Banks prioritise AI augmentation over automation to bridge adoption gap

The artificial intelligence sector is currently facing a distinct supply problem, characterised by a significant disconnect between the rapid expansion of available tools and the slower pace of organisational integration. Vendors are continuously releasing increasingly sophisticated models and platforms, yet many organisations remain focused on extracting value from existing systems rather than adopting new ones. This disparity has been described by research firm Gartner as an AI adoption gap, where demand for practical implementation lags far behind the industry’s growing supply of capabilities. Despite years of investment and experimentation in the field, only 22 per cent of organisations have successfully scaled AI across multiple business units. For the banking sector, the primary challenge is not access to these technologies, but rather identifying practical methods for their application. A clear lesson emerging from this landscape is that the most significant benefits often arise from improving existing workflows rather than attempting to remove human involvement entirely, leading many institutions to favour augmentation over automation.

Banking remains an industry where human judgment is critical, as customers expect personalised support and regulators demand accountability and transparency in decision-making processes. Removing humans from complex customer interactions introduces substantial operational, compliance and reputational risks. Consequently, the most valuable AI applications in this sector are often less dramatic than industry headlines suggest. Rather than delegating decision-making authority to machines, many financial institutions are concentrating on routine tasks that consume employee time and attention. Research from the National Bureau of Economic Research, while conducted outside the banking context, indicates that this approach can yield meaningful results. The study found that customer service agents using a conversational AI assistant increased productivity by an average of 14 per cent. Among novice and lower-skilled workers, these gains reached 34 per cent, as the technology effectively distributed the expertise of top performers across the workforce, helping newer employees become productive more quickly.

The case for augmentation is particularly strong in collections and recovery, where conversations require significant judgment. Borrowers may be navigating financial hardship, job loss or illness, requiring collectors to balance compliance requirements, bank policies and customer experience in a single interaction. Several US banks are exploring this augmented approach through solutions such as C&R Software’s Collector Assist. This technology acts as a copilot for collectors, providing immediate guidance drawn from approved policies and servicing documentation without interacting directly with customers. The value lies in allowing collectors to quickly retrieve relevant information without placing customers on hold, thereby maintaining confidence in the conversation. The collector remains responsible for the interaction, judgment and outcome, ensuring that human oversight is preserved throughout the process.

A common mistake in AI implementation is treating the deployment itself as a success metric. Institutions making the most progress focus instead on the bottlenecks slowing their people down, such as excessive time spent searching for answers, increasing call handling times or the loss of institutional knowledge as experienced employees retire. Starting with these specific challenges often leads to more practical opportunities than starting with the technology itself. Ultimately, organisations closing the AI adoption gap are not necessarily deploying the most advanced technology, but are finding targeted ways to help employees work faster and make better decisions. In banking, this means prioritising augmentation before automation, strengthening human judgment rather than replacing it. The banks seeing the strongest results are asking how AI can help people do their jobs better, rather than whether it can do a person’s job. C&R Software, which is trusted by seven of the top 15 US banks, provides credit risk and collections technology that leverages AI to humanise collections and recovery.