Talla Company Blog

Something we talk about on AI at Work is a debate around whether AI is a fundamentally different type of technology from the viewpoint of competitive dynamics. Previously, when a new software was launched, it might have made sense to wait until a few rounds of updates had passed and all of the bugs had been worked out before adopting the new technology. Waiting to implement the newer advances wouldn’t mean you would be left behind or at a disadvantage compared to those who had embraced them early on.

Artificial intelligence is powered by well annotated training data, but the data you need to train a customer support assistant includes more information than you might expect. The "tribal knowledge" of your customer support teams is critical to developing a customer service AI agent that can most effectively help your business.

We've all had interactions with clunky or incoherent chatbots that made their owners look dumb, but few companies know what steps to take to ensure their own bots don't make the same mistakes. Before you entrust a chatbot to help with customer service and support, make sure you've given it the tools it needs to avoid embarrassing itself.

Previously, we shared five things support leaders must do when bringing automation into their business. But modernizing support also means avoiding certain pitfalls. By building awareness of these, leaders improve their chance of a successful transformation initiative. They’ll reduce inefficiencies and get the most value out of their people, dollars and customer interactions.

When you’re investing, knowing the forecast of future trends is essential. On Episode 28 of AI at Work, James Cham of Bloomberg Beta joins us to share four top insights on investing in the future of work.

We’ll cover how the tech business model landscape may soon be shifting in the wake of AI, strategies for adopting AI and emerging as an AI leader, and a glimpse into the paradox of why the real experts on automation m ay not be willing to share their secrets, yet.

Last month, we were excited to announce Talla’s Learning Dashboard, a report-card-like view to show Talla’s learning. Our customers are loving it, and today we’re going to take a look at some of their favorite things that the learning dashboard measures.

Automation is not a necessary evil of scale. It doesn’t signal the end of great human service or the sad inevitability of technology’s takeover. Widespread adoption actually proves the opposite: as customer expectations evolve, innovations improve and agents’ skills expand, affordable, quality service at scale becomes a reality.

As AI briskly disrupts today’s world of goods and services, some people lose their jobs to the process of automation. Yet paradoxically, as we become increasingly data-driven, more and more opportunities in the data space are forming at the same time as other jobs are disappearing.

Name a business problem and chances are there is an automation tool to solve it. With billions in R&D and VC capital deployed, it’s the latest innovation pocket to pack a transformational punch.

No technology is a panacea. But some, when applied in ripe industries and ideal use cases, creates an advantage. And in the digital age of customer support, automation offers just that.

Defined simply, automation is the organization of systems and process workflows using digital technology. These technologies incorporate tools that reduce manual involvement in repeatable and predictable activities. Available tools vary in terms of their “intelligence” – some are pre-programmed and highly-rules driven. These types rely on connections to various systems, a predictable set of task requirements and perform repetitive, low value work at scale. Others are designed to mimic human thinking, learning and adaptability by generating meaningful insights as tasks are completed and data is processed.

In the world of financial services, introducing machine learning applications can offer soaring profits. With an abundant diversity of techniques available, a common mistake some companies make is failing to ask “are those techniques giving me the right answer?” Choosing the wrong explainability method can get you the wrong answer, and in the business of financial services, this can be highly problematic.