Realising the business potential of AI means going beyond adopting tech tools as a shortcut to lower back-office costs. It requires a strategic, thoughtful approach that seeks to integrate AI with human capabilities.
Success with AI depends on managing both technology and the human elements of your organisation. Leaders must assess the inherent risks of AI, navigate complex regulatory and ethical landscapes, and consider the needs and views of their stakeholders.
Throughout this process humans remain an integral part of the AI loop. Human oversight is required for ethical decision-making, contextual understanding, and ongoing refinement of AI systems. Unassisted AI cannot replicate human capacity for nuanced judgement and empathy. These qualities are needed to address biases and ethical concerns, and to ensure alignment with company values and community expectations.
Human judgement must choose the right tool for the job. Not all AI technologies fit all applications.AI that plays a meaningful role in human impact decisions, for example assisting parole boards and debt collectors, needs to be explainable. For all the proven fallibility of human decision-making, artificial “black boxes” delivering unexplainable decisions are not yet suitable to make these calls unassisted.
When using black box AI, leaders must develop and refine a framework to evaluate the trustworthiness, reliability, biases and vulnerability of the models to cyberattack and sabotage. We must accept that we are optimising rather than minimising risk, and there will be no perfect answers. Balancing compliance, innovation, productivity, margin for error and stakeholder expectations are necessary and very human considerations. Taking these steps can prevent the uncontrolled spread of AI systems with undocumented risk profiles in different units across the business.
These higher-level decisions that establish governance frameworks and deployment guardrails should precede more operational decisions about how human-AI loops should look in your organisation.
Established models of human-AI collaboration can guide business. For example, where AI might misclassify or incompletely process documents, a tight, pre-emptive approach is often useful.
Initially human involvement may be extensive, verifying individual AI predictions against ground truth. As the AI’s performance improves, humans can move to a downstream quality assurance role, checking samples of the high-risk outputs, along with those flagged by AI as low confidence.
Validation and diagnostic tools like Affinda’s can also be used with in-house models, avoiding the need to develop these from scratch.
Your team can develop rules to validate outputs and correct those that fail validation. You might use tools like Affinda to cross-reference predictions against records in your database (like purchase orders, invoice numbers and supplier names) to ensure accuracy and consistency. This could be augmented by checks of things like the expected number of rows in tables, digits in fields, sum totals for taxable items, or even using fields like ABNs with consistent formats as uniform “checks” to flag inaccurate data processing.
In some cases, you may notice issues at a dashboard level - statistical differences in the characteristics of AI- and human-created outputs – but lack direct evidence of errors. Although humans will make errors too, they’re unlikely to repeat the same mistakes as AI. In these situations, focus on diagnostics. You can compare the distribution of errors across both categories to identify broad patterns, and then set up control workflows with skilled team members and use diagnostic analytics to identify discrepancies between AI automation results and human decision-making.
As you plan these interventions, align them with the overall business strategy, governance frameworks, and risk matrices. This will guide resource allocation. Lower-risk systems may tolerate more leniency, while critical systems near governance redlines require stricter controls. Over time, human resource allocation within AI automation loops will tend to stabilise, though minor adjustments will occur as systems evolve.
The key to unlocking AI’s full potential lies not merely in its deployment but in how it complements and enhances human judgment, ethical considerations, and contextual understanding. By embedding humans into the AI loop, companies ensure that their technology remains transparent, reliable, and aligned with societal values. This human-centric approach not only mitigates risks but also drives continuous improvement.
In this new era, embracing this collaboration will be essential for achieving long-term, sustainable growth and competitive advantage.
