Generative AI assistants have arrived, but adoption, and value are TBD.

If there was a “term of the year” award for the communications, collaboration, and customer experience space, “generative AI” would win for 2023 in a landslide. This past year saw a slew of gen AI announcements from just about every vendor in the market. But, as we hit the peak of the hype curve, there are very real questions about gen AI heading into 2024, answers to which will determine if the hype translates into actual adoption and customer benefit.

WILL COMPANIES ADOPT IT?

Metrigy’s Customer Experience Optimization: 2023-24 – Consumer Perspective study earlier this year of ~500 people in the United States found widespread awareness of generative AI but significant concerns related to privacy, security, and trust. Adoption plans were limited, with just 19.7% saying they would use generative AI for content creation and meeting/chat summarization if available. 16.1% said they would not use it. Almost 41% of participants said that for them to trust gen AI, the technology would require human oversight of content creation as well as limitations on how the tools collect data. Nearly 32% say they will never trust it.

Specific concerns include accuracy of generative AI responses, security of corporate information that may be available to large language models, and privacy of information captured by generative AI tools such as meeting transcripts and interaction critiques.

WHAT’S THE ROI?

As companies get their hands on generative AI assistants they are beginning to focus on ROI, especially when evaluating vendors who charge for AI assistants. We’ve seen in our research over the years that ROI for investments in collaboration applications and features is often difficult to quantify. Some companies look at metrics such as average time spent on repeatable processes to see if they can identify reductions. Other areas may include overall revenue, reduction in costs or staff time, or metrics specific to customer interactions such as first call resolution and customer satisfaction scores.

As generative AI assistants go beyond meeting and chat summarization and transcription, identifying ROI should get easier. For example, employees can identify improvements in data analysis and speed of content creation. IT staff can measure improvements in mean-time-to-repair (MTTR) and administrative tasks from the use of generative AI tools for application and network management.

Even for those adopting free tools there will be some associated costs in terms of user and IT training, IT support, and administration, and to meet security, governance, and compliance requirements for generated content such as meeting transcripts or chat summaries.

DOES IT WORK?

Continue reading at nojitter.com.

Business leaders and consumers are understandably reluctant to embrace AI right now – but research shows they don’t see federal oversight as the response to their concerns about the new technology.

With only 25% of business leaders and 13% of consumers fully trusting generative AI, the Biden Administration’s Executive Order governing the development and use of AI may appear to be a welcome development.

However, according to Metrigy’s Customer Experience Optimization 2023-24 global study of 641 companies, along with the Consumer Perspectives study of 502 consumers, government oversight is not viewed as a panacea by the majority of business leaders and consumers. In fact, only 30% of business leaders and 22% of consumers surveyed say government regulations would make them trust generative AI more. (Although the EO does apply to any type of AI, the rapid speed of generative AI’s adoption and capabilities presumably delivered incentive for the administration’s actions.)

IT, customer experience (CX), and business unit leaders see other areas as more effective at increasing their trust of AI. They include limiting the data AI can use to create content (56%) and human oversight of content creation (51%). Consumers agree on the latter (41%), along with limitations on AI’s capabilities (39%). But in the end, consumers are four times more likely to never fully trust AI than IT, CX, or business unit leaders, so efforts to placate consumers are paramount.

During our interviews with research participants, most said they would prefer AI be managed by a non-government-run standards body, including tech vendors, leaders in academia, researchers, IT leaders, and government officials that would jointly address areas of concern. They said standards bodies would be more efficient than a government task force or agency.

CHALLENGES OF THE EO

So does an executive order provide value? It may help, if executed properly, but it won’t eliminate risks. And it could potentially do more harm than good by stifling U.S. innovation. Falling behind other countries on AI development because of government bureaucracy would put the country at a disadvantage in a variety of areas, including defense, healthcare, energy, and technology innovation.

Continue reading at nojitter.com.

