AI Vision Report: Past the Pilot to the Agentic Future of Work
Insights from our survey reveal what's next for AI and the future of work.
AI adoption is outpacing governance. As AI use scales, employees adopt unvetted tools, and stakeholders ask tougher questions about risk, businesses need guardrails that match the speed and scale of AI adoption. Without them, AI can work against your strategy, introduce material risks, and slow the value it was meant to create.
Trust, controls, and accountability are not constraints, but accelerators. Wherever you are on your AI journey, we can help you build an AI governance strategy that's defensible, practical, and right-sized for your organization today.
What’s driving the urgency for AI governance today?
AI pilots are proliferating across the business without consistent oversight or governance in place.
Employees are using unsanctioned tools, creating shadow AI risks that are difficult to detect and control.
Boards are demanding visibility into AI risk as part of broader governance and accountability expectations.
Customers are asking questions about how AI is being used and what controls are in place to support responsible use, making governance an important part of trust, transparency, and commercial readiness.
Regulators and auditors are requesting evidence of AI controls, and teams are preparing for ISO 42001 and ISO 27001 alignment.
AI governance is stuck between policy and practice.
Organizations have principles, standards, and risk appetite statements, but lack the operating model, controls, evidence, and assurance mechanisms needed to scale AI safely, while balancing speed and adoption.
The costs of inaction
Eroded trust and reputational risks
Lost or delayed deals
Regulatory exposure
Constrained growth across markets
Increased risk of cyber incidents
Legal and litigation risks
AI governance consulting services that scale
Our end-to-end AI governance consulting services span strategy, implementation, and assurance, enabling human-led AI adoption while protecting business value and empowering safe scale. Our approach is grounded in our AI Vision 2030—the belief that we’re moving towards a human-led and AI-embedded future.
- Assess your current AI readiness and governance maturity.
- Identify control gaps and develop a road map.
- Establish governance structures, roles, and accountability.
- Develop AI policies, standards, and processes.
- Design and deploy critical capabilities to enable AI governance.
- Identify and manage AI risks.
Across each engagement, we work closely with you to deliver right-sized frameworks, practical guardrails, and implementation approaches tailored to your organization’s maturity and industry context.
Build AI trust with BDO
We are positioned to help you build a practical and trusted AI governance framework to stay ahead of operational risk, external scrutiny, and board confidence.
Your challenge
AI governance standards are defined, but difficult to operationalize.
Our solution
We can help you align with guidelines such as ISO/IEC 42001, the NIST AI Risk Management Framework, Canada's AI for All strategy, and industry-specific regulations like those issues by the Office of the Superintendent of Financial Institutions.
Your challenge
Siloed teams, fragmented execution, and limited governance expertise.
Our solution
Our multi-disciplinary team brings applied knowledge across the full AI life cycle, from cybersecurity to audit readiness.
Your challenge
Limited real-world governance experience applying governance at scale.
Our solution
We’ve applied governance practices both as client advisors and internally as AI client zero, bringing practical insight into what works.
End-to-end AI governance consulting
Effective AI governance needs to work in practice, not just on paper. We can assess your current position, design a practical AI governance baseline, and validate that your policies and guardrails are fit for purpose. Our team brings a practical advisory mindset and hands-on experience to help you scale AI for value—responsibly.
Connect with our team to move from AI sprawl to governed AI outcomes
1Go further, faster with AI, IBM Institute for Business Value, December 2025.
FAQ: Responsible AI in the enterprise
AI governance refers to the structured guardrails organizations use to manage AI systems across their life cycle, ensuring they operate ethically, transparently, and in line with business objectives. AI governance is not just a compliance exercise, but enables organizations to scale AI with confidence by proactively mitigating risks and balancing innovation with accountability.
AI governance should have clear, centralized ownership. In leading enterprises, accountability typically sits at the executive level, with formal oversight from the board. At the same time, effective AI governance should be operationalized through cross-functional collaboration spanning technology, risk, legal, HR, and business teams.
AI governance should begin at the start of AI adoption, before tools are deployed or embedded into business processes. Governance needs to be built into AI strategy, risk assessment, tool selection, and operational workflows from the outset. Organizations that treat governance as an afterthought risk creating accountability and oversight gaps that become harder to manage as AI adoption scales.
Canada does not yet have a comprehensive federal law governing AI, but broad federal legislation is widely expected in the near term. At the same time, several provinces and industry regulators such as OSFI are advancing their own legislative and regulatory requirements independently. Taken together, these developments signal a rapidly evolving regulatory landscape that organizations should proactively prepare for.
ISO/IEC 42001 is a voluntary international standard for AI management systems. It provides organizations with a structured way to govern AI responsibly, including leadership commitment, risk assessment, auditing, awareness, and ongoing monitoring. Your organization can use ISO/IEC 42001 to assess AI tools before adoption, assign clear accountability, manage risks such as bias and hallucination, and show boards, regulators, clients, and other stakeholders that AI is being used responsibly.