Wicked Problems of AI Adoption and then builds a clear path forward.
Define what great AI use actually looks like: Identify the behaviours, skills, and routines that differentiate super users from casual users and translate them into replicable standards.
Understand what people really do in their workflows: Map real behaviours (not documented ones), decision points, shortcuts, risks, dependencies, and where AI naturally fits or fails.
Capture pain with precision: Observe and document friction, ambiguity, errors, handoffs, slowdowns, and psychological blockers in a way that directly links to adoption barriers.
Understand the context behind behaviour: Uncover incentives, pressures, constraints, cultural norms, trust gaps, escalation patterns, and historical experience that shape how teams respond to AI.
Define realistic human-centred outcomes: Translate AI ambition into practical behaviours, measurable success signals, and achievable adoption milestones.
Create a strategic-to-tactical adoption plan: Build a single strategy that moves from executive clarity → team capability → behaviour change → governance → measurable adoption loops.
Diagnose team impacts and barriers: Identify role shifts, skill gaps, fear patterns, workload changes, and system dependencies that shape each team’s readiness for AI.
Surface behavioural risks and outcomes: Reveal where behaviour amplifies AI risk (bias, misuse, over-trust, under-trust) and design targeted interventions to reduce it.
Establish transparency and trust conditions: Define what teams need to feel safe using AI: explainability, oversight routines, clear decision rights, and escalation paths.
Build a decentralized AI adoption program: Empower local teams to adopt AI safely through repeatable behaviours, micro-governance, and community-driven learning loops, reducing reliance on central command-and-control.
ADO analyzes how people work, what they fear, what they trust, and what truly blocks adoption, then turns this insight into a decentralized adoption program, clear behavioural standards, practical governance routines, and measurable outcomes that make AI safe, usable, and scalable.
We are a small, specialized company with a unique capability to help:
1 Organizations asking the AI 0 to 1 question.
2 Teams are starting the internal AI business analysis.
3 Companies determining how to measure & audit AI success.
4 Clients trying to solve a problematic AI pilot or platform launch.
5 Leaders who want to design an AI strategy.
6 Organizations developing their AI governance and policy framework.
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