Mark Hickman, Managing Director, North America at Sage – Simplifying business workflows to break down barriers and achieve growth.
In Canada and the U.S., artificial intelligence adoption among small and midsize enterprises (SMEs) has often lagged behind larger organizations. SMEs make up 98% of businesses in Canada and 99% in the U.S., meaning their ability to harness AI’s benefits is crucial to economic growth. A key factor in accelerating AI adoption is building trust and understanding of the technology.
When businesses understand how AI can help reduce complexity, automate repetitive tasks and simplify highly technical functions—like compliance—adoption of AI often accelerates. However, a lack of trust and understanding remains a major barrier preventing SMEs from fully embracing AI solutions.
How can companies bridge the gap between awareness and implementation?
Overall, businesses widely recognize the value technology investments bring to their businesses. According to my company’s latest “Small Business, Big Opportunity” report, 94% of Canadian SMEs consider technology essential for operations and growth. However, there is still a significant gap between acknowledging AI’s potential and understanding how it enables greater productivity, efficiencies and return on investment.
For example, many SMEs are not aware they’re already using AI-enabled solutions. A recent survey by the Business Development Bank of Canada found that when first asked about AI usage, only 39% of SMEs reported using AI. But “when presented with a list of AI-powered tools, this number jumped to 66%.”
This lack of understanding can lead to skepticism, which can cause business owners to question the accuracy of AI-driven insights or hesitate to integrate AI into their operations. The responsibility of building trust in AI shouldn’t fall to businesses alone—it requires a collaborative effort among software developers, governments, industry groups and businesses.
Here are five key principles that I think will be fundamental to building trust in the technology to accelerate adoption for small businesses in Canada and around the world:
1. AI needs to be transparent, auditable and explainable.
Transparency in AI systems builds trust among users by helping them understand how solutions work and ensuring they can identify and correct biases. Making AI accountable allows organizations to trace the decision-making process, giving SMEs—especially those in regulated industries—greater control over automated workflows and compliance requirements.
Explainable AI builds greater understanding for users by bridging the gap between highly technical concepts and business outcomes. As many end users within SMEs don’t have technical backgrounds or the expertise, AI tools and platforms that provide built-in explainability features make the technology far more accessible for SMEs to adopt and realize its benefits.
2. Developers must embed trust throughout the AI life cycle.
Trust in AI cannot be an afterthought—it must be built into the software development life cycle from the outset. Developers should prioritize ethical AI practices, ensuring that transparency, accountability and explainability are core principles throughout AI’s design and deployment.
When developers commit to a trust-first approach from the beginning, they can help ensure that AI code and tools meet established quality and security standards. Additionally, establishing clear guidelines and policies for acceptable AI use can help businesses deploy AI responsibly, avoiding unintended risks.
3. Regulatory frameworks should balance safety and innovation.
Transparent regulatory frameworks are crucial to building public trust in AI technologies by ensuring they are developed and used responsibly. Regulatory frameworks help ensure that AI systems are safe, nondiscriminatory and respect fundamental human rights.
For example, Canada’s Artificial Intelligence and Data Act (AIDA) focuses on mitigating risks in high-impact AI systems while promoting responsible innovation. AIDA requires measures to identify and mitigate risks of harm or bias, ensuring compliance through oversight bodies like the AI and Data Commissioner in Canada. At the same time, flexible frameworks allow regulation to foster innovation by adapting to the quickly evolving technology.
With a flexible regulatory framework, governments can ensure AI remains both safe and innovative, adapting to technological advancements while providing necessary safeguards.
4. An ecosystem for AI auditing is essential.
As AI becomes more prevalent and influential in business and our daily lives, having the proper oversight is critical to ensuring its ethical and effective use. I think an ecosystem of organizations that audit AI systems—similar to how accounting firms audit financial statements—is quickly becoming necessary for ensuring the ethical, transparent and efficient use of AI.
Auditing ensures adherence to legal standards, reduces risks of noncompliance and improves performance by identifying inefficiencies or flaws in AI systems. Most importantly, AI audits can create trust by demonstrating a commitment to fairness and responsible innovation.
5. AI development must include diverse perspectives.
Ensuring greater diversity of voices in building and training AI is crucial for creating technology that truly represents and serves all communities. AI systems developed by diverse teams are more likely to reflect the needs of a broader user base, resulting in inclusive and equitable tools. Diverse teams are better equipped to identify and mitigate biases in AI algorithms by bringing together different cultural and professional backgrounds, allowing for more comprehensive approaches to addressing complex challenges.
Research supports the value of diversity in AI development. For example, researchers from Google, Meta and Lincoln Laboratory all found that more diverse training scenarios led to improved AI performance and stronger collaboration between users and AI systems.
Focus on building confidence in AI.
At the end of the day, small businesses often operate with tighter budgets and narrower margins for error than larger enterprises, making trust in AI paramount. Building trust through transparent and ethical AI development processes fosters user confidence, helping SMEs bridge the gap between understanding the value of AI and successfully integrating it into their business operations.
Through prioritizing transparency, regulatory oversight, independent auditing and diversity in AI development, businesses, policymakers and developers can work together to create AI solutions that SMEs can trust—helping them harness AI’s full potential for growth and efficiency.
Forbes Business Council is the foremost growth and networking organization for business owners and leaders. Do I qualify?
Read the full article here
