Self-driving cars. AI-powered supermarkets. Google Duplex. It’s easy to get excited about the many ways that artificial intelligence can potentially make our lives more convenient. Add the label “AI-powered” to any industry and it instantly sounds innovative and compelling.
But the truth is AI is already transforming industries in incremental ways. Advances in AI, machine learning, and predictive analytics are being used to automate, optimize, and streamline more areas of business. Also, just because something can be automated or powered by artificial intelligence, doesn’t mean it should be.
Invisible AI aids contact center transformation
The way in which AI and machine learning are deployed is as important as what is being developed. In the contact center, the next level of sophistication is not replacing associates with robots, but rather, leveraging AI in real-time conversations to help associates provide better customer care.
Contact centers are deploying AI technology to “listen” and analyze the sentiment of live customer interactions. If a conversation is going poorly, e.g., the caller is aggravated and speaking harshly, AI may offer suggestions to resolve the issue, or alert a manager. In this situation, AI helps associates be more successful and productive in their jobs.
This occurring in the contact center and elsewhere. Gartner estimates that by 2022, one in five workers will rely on AI to do their jobs. “AI will electrify the productivity tools these workers use, bringing them to another level of power and accuracy that become part of the way they work and redefine expectations…and the level of quality they can produce,” writes Craig Roth, research VP at Gartner, in a blog post. “At that point, taking away their AI would be like a craftsman having their electric woodworking tools replaced with manual ones.”
For instance, AI can assist IT and cybersecurity teams by looking for patterns in how data in the cloud is accessed to report anomalies and predict security breaches. In manufacturing, networked devices are being used to capture data from the supply chain as well as product design, production, development, and delivery to optimize workflows and increase efficiency. In finance, investors are using AI tools to analyze historical trends and predict when to buy and sell securities and execute trades.
To automate or not to automate CX
As more companies invest in AI technology and resources, it’s less a question of how, but when to automate more parts of the organization. This can be a risky approach if companies fail to consider other factors, such as how it affects the customer relationship.
Executives should think carefully about where they do or don’t want to automate their customer interactions. What’s more, the goal should not be to interact as often as possible, but only in valuable situations. Having a conversation with a customer who just wants to reset a password is not too valuable.
However, when customers are at a critical part of the customer journey, whether it’s negative or positive, the last thing a company should do is automate the interaction. Instead, organizations should enhance the interaction with AI and help the associate be more informed and/or empathetic.
Consider retention. It costs five times more to acquire a new customer than to keep an existing one, which is why savvy businesses focus on building a loyal relationship with customers, in addition to acquiring new ones.
By processing and analyzing CRM data, contracts, and other data points, predictive AI tools can identify the customers most at risk of leaving and alert associates to act before it’s too late. And by building on past results, prescriptive systems can pull insights from predictive functions to offer specific, personalized actions at key decision points.
For example, if an associate was alerted that a valuable client was at risk of churning, a prescriptive function can inform the associate not only who to contact and why but also how, based on similar situations.
When speaking with the high-risk customer, the associate may be encouraged to waive a fee or mention a special promotion for a relevant product or service as a way of thanking the customer for his or her continued business. Or perhaps the associate picks up cues from the conversation and offers a different deal. The AI support helps associates frame the conversation, but a successful interaction also relies on the innate human traits of critical thinking, creativity, and emotional intelligence.
Or, if something goes wrong and a product or service malfunctions it isn’t enough for the company to apologize; the goal should be to solve for the customer’s immediate problem, as well as identify the cause of the issue. AI can play a key role here in searching for anomalies and containing the issue on the backend while employees communicate with the customer and maintain the relationship.
Studies have shown that while most customer relationships start off as purely transactional, the quality of their experience determines if they will return. Every interaction point leaves an impression of that business. And companies that seek to automate as much of the customer relationship as possible to reduce costs and increase efficiency may lose out to competitors that maintain a human touch.
There’s also a common misconception about the path to maturity. Whether it’s to beat competitors, justify spend, or simply impatience—many companies want to skip the necessary building blocks of technology implementation and go straight to the more complex and impressive outcomes. Instead of chasing the latest and greatest product rollout, a smarter approach is to ask, “What’s my business, where does the technology fit, how will I prove it out, and who do I prove it out with?”
As for measuring the results, start with routine, transactional metrics that are easy to analyze and replicate. Once it’s clear that automating certain areas adds value, the next step is to ask what are other use cases that would make the technology even more valuable. The idea is to continue adding use cases until it becomes impactful.
What’s next for AI: IoT
If companies need more proof that mastering the fundamentals of AI is critical for business, consider the Internet of Things (IoT). We’re rapidly moving into a world where sensors, database functions, analytics, and communication protocols are all part of an ecosystem of interconnected services that optimize human interactions and business processes.
Data orchestration, defining how data flows among devices, applications, and data services, etc., is essential in an IoT ecosystem. And what we’re hearing from clients that represent different industries is that companies are looking to orchestrate those IoT actions using AI and automation. Simply put, the companies that aren’t ready to leverage AI will be left behind.
AI experience at a tipping point
AI represents a potential threshold moment for businesses and people where advances in technology can significantly impact our personal lives, in addition to business operations and the way decisions are made.
And these changes are already happening. Although AI is eliminating some jobs, it can also be an invaluable tool that makes employees more effective. Whether companies find value in AI depends on whether they think critically about where it fits in their plans and goals, and not the other way around.