Scaling customer experiences with data and AI

Today, interactions matter more than ever. According to data compiled by NICE, once a consumer makes a buying decision for a product or service, 80% of their decision to keep doing business with that brand hinges on the quality of their customer service experience, according to NICE research. Enter AI.

“I think AI is becoming a really integral part of every business today because it is finding that sweet spot in allowing businesses to grow while finding key efficiencies to manage that bottom line and really do that at scale,” says vice president of product marketing at NICE, Andy Traba.

When many think of AI and customer experiences, chatbots that give customers more headaches than help often come to mind. However, emerging AI use cases are enabling greater efficiencies than ever. From sentiment analysis to co-pilots to integration throughout the entire customer journey, the evolving era of AI is reducing friction and building better relationships between enterprises and both their employees and customers.

“When we think about bolstering AI capabilities, it’s really about getting the right data to train my models on so that they have those best outcomes.”

Deploying any technology requires a delicate balance between delivering quality solutions without compromising the bottom line. AI integration offers investment returns by scaling customer and employee capabilities, automating tedious and redundant tasks, and offering consistent experiences based on collected and specialized data.

“I think as you’re hopefully venturing into leveraging AI more to improve your business, the key recommendation I would provide is just to focus on those crystal clear high-probability use cases and get those early wins and then reinvest back into the business,” says Traba.

While artificial intelligence has increasingly grabbed headlines in recent years, augmented intelligence—where AI tools are used to enhance human capabilities rather than automate them—is worthy of similar buzz for its potential in the customer experience space, says Traba.

Currently, the customer experience landscape is highly reactive. Looking ahead, Traba foresees a shift to proactive and predictive customer experiences that blend both AI and augmented intelligence. Say a customer’s device is reaching its end-of-life state. Rather than the customer reaching out to a chatbot or contact center, AI tools would flag the device’s issue early and direct the customer to a live chat with a representative, offering both the efficiency of automation and personalized help from a human representative.

“Where I see the future evolving in terms of customer experiences, is being much more proactive with the convergence of data, these advancements of technology, and certainly generative AI,” says Traba.

This episode of Business Lab is produced in partnership with NICE.

Full Transcript

Laurel Ruma: From MIT Technology Review, I’m Laurel Ruma and this is Business Lab, the show that helps business leaders make sense of new technologies coming out of the lab and into the marketplace.

Our topic is building better customer and employee experiences with artificial intelligence. Integrating data and AI solutions into everyday business can help provide insights, create efficiencies, and free up time for employees to work on more complicated issues. And all of this builds a better experience for customers.

Two words for you: augmented intelligence.

My guest is Andy Traba, vice president of product marketing at NICE.

This podcast is produced in partnership with NICE.

Welcome Andy.

Andy Traba: Hi Laurel. Thanks for having me.

Laurel: Well, thanks for being here. So to set some context, could you describe the current state of AI within customer experience? Common use cases that come to mind are chatbots, but what are some other applications for AI in this space?

Andy: Thank you. I think it’s a great question to get started, and I think first and foremost, the use of AI is growing everywhere. Certainly, we had this big boom last year where everybody started talking about AI thanks to ChatGPT and a lot of the advancements with generative AI, and we’re certainly seeing a lot more doing now, moving beyond just talking. So just growing a use case of trying to apply AI everywhere to improve experiences. One of the more popular ones, and this technology has been around for some time, is sentiment analysis. So instead of just proactively surveying customers to ask how are they doing, what was their experience like, using AI models to analyze the conversations they’re having with brands and automatically determine that. And it’s also a good use case, I think, to emphasize the importance of data that goes into the training of AI models.

As you think about sentiment analysis, you want to train those models based on the actual customer experience conversations, maybe past records or even surveys. What you want to avoid is training a sentiment model maybe based on movie reviews or Amazon reviews, something that’s not really well connected. So certainly sentiment analysis is a very popular use case that goes beyond just chatbots.

Two other ones I’ll bring up are co-pilots. We’ve seen, certainly, a lot of recent news with the launch of Microsoft Copilot and other forms for copilots within the contact center and certainly helping customer service agents. It’s a very popular use case that we see. The reason driving that demand is the types of conversations that are getting to agents today are much more complex. AI has done a good job of taking away the easy stuff. We no longer have to call into a contact center to reset our passwords, so what’s left over for the agents is much more difficult types of interactions.So being able to assist them in real time with prompts and guidance and recommending knowledge articles to make their job easier and more effective is really popular.

And then the third and final one just on this question is the really kind of rise of AI-driven journeys. Many, many years ago, you and I would call into a contact center, and the only channel we could use was voice. Today, those channels have exploded. There’s social media, there’s messaging, there’s voice, there’s AI assistance that we can chat with. So being able to orchestrate or navigate a customer effectively through that journey and recommend the next best action or the next best channel for them to reduce that complexity is really in demand as well. And how can I even get to a point where I can proactively engage with them on the channel of their choice at the time of day that we’re likely to get a response is certainly an area that we see AI playing an important role today, but even more so in the future those three really sentiment analysis, the rise of co-pilots and then using AI across the entire customer journey.