Can AI both increase profits and reduce the traditional 40-hour work week?

Many times, I’ve pondered why the full-time work-week is defined as five days on, two days off. Why not four days on, three days off, or some other ratio that gives us more time to enjoy our lives?

In the businesses I’ve run, I have always focused on output rather than the number of hours someone works. If two people have a job to finish in a day—say, write a report—and it takes one person four hours and the other eight hours, shouldn’t the person who completed the task in four hours be able to lay low the rest of the day? Work hours vs. productivity is always an interesting debate.

Let’s take it to a contact center. If an outbound sales rep can close 30% over a five-day quota in three days, should that person get the rest of the week off? In our workaholic world, the answer is typically “No! Keep going and get to 50% above quota!” When employees are super-productive, we just keep raising the bar. And at what expense? Employees are stressed, tired, and anxiety-ridden because they spend too much time working and not enough time enjoying life.

Can we start hiring and managing based on productivity vs. hours?

Yes, and generative AI may be able to help us do just that.

Because of its speed and massive scale, generative AI has the potential to reduce the number of hours required for a full-time workweek for many types of jobs. Generative and all types of AI improve business metrics—specifically productivity, but also revenue and customer satisfaction. The most successful companies using any type of AI in CX initiatives reported revenue increase by 38%, customer ratings improve by 45.8%, operational costs drop by 5.6% and agent efficiency rise by 36%, according to the same study.

Continue reading at nojitter.com.

AI has become as confusing as it is exciting. With so many options for applying AI to customer interactions, how do you know what to prioritize, and where you’ll get the biggest bang for the buck? And how can you prepare for the future as AI continuously evolves? It’s imperative to be both receptive to technology advancements and also to be innovative for your own use cases.

This webinar will give you strategic guidance, based on real-world research data from Metrigy CEO & Principal Analyst Robin Gareiss. You’ll also hear from NICE Director of Product and Segment Marketing Brian Mistretta on how NICE customers are leveraging AI today. We’ll share insights and recommendations, and answer the following questions:

How to Prioritize AI Now to Drive Operational Success—and Prepare for the Future

Presented by: Robin Gareiss, CEO & Principal Analyst, Metrigy
Brian Mistretta, Director of Product and Segment Marketing, NICE

Date: December 5, 2023
Time: 11:00 am ET

VIEW REPLAY

Generative AI is moving from concept to reality. A proactive approach is required to ensure success.

Generative AI has captured technology media headlines since the launch of ChatGPT nearly a year ago. The appeal of GenAI is that it can replace mundane tasks and improve productivity and efficiency; the concern is that this technology could be so disruptive, it replaces human employees entirely.

Even as we all sift through the benefits and threats that GenAI presents, communications and collaboration vendors have rushed to bring GenAI capabilities to their platforms, in some cases announcing previews before they were fully available. In the last several months the GenAI space has accelerated. Here are some of the products that have been announced or released:

These recent announcements follow releases, previews, and roadmaps from just about every other collaboration provider from Cisco to Dialpad, Notion to RingCentral, and more. GenAI capabilities range from assistants for content creation; auto-transcription, summarization, and action item identification in meetings and phone calls; and real-time and after-call coaching and feedback. Tomorrow, capabilities will continue to improve and expand.

As GenAI moves from preview to reality, IT leaders are faced with the challenge of understanding the value, as well as assessing the potential risk, and determining when and how to deploy GenAI capabilities to their workforce.

Key concerns these leaders should consider include:

COST

Continue reading at nojitter.com.

Sam Wilson, formerly CFO, CCO, and interim CEO, at 8×8, was recently named CEO. In this episode, we’ll talk to Sam to learn how his experience has prepared him for this role, and what 8×8’s strategy is to differentiate from competitors and grow revenue.

Chatbots only resolve 16% of customer interactions, on average.

