Artificial Intelligence is transforming industries. It’s streamlining operations, creating new possibilities. The shift is no longer theoretical, even in the mortgage industry; it’s now unfolding in real time across organisations seeking to harness its potential.
A key starting point for many is the deployment of virtual assistants, which offer a practical entry into AI’s capabilities. By way of example, we’ve just debuted an HR chatbot designed for our employees. This is not something we’re selling – it addresses routine inquiries about policies such as overtime, sickness or carer’s leave in seconds, providing 24/7 access to information that might otherwise be buried in complex systems. It can be accessed at a desk or on a mobile device, via Teams. Early results often show reduced response times and increased satisfaction, prompting exploration into broader applications like supporting a wider range of queries.
The appeal of starting with chatbots lies in their simplicity. They are easy to deploy, scalable and yield immediate benefits. Rather than attempting to overhaul entire systems at once, they serve as a manageable first step, revealing where AI can add value day-to-day. This phased approach avoids overambition, allowing organisations to build experience and refine their strategies.
Yet the road implementation has not been without its metaphorical potholes. Training AI to align with specific needs can be tricky. For instance, an HR bot might fail to recognise ‘sickness’ if internal documents use ‘absence’ instead. Bridging such gaps requires focused efforts to ensure the technology stays on message. Occasionally, it may need reining in, such as when it attempts to answer abstract questions like “How many stars are in the sky?” The lesson is clear: AI must stay tethered to practical outcomes.
This isn’t about replacing humans; it’s about enhancing their work. A chatbot might handle 90-95% of a routine task, freeing experts to focus on higher-value contributions. This balance reflects a broader truth: AI is not a panacea for all your business’s ills. Its strength lies in amplifying human effort when applied with care, not in delivering standalone solutions.
The potential extends beyond virtual assistants: voice analytics and transcription AI is the next practical step in the evolution of AI beyond chatbots. In sectors where calls are recorded, such as financial services, AI can transcribe conversations, summarise key points and detect sentiment or keywords to identify vulnerable customers. Far from merely documenting, it acts as a real-time aid, suggesting questions to ensure fair treatment. Looking ahead, Generative AI opens further possibilities, from drafting reports to supporting personal development and testing processes.
Evidence supports this momentum. Research from McKinsey suggests up to 30% of tasks in most jobs can be automated, saving time and reducing costs. AI can also reveal insights humans might overlook, sharpening decision-making. A striking example is Google’s ‘co-scientist’ tool, which solved a microbiological challenge in two days that had stumped experts for a decade. Additionally, AI’s ability to personalise experiences, from marketing to customer interactions, is well-documented.
Success, however, depends on execution. The devil is in the detail. The journey involves grappling with integration challenges, refining training data and learning from each step. This hands-on process contrasts with theoretical promises, grounding AI’s adoption in tangible results. It’s not about enthusiasm alone; it’s about solutions that work today and scale tomorrow.
As AI’s role grows, sharing lessons learned, including the setbacks, fosters its credibility. The shift from chatbots to broader capabilities is a journey of discovery, not a race to perfection. In a landscape often shrouded in the mists of hype, the focus must remain on pragmatic steps that deliver measurable value – proving AI’s worth one application at a time.