Laurel: So as AI becomes more popular across enterprises and across industries, why is integrating AI and customer experience then so crucial for today’s business landscape?

Andy: I think it’s so crucial today because it’s finding this sweet spot in terms of business decision-making. When we think of business decision-making, we are often challenged with, am I going to focus on revenue or cost cutting? Am I going to focus on building new products or perfecting my existing products? And rarely has there been a technology that has allowed a business to achieve all of those at once. But we’re seeing that today with AI finding a sweet spot where I can improve revenue and keep customers happy and renewing or even gain new ones without having to spend additional money. I could even do that in a more efficient way with AI. Within AI, I can take a very innovative approach and produce new products that my customers demand and save time and money through efficiencies in making my current products better. I think AI is becoming a really integral part of every business today because it is finding that sweet spot in allowing businesses to grow while finding key efficiencies to manage that bottom line and really do that at scale.

Laurel: And speaking of those efficiencies, employee experience lays that foundation for the customer. But based on your time at NICE and within business operations, how does employee experience affect the overall experience then for customers?

Andy: I think what we’ve seen at NICE is really that customer experience and employee experience are hand in glove. They’re one and the same. They have tremendous correlation between each other. Some examples, just to give some anecdotes, and this is customer experience really happening everywhere. If you go into a car dealership for a Tesla or a BMW, a high-end product, but you are interacting with a salesperson who’s a little pushy or maybe just having a bad day, it’s going to deteriorate the overall customer experience, so that bad employee experience causes a negative effect. Same thing if you go to your favorite local restaurant, but you maybe have a new server who’s not really well-trained or is still figuring out the menu and the logistics that’s going to have a negative spillover effect. And then even on the flip side of that, you can see employee experience having a positive effect on their overall customer experience.

If employees are engaged and they have the right information and the right tools, they can turn a negative into a positive. Think of airlines, a very commoditized industry right now, but if you have a problem with your flight and it got canceled and you have a critical moment of need, that employee from that airline could really turn that experience around by finding a new flight, booking you, making sure that you are on your trip and meeting your destination on time or without very little delay. So I think when we think about experiences at large and the employee and the customer outcomes are very much tied together, we’ve done research here at NICE on this exact topic, and what we found was once a consumer makes a buying decision for a particular product or service, after that point, 80% of that consumer’s decision to continue doing business with that brand is based on the quality of their interactions.

So how those conversations play out, plays a very, very important part of whether or not they will continue doing business with that brand. Today, interactions matter more than ever. To conclude on this question, one of my favorite quotes, customer experience today isn’t just part of the business, it is the business. And I think employees play a really important front role in achieving that.

Laurel: That certainly makes sense. 80% is a huge number, and I think of that in my own experiences, but could you explain the difference between artificial intelligence and augmented intelligence and also how they overlap?

Andy: Yeah, it’s a great question. I think today artificial intelligence is certainly capturing all of the buzz, but what I think is just as buzzworthy is augmented intelligence. So let’s start by defining the two. So artificial intelligence refers to machines mimicking human cognition. And when we think about customer experience, there’s really no better example of that than chatbots or virtual assistants. Technology that allows you to interact with the brand 365 24/7 at any time that you need, and it’s mimicking the conversations that you would normally have with a live human customer service representative. Augmented intelligence on the other hand, is really about AI enhancing human capabilities, increasing the cognitive load of an individual, allowing them to do more with less, saving them time. I think in the domain of customer experience, co-pilots are becoming a very popular example here. How can co-pilots make recommendations, generate responses, automate a lot of the mundane tasks that humans just don’t like to do and frankly aren’t good at?

So I think there’s a clear distinction then between artificial intelligence, really those machines taking on the human capabilities 100% versus augmented, not replacing humans, but lifting them up, allowing them to do more. And where there’s overlap, and I think we’re going to see this trend really start accelerating in the years to come in customer experiences is the blend between those two as we’re interacting with a brand. And what I mean by that is maybe starting out by having a conversation with an intelligent virtual agent, a chatbot, and then seamlessly blending into a human live customer representative to play a specialized role. So maybe as I’m researching a new product to buy such as a cell phone online, I can be able to ask the chatbot some questions and it’s referring to its knowledge base and its past interactions to answer those. But when it’s time to ask a very specific question, I might be elevated to a customer service representative for that brand, just might choose to say, “Hey, when it’s time to buy, I want to ensure you’re speaking to a live individual.” So I think there’s going to be a blend or a continuum, if you will, of these types of interactions you have. And I think we’re going to get to a point where very soon we might not even know is it a human on the other end of that digital interaction or just a machine chatting back and forth? But I think those two concepts, artificial intelligence and augmented intelligence are certainly here to stay and driving improvements in customer experience at scale with brands.

