As agentic AI starts to permeate into core processes and enterprise workflows such as software programming, cybersecurity, ERP, CRM, BI, supply chain, retail, and other areas, the trust equation will shift from informational trust issues to transactional trust issues. The latter include ensuring appropriate levels of human oversight, accountability, transparency in decision-making, exception handling, and so on. While the no-code/low-code nature of agentic AI will streamline BP redesign efforts, it’ll be critical to spend a suitable amount of these time savings on thorough testing across all workflows and scenarios. Even though your AI is now smart enough to handle exceptions, it’ll be important to carefully test these situations as well.
Decide on AI policies…
…to align with and clearly communicate to end users, and proactively impact trust levels in your implementations.
Aligning with various national and international pacts and other forms of standards, policies, and agreements is a great way to demonstrate commitment to AI ethics to end users. For example, the EU AI Pact supports “voluntary commitments from the industry to adopt the principles of the EU AI Act before its official implementation.” Your AI governance practices can be a key differentiator, so it’s important to communicate internally as well as with customers and partners.
