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Three predictions for enterprise artificial intelligence in 2019
There’s no better way to describe how technology is disrupting work life…
Here are three key trends that will catalyze the rise of enterprise AI in 2019 and beyond: AI becomes ambient AI at work will soon become ambient –
In Georgia, for example, the country’s second-largest producer of chemicals for oil-based paint uses AI-based automation to manage HVAC settings and building access.
to answer three key questions: Just like even the most reputable lawyers and surgeons can’t make arbitrary decisions, so too will algorithms be expected to explain their behavior.
It’s called the “trolley problem” and it’s based on academic research from Philippa Foot in 1967: given control of a lever, would you choose to divert a runaway trolley from hitting five people if you knew pulling it would kill one person.
Most of the jobs created by AI-driven automation will involve data management, algorithm tuning, and most important, system configuration.
For example, when IT infrastructure owners use AI to assess the risk of upgrading the firmware in a router, they need the ability to set thresholds for risk and conduct scenario analyses based on upgrading different systems to different patch levels at different times.
According to a recent McKinsey study, 70% of companies will have adopted at least one AI solution by 2030 and those solutions will contribute up to $120B in global GDP.
Today, every enterprise leader faces pressure from customers and shareholders to define AI strategies that create value in ways that are fair, transparent, and predictable.
The Role Of AI In Customer Experience
In this post, I will lay out why artificial intelligence is a game changer in CX, take a look under the hood at how AI is applied to CX, and explore use cases for how leading edge companies are already reaping benefits from AI applications in customer experience.
Salespeople, call center agents and employees in other customer-facing roles cannot be expected to understand a customer’s entire history and derive their own insights from it in real time.
Customer journey analytics platforms provide this service for a fraction of the cost of the dedicated data services providers of yore—even delivering a level of data integration free of charge.
The truth is that, in addition to elegant SaaS data streams, most enterprises must rely on myriad on-site, home-grown and legacy touchpoint data sources—product interfaces, payment platforms, point-of-sale systems, customer care, etc..
Customer journey analytics platforms are now filling this gap with a host of APIs options and development kits to deliver comprehensive, real-time touchpoint integration with minimal investment.
By context, I mean more than simply designating a certain interaction as an “inbound call” and another as “order fulfillment.” AI must know the significance of these events in shaping a customer behavior.
That requires an awareness of both the journey that these touchpoints helped to shape and the KPIs which were subsequently impacted by that customer behavior—whether related to revenue, profitability, customer lifetime value, customer satisfaction or other factors driving high-level business performance.
With proper business context, an AI can find touchpoints and tactics which actually shape the customer behaviors behind the business’s primary measures of performance.
Now that we understand what it takes to successfully apply artificial intelligence in customer experience, let’s delve into some of those applications to see how AI is unleashing disruption across various aspects of customer experience by unifying data, providing insights in real-time, and incorporating critical business context.
As I previously mentioned, salespeople, call center agents and employees in other customer service roles cannot be expected to ingest and understand a customer’s entire history prior to each conversation.
Mining insights across billions of unique customer journeys using traditional analytics methods and tools is a laborious and slow process, which tends to confine it’s usage to a small set of pre-defined problems.
The power of AI-enabled customer journey analytics is that it can sift through a much, much larger and more complex data space and thereby uncover many more business opportunities—even opportunities you didn’t realize you should look for.
Artificial intelligence-enabled customer journey analytics can find answers to important CX queries like: Leading companies are constantly experimenting to determine the best way to employ AI to improve customer experience.
These companies have unified disparate customer data sources, analyzed end-to-end customer journeys and are using machine learning algorithms to predict future customer behavior.
Nordea also partnered with an AI-based text analytics solution provider to interpret hundreds of inbound customer communications per second and intelligently forward them to the right business unit.
Sephora has thoughtfully considered the entire customer journey—the Visual Artist tool ties in to Sephora’s entire inventory of products seamlessly, and driven by the AI engine, personalized recommendations and offers are made in real-time.
They use sophisticated analytics to glean insights from a customer’s purchasing history, and combine it with weather conditions and other relevant data to make product recommendations in real-time.
AI presents an opportunity to turn many-siloed, multi-channel enterprises into singular “personas” who remember, understand, and respond to their customers’ achievements and setbacks in a meaningful way.
Most companies find it difficult, if not impossible, to accomplish those tasks on their own, given the dearth of data scientists, the fact that disparate systems are not AI ready, and the need to rapidly build new systems, apps, and capabilities.