Laurel: Well, there’s the customer journey, but then there’s also the AI journey, and most of those journeys start with data. So internally, what is the process of bolstering AI capabilities in terms of data, and how does data play a role in enhancing both employee and customer experiences?

Andy: I think in today’s age, it’s common understanding really that AI is only as good as the data it’s trained on. Quick anecdote, if I’m an AI engineer and I’m trying to predict what movies people will watch, so I can drive engagement into my movie app, I’m going to want data. What movies have people watched in the past and what did they like? Similarly in customer experience, if I’m trying to predict the best outcome of that interaction, I want CX data. I want to know what’s gone well in the past on these interactions, what’s gone poorly or wrong? I don’t want data that’s just available on the public internet. I need specialized CX data for my AI models. When we think about bolstering AI capabilities, it’s really about getting the right data to train my models on so that they have those best outcomes.

And going back to the example I brought in around sentiment, I think that reinforces the need to ensure that when we’re training AI models for customer experience, it’s done off of rich CX datasets and not just publicly available information like some of the more popular large language models are using.

And I think about how data plays a role in enhancing employee and customer experiences. There’s a strategy that’s important to derive new information or derive new data from those unstructured data sets that often these contact centers and experience centers have. So when we think about a conversation, it’s very open-ended, right? It could go many ways. It is not often predictable and it’s very hard to understand it at the surface where AI and advanced machine learning techniques can help though is deriving new information from those conversations such as what was the consumer’s sentiment level at the beginning of the conversation versus the end. What actions did the agent take that either drove positive trends in that sentiment or negative trends? How did all of these elements play out? And very quickly you can go from taking large unstructured data sets that might not have a lot of information or signals in them to very large data sets that are rich and contain a lot of signals and deriving that new information or understanding, how I like to think of it, the chemistry of that conversation is playing a very critical role I think in AI powering customer experiences today to ensure that those experiences are trusted, they’re done right, and they’re built on consumer data that can be trusted, not public information that doesn’t really help drive a positive customer experience.

Laurel: Getting back to your idea of customer experience is the business. One of the major questions that most organizations face with technology deployment is how to deliver quality customer experiences without compromising the bottom line. So how can AI move the needle in this way in that positive territory?

Andy: Yeah, I think if there’s one word to think about when it comes to AI moving the bottom line, it’s scale. I think how we think of things is really all about scale, allowing humans or employees to do more, whether that’s by increasing their cognitive load, saving them time, allowing things to be more efficient. Again, that’s referring back to that augmented intelligence. And then when we go through artificial intelligence thinking all about automation. So how can we offer customer experience 365 24/7? How can allowing consumers to reach out to a brand at any time that’s convenient boost that customer experience? So doing both of those tactics in a way that moves the bottom line and drives results is important. I think there’s a third one though that isn’t receiving enough attention, and that’s consistency. So we can allow employees to do more. We can automate their tasks to provide more capacity, but we also have to provide consistent, positive experiences.

And where AI and machine learning really help here is finding areas of variability, finding not only the areas of variability but then also the root cause or the driver of those variabilities to close those gaps. And a brand I’ll give a shout out to who I think does this incredibly well is Starbucks. I can go to a Starbucks in any location around the world and order an iced caramel macchiato, and I’m going to get that same drink experience regardless of the thousands of Starbucks locations. And I think that consistency plays a really powerful role in the overall customer experience of Starbucks’ brand. And when you think about the logistics of doing that at scale, it’s incredibly complex and challenging. If you have the data and you have the right tools and the AI, finding those gaps and offering more consistent experiences is incredibly powerful.

Laurel: So could you share some practical strategies and best practices for organizations to leverage AI to empower employees, foster positive and productive work environments, and then also all of this would ultimately improve customer interactions?

Andy: Yeah, I think the overall positive, going back to earlier in our conversation is there are many use cases. AI has a tremendous opportunity in this space. The recommendation I would provide is to focus first on a crystal clear, high-probability use case for your business. Auto summary or the automated note-taking of agents after call work is becoming an increasingly popular one that we’re seeing in the space. And I think the reasons for it are really clear. It’s a win-win-win for the employee, the customer, and the business. It’s a win for the employee because AI is going to automate something that is mundane for them or very procedural. If you think of a customer service representative, they’re taking 40, 50 maybe in upwards of 60 conversations a day during their job, taking notes of what was talked about. What are action items? Very complicated, mundane, tiresome even. They don’t like doing it.