This is the last of a three-part series examining how companies are transforming their customer experience strategies through the use of technology. The first article in the series is about the use of all types of AI in the contact center while the second article discusses the potential and current usage of generative AI.

When customer experience (CX) leaders or consumers talk about AI in customer service, chatbots are at the forefront of the discussion. But chatbots aren’t producing the success metrics they should be—at least not yet.

Chatbots and voicebots interact with customers on websites, in self-service channels, over SMS, or on phone calls. Chatbots are software automation programs that may or may not use AI, but nowadays, most companies opt for AI to interpret information, boost productivity, and even make recommendations that drive revenue. Chatbots can respond using decision trees or pre-programmed information (non-AI), or they can leverage machine learning to generate responses that adapt to the situation, content, or requests.

Semantics is an issue with this technology. As AI comes into the picture, some refer to the chatbots as “conversational AI,” where natural language processing, sentiment analysis, and other forms of AI help enable the chatbots to be more intelligent—helping customers solve a problem or complete a task. Virtual assistants are another term used in the context of chatbots. Alexa and Siri are familiar personal virtual assistants, helping with reminders and automating tasks. Virtual assistants in the contact center are sophisticated chatbots that use conversational AI, engage in human-like conversations, and assist both agents and customers.

Use of Chatbots

Companies use chatbots, or their more sophisticated versions, for several purposes. In some cases, they act independently and don’t assist live agents; in others, they do.

Internally, they can confirm agents have read the required statements and are being friendly. They also can provide context to help live agents resolve issues. They can close sales without agent involvement, or they can help the agents close sales through making next-best-action recommendations. They’re also starting to verify customer identity, saving agents time during a live interaction.

Externally, they are gathering feedback from customers after an interaction. They’re also helping customers in self-service channels by guiding them to the right spot on a website, providing product information, or completing warranty claims. They also can triage issues at hand and route to the best live agent.

Impact of Chatbots

Continue reading at nojitter.com.

But trust concerns of the technology behind it is widespread.

This is the second of a three-part series examining how companies are transforming their customer experience strategies through the use of technology. The first article in the series is about the use of all types of AI in the contact center.

In all my decades covering technology, I haven’t seen one catch on with the speed of generative AI. Already, 27.3% of companies are using the technology for their customer-related activities, with another 47.2% planning to do so this year, according to Metrigy’s Customer Experience Optimization:2034-24 global study of 641 companies.

Generative AI is a category of techniques and models that respond to natural language prompts to produce text, images, audio, software code, or other media from data on which they’ve been trained. Examples of generative AI, also known as Large Language Models (LLMs), include Open AI ChatGPT, Google Bard, Microsoft Copilot, and many others.

Potential Uses of Generative AI

The biggest value of generative AI is to automate tasks—keeping in mind that the algorithms are trained on language, not truth, so the ability to put guardrails around data becomes vital to the success of generative AI in business. How can businesses and consumers use the technology?

Current Uses of Generative AI

Continue reading at nojitter.com.

Even as CX operations become steeped in advanced technologies such as AI and automation, the agent’s role is far from diminishing. In fact, offloading routine customer inquiries and tasks to AI and automation raises the bar for agents. Moving forward, the best agents need to be more highly skilled than ever before, capable of handling the most complex customer service issues quickly and intelligently. Preparing agents for this new reality requires a next-generation approach to workforce engagement management (WEM), one that provides generative AI-driven conversational assistance plus automation to deliver smart guidance for interactions, personalized coaching, and automated tasks.

In this webinar, get a status report on the agent experience based on Metrigy’s latest CX research, and discover the role AI, including conversational generative AI, and automation are playing today.

Getting CX Complexity Under Control for Agents with WEM & Generative AI

Presented by: Beth Schultz, Vice President of Research and Principal Analyst, Metrigy
Dana Shalev, WEM LOB Head of Product Marketing, NICE

Date: September 13, 2023
Time: 11:00 am ET

VIEW REPLAY