Generative artificial intelligence (AI) describes algorithms that can be used to create new content, including audio, code, images, text, simulations, and videos. Generative AI algorithms use machine learning models to predict the next word based on previous word sequences, or the next image based on words describing previous images. Generative AI tools can produce credible content in near real time, enabling organizations to produce content to better educate their stakeholders.
There are limitations and we are very early in understanding the effects of generative AI on businesses. The information produced can be wrong, full of biases and unethical, potentially exposing reputational and legal risks associated with the content. The source of initial data, the training models, and ethical and humane use of software development guidelines and governing principles can help mitigate risks associated with use of generative AI.
Research from CBInsights shows that 2022 was a record year for investment in generative AI startups, with equity funding topping $2.6B across 110 deals. The field of Generative AI is at the infancy stage. Among the 250+ generative AI companies identified, 33% have yet to raise any outside equity funding. Another 51% are Series A or earlier, highlighting the early-stage nature of the space.
Where is the investment in generative AI? Research shows that majority of investments are in visual media, with the largest investment categories being social media and marketing content, enterprise AI avatars, and cross-functional APIs. The other major category of investment is generative interfaces with sub-category investments in human-machine interfaces, general search and productivity, and knowledge management. These categories reveal the future benefits of generative AI in sales, services, marketing customer service, and e-commerce lines-of-business. So how can generative AI improve the employee and customer experience?
To better understand the impact on generative AI on improving the customer experience, I connected with one of the world's top customer service and experience management experts in the world. Michael Maoz is senior vice president of Innovation Strategy at Salesforce. Prior to joining Salesforce, Maoz was research vice president and distinguished analyst at Gartner, serving as the research leader for the customer service and support strategies area. His research focuses on customer strategies and technologies, with an emphasis on the CRM customer service disciplines, collaborative customer strategies, AI and Mobile strategies, and cloud-based CRM applications and analytics.
I asked Maoz to share his insights on generative AI's impact on improving the customer experience. Given his deep expertise covering AI and customer experience, Maoz provides an expert's point of view on how immerging technologies like generative AI can reduce time to value and scale differentiated capabilities to improve the overall stakeholder experiences. Here is a summary of my conversation with Michael Maoz:
Q: Why are we suddenly seeing a wave of hype around Generative AI? What is it, exactly, and why should we care?
A: We'll start from the last question and work back. Over the next five years, Generative AI will blend with traditional CRM to create a future of intuitive customer-business engagement that has never been possible before.
We have to take a step back to understand the 'why now' behind the hype. The AI technologies are confusing, and we should concede we do not understand all of the various forms of Artificial Intelligence. A quick recap of the major types helps.
Basic AI is inferential reasoning on a data set, regardless of size. It can perform any straightforward mathematical routine faster and more accurately than a human and work at all times. A developer can use this super-fast and precise ability and write applications such as calculating routes, or creating schedules, or measuring and predicting engine performance.
AI did not stop there. It then evolved to infer from the past how to act in the present, and continually improve based on each future interaction. An example of this is a self-driving vehicle. Its current ability is the result of hundreds of billions of previous calculations, and is continually improving. The same is happening with Amazon's Alexa and Apple's Siri.
The same principles are applied to understand what a person's emotions are at the moment based on AI analysis of voice, tone, intonation and changes in breathing patterns.
Now comes Generative AI. Artificial intelligence advanced on multiple fronts. Machine Learning added the capacity of software to learn on its own, and to be trained by humans or other software. Natural language processing adds the ability to generate text or an image based on text inputs. AI now can recognize an image, or speech, or movement.
Tied together and you have Generative AI to create art (think about the Cosmopolitan magazine cover last year), articles, video, and an entire conversation that AI can have with a human. There is a new burst of products and companies to perform these feats of AI magic, such as OpenAI's Dall-E 2 and ChatGPT, Google's Imagen Video, Stable Diffusion, and many more. These images and text are sufficiently advanced to convince a human that people and not computers create them.
To sum up: Generative AI has the possibility to change the game for business creativity and decision making, once it is harnessed to the business applications that underpin growth and efficiency.
Q: Why do you rank Generative AI, and AI overall, as less of a factor in corporate success? From the media attention, it seems like the next big thing.
A: We just looked at how AI has matured over the past five years, to a point where it will seep into every piece of software. In many ways it already has. There is one big inhibitor to be overcome in using the new technology. This is true whether you are in a business, Public Sector, healthcare or education, and that missing piece is tying advanced AI to Personalization.
AI is not what most influences business growth and customer retention. In every industry, marketers look at the dimensions that are most valued by the customer. In the airline industry, for example, these are often listed as the cost of the flight, the emotional value of the brand to the customer, the availability of flights that interest the customer, and the experience a traveler has in flight. Airlines use advertising, flight crew compensation, good customer service, and operational excellence to meet those customer expectations.
That does not mean that AI is not important. Once an organization understands the key dimensions of growth and customer loyalty, AI is introduced as something that will be embedded in business processes to make them more powerful. To return to our example, an airline can introduce a tool like Generative AI to personalize web experiences, video content, and messages to fit each customer. These new tools are not just for large enterprises. Take a young company like Runway that is democratizing content creation for web and social media channels.
The next step is for the enterprise to develop a plan to bring together the right team to blend Generative AI into existing customer experience programs.
Q: How do Marketing and classic CRM fit together with AI?
A: The dream of personalizing the customer experience is 30 years in the making. It was almost in 1993 when Don Peppers and Martha Rogers first published their visionary book, The One to One Future: Building Relationships One Customer at a Time. It was subtitled, Building relationships one customer at a time. They painted an amazing picture of the future of customer experience. The problem was that achieving their vision was technically impossible for companies to do at scale. There was no way to treat each customer in a personalized way on their preferred communication channel using reliable real-time data. As digital channels continue to expand to include TikTok, WhatsApp, websites, devices, and mobile apps, it is a massive challenge to synchronize them atop of traditional channels, and to bring together the right customer data and business rules.
It's audacious what Peppers and Rogers imagined the future could look like. Keep in mind that 1993 was the same year that Siebel Systems was founded, with a product line limited at the time to automation tools for salespeople.
Peppers and Rogers listed simple steps to get to this one to one future. It was the execution that was --- and still is -- difficult at scale. They pointed out the need to collect all relevant data about the customers' preferences. These are the timeless questions about their expectations, and their perception of the current relationship with the brand, and how they wanted to be treated, and how they have been treated. They really set the bar for what the future could look like, and it has taken us 30 years to build the technology to achieve their vision.
Q: Looking past the challenges inherent in introducing Generative AI, what is the big opportunity for businesses to get this right?
A: Where this gets incredibly exciting is how Generative AI converges with emerging ways of managing data, and how that links to the future of CRM and engaging customers. It opens up enormous opportunities.
Until now it has not been possible to turn the idea into action. The notion that a company can collect this data, prioritize it for each customer, and for multiple customer segments, and then engage customers in a customized way in real time based on the analysis of this information on the customer's preferred channel is only now becoming a reality.
A business can think of this as a Customer Data Cloud. Until now, it was impossible to connect all of the customer data at scale. It came in too many formats, from too many devices and applications and systems. There was also too much data for a business with a large customer base. The data management alone required dozens of data scientists, plus custom built connectors. At Salesforce where I work, we have changed all of that with the advent of Salesforce Genie Customer Data Cloud. We have connected the customer data, harmonized it into a customer graph, and made it available to all departments in the organization. The result is the foundation for a personalized customer experience.
It looks like the problem of a unified view of the customer should be enough for a great customer experience. That still does not answer the question: "What does Generative AI add?"
Q: To wrap up, what do we recommend that businesses do to accelerate their use of new AI technologies?
A: AI initiatives have resulted in gains for some companies, though they are in the minority. For the other 99% of organizations, AI projects are small and tactical and focused on analyzing and optimizing business patterns and processes. Through 2025, Generative AI, which we defined roughly as the ability of language models to create things -- images, code, language transcription -- will be an added IT component, the impact of which is as of yet unclear. There is some practical advice on how to move ahead.
The first step is to make it clear that AI, like automation and analytics, is meant to make the lives of people easier. There will be profit, but it is not about profit over people. There is a good deal of anxiety about AI, and not as we might think. Much of it is around the effect of AI in the workplace. It is replacing all non-complex activities. Some activities look complex, but they are not so much complex as requiring many steps and advanced reasoning and calculations. Activities like completing forms, assessing contracts, forecasting, ordering materials, and others already mentioned, are math problems, and perfect for AI. What this leaves employees for work is one of two scenarios: Either they are performing menial tasks that are too expensive to build AI and robotics for, or knowledge workers with a constant stream of highly complex challenges with no single correct answer.
Unless AI is deployed thoughtfully, the workers of the future will either be poorly paid and bored, or well compensated and stressed. How will we engineer dignity and happiness in the AI future?
Where does this leave an employee at work? Mostly spending more of their time assigned complex tasks that require higher-order analysis of situations that have no clear resolution. The situation will lead to anxiety and fatigue.
The only way to turn AI into a net-positive is to make it work as a support tool that makes the life of the employee more productive and rewarding. Be creative and create a short list of the top processes that you would like your organization to do better, and see if AI offers an answer. Here are a few examples:
Create personalized sales presentations and demos.
Create personalized marketing campaigns and content in real time.
Deploy Web 3.0 dynamically customized web experiences that evolve with customer engagement.
Refine customer service to pinpoint information of high value to customers and deliver it proactively on their preferred channel.
Detect problems before they arise -- delayed delivery, software glitch, engine wear, insufficient funds -- and create solutions that work for the customer.
Give agents and customers the mutual understanding of a situation and suggest the next best action to take.
The advice is for business leaders to educate themselves on the implications of AI. They need to understand not just the technology, but the impact on existing processes and in turn the impact on the culture of the enterprise. Every business leader wants to have a trusted enterprise.
So, the advice is: To build a Trusted Enterprise with a strong AI footprint, express AI programs in terms that better the lives of employees and customers. Done correctly – following ethical and humane use guidelines to reduce risks associated AI technologies – Generative AI baked into customer processes will improve customer experience and create unique new moments for success. Proceed with caution, taking small steps into the AI future.
Maoz reminds us that the combination of AI technologies, automation at scale and real-time data analytics, visualization and reporting are key to improving the customer experience.
This article was co-authored by Michael Maoz, Senior Vice President, Innovation Strategy, Salesforce.