AI trust is the new growth engine

Leaders have spent recent years learning how to thrive in an AI-transformed world — rethinking channels, preserving human meaning, cutting through overload, turning noise into signals of trust. Along the way, one truth has emerged: buyer confidence depends on more than campaigns and channels. 

But what happens when an AI chatbot delivers a false answer — or when an ad algorithm quietly excludes an entire demographic? These aren’t cautionary tales. They’re real risks. As we move into 2026, AI is no longer niche or experimental — it’s everywhere. And with it comes a new mandate: build accountability into the AI stack.

AI everywhere: The new reality

AI is part of every enterprise function. Companies are redesigning workflows, elevating governance and raising awareness of AI-related risk as adoption accelerates, according to McKinsey’s report “The State of AI: How Organizations Are Rewiring to Capture Value.”

Even if an enterprise isn’t adding AI, it’s embedded in vendors’ solutions, employees’ tools and bring-your-own-AI solutions. The result: unchecked tools, opaque algorithms and siloed deployments accumulate AI tech debt.

Why accountability is the differentiator

Executives have moved from wondering if they should deploy AI, and now grapple with how to do it responsibly. Accountability rests on a few clear pillars.

• Governance: Policies that define what AI can and cannot do.
• Ethics: Ensuring AI reflects fairness, inclusivity and brand values.
• Transparency: Making model behavior visible internally — clarifying when customers interact with AI externally.

McKinsey reports organizations investing in responsible AI see measurable value — stronger trust, fewer negative incidents, more consistent outcomes. Yet many still lack formal governance, oversight or clear accountability. Accountability must be an integral part of a growth strategy, not treated as an afterthought.

Dig deeper: In an age of AI excess, trust becomes the real differentiator

Architecting the trust stack

How do leaders translate accountability into practice? Through what I call the trust stack — a layered architecture for responsible AI at scale.

• Governance bodies: Ethics committees, cross-functional oversight (including legal, IT, compliance).
• Monitoring tools: Bias detection, model drift monitoring, anomaly logging, output validation.
• AI inventories: Full visibility into all models, tools and vendor dependencies across functions.

At the foundation of this architecture is trust, risk and security management that ensures governance, trustworthiness, fairness, reliability, robustness, efficacy and data protection. That provides the guardrails that make the trust stack work at scale.

Dig deeper: Marketing gains from AI begin with governance

The leadership mandate: Trust beyond silos

AI accountability cannot live in one department. It is the responsibility of the entire organization.

• Marketing must preserve brand promise: personalization that feels human and messaging that doesn’t mislead.
• Sales must ensure that AI-powered outreach or scoring reinforces, rather than erodes, trust. A model that excludes key demographics or misrepresents value damages credibility.
• CROs must ensure pipeline growth is ethical and sustainable. Unvetted algorithms can generate volume but produce long-term reputational or churn costs.
• Customer success must oversee support, recommendations and services powered by AI. One hallucinated response or misaligned suggestion can undo loyalty built over years.

Curiosity is a leadership skill: ask what could go wrong. 

• How does the AI decision feel to a customer? 
• Where is bias likely? 
• What transparency is required? 

These questions act as preventive guardrails.

Proof in practice: Who’s leading the way

Several organizations are already modeling parts of the trust stack:

• TELUS built a human-centric AI governance program and became the first Canadian company to adopt the Hiroshima AI Process reporting framework.
• Sage introduced the AI trust label, disclosing AI use, safeguards and governance standards to help SMBs adopt AI with confidence.
• IBM publishes AI FactSheets and maintains an internal AI ethics board, ensuring every model is documented, explainable and aligned to principles of transparency.

These examples show that trust isn’t a drag — it accelerates adoption, loyalty and long-term value.

Trust as strategy

AI accountability will be what separates leaders from laggards. In a world saturated with AI, the trust stack isn’t just a firewall — it’s the GPS guiding organizations toward sustainable growth and lasting customer connection.

For growth leaders, the mandate is clear:

• Lead cross-functional AI governance.
• Make trust a visible brand promise.
• Translate ethics and risk into language the C-suite and customers understand.

Done right, accountability delivers more than risk mitigation. Organizations that build a robust trust stack can accelerate adoption of AI-powered innovations, deepen buyer confidence that compounds over time and unlock scalable growth by avoiding costly tech debt.

In a world of AI excess, trust is the true engine of growth. Leaders who champion accountability won’t just preserve their brands — they’ll expand them, shaping the next era of ethical, intelligent and resilient customer relationships.

Dig deeper: Your AI strategy is stuck in the past — here’s how to fix it