So AI can offload that activity from them, which is a win for the employee. It’s a win for the customer as a lot of times the agents are great at note-taking, especially when they’re doing that so often, which can lead to that unfortunate experience where you have to call back as a consumer and repeat yourself because the agent you’re now talking to can’t understand or doesn’t have good information about what you called or interacted with previously. So from a consumer experience, it helps them because they have to repeat themselves less often. The agent that they’re currently speaking with can offer a more personalized service because they have better notes or history of past interactions.

And then finally, the third win, it’s really good for the business because you’re saving time and money that the agents no longer have to manually do something. We see that 30 to 60 seconds of note-taking at a business with 1,000 employees adds up to be millions of dollars every year. So there’s a clear-cut business case for the business to achieve results, improve customer experience, and improve employee experience at the same time. I think as you’re hopefully venturing into leveraging AI more to improve your business, the key recommendation I would provide is just to focus on those crystal clear high-probability use cases and get those early wins and then reinvest back into the business.

Laurel: Yeah, I think those are the positive aspects of that, but concerns about job loss due to automation tend to crop up with AI deployment. So what are the opportunities that AI integration can provide for organizations and their employees so it’s a win-win for everybody?

Andy: And certainly empathetic to this topic. As with all new technologies, whenever there’s excitement around them, there’s also this uncertainty of what will those long-term outcomes be? But I think when we historically look back, all transformative technologies have boosted GDP and they’ve created more jobs. And so I see no reason to believe this time around will be different. Now those jobs might be different and new roles will emerge. When it comes to customer experience and the employee experience one interesting theory I’m following is, if you think about Apple, they had a really revolutionary model where they branded their employees geniuses. So you’d go into an Apple store and you would speak to a genius, and that model carried through all of their physical flagship stores. A very positive model. Back in the day, people would actually pay money to go speak to a genius or get a priority customer service slot but a model that’s really hard to scale and a model that hasn’t been successful in a virtual environment.

I think when we see AI and a lot of these new technology advancements though, that’s a prime example of maybe a new job that does emerge where if AI is offloading a lot of the interactions to chatbots, what do customer service agents do? Maybe they become geniuses where they’re playing a more proactive, high-value add back to consumers and overall improving the service and the experience there. So I do think that AI will have job shifts, but overall there’ll be a net positive just like there has been with all past transformative technologies.

Laurel: Continuing that look ahead, how do you see the era of AI evolving in terms of customer and employee experience? What excites you about the future in this space?

Andy: This is actually what I’m most excited about is when we think about customer experience today, it’s highly reactive. As a consumer, if I have a problem, I search your website, I interact with your chatbot, I end up talking to a live customer service representative. The consumer is the driving force of everything and the business or the brand is having to be reactive to them. Where I see the future evolving in terms of customer experiences, is being much more proactive with the convergence of data, these advancements of technology, and certainly generative AI. I do see AI becoming smarter and being more predictive and proactive to alert that there is going to be a problem before the consumer actually is experiencing it and to take action on that proactively before that problem manifests itself.

And just a quick example of maybe there’s a media or a cable company where a device is reaching its end-of-life state, so rather than it have it go on the fritz the day of the Super Bowl, reach out, be proactive, contact that individual, give them specific instructions to follow. And I think that’s really where we see the advancements of not only big data, AI, but just the abundance of the ability to reach out in preferred channels, whether that’s a simple SMS or a high-touch service representative reaching out really where the future of customer experience moves to a much more proactive state from its reactive state today.

Laurel: Well, thank you so much, Andy. I appreciate your time, and thank you for joining us on the Business Lab today.

Andy: Thanks. This was an excellent conversation, Laurel, and thanks again for having me.

Laurel: That was Andy Traba, who is the vice president of product marketing at NICE, who I spoke with from Cambridge Massachusetts, the home of MIT and MIT Technology Review.

That’s it for this episode of Business Lab. I’m your host, Laurel Ruma. I’m the global director of Insights, the custom publishing division of MIT Technology Review. We were founded in 1899 at the Massachusetts Institute of Technology, and you can find us in print on the web and at events each year around the world. For more information about us and the show, please check out our website at technologyreview.com.

This show is available wherever you get your podcasts. If you enjoyed this episode, we hope you’ll take a moment to rate and review us. Business Lab is a production of MIT Technology Review. This episode was produced by Giro Studios. Thanks for listening.

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.

Advancing AI innovation with cutting-edge solutions  

AI is helping organizations in nearly every industry increase productivity, engage customers, realize operational efficiencies, and gain a competitive edge. Advances in supercomputing in the cloud and the ability to achieve processing at an exascale level are major catalysts for this new era of AI innovation.

Common AI use cases today include personalized healthcare and targeted therapies, virtual assistants and chatbots, financial fraud detection, predictive maintenance, autonomous cars and machinery, energy management, and accelerated scientific discoveries.

Some companies are deep into their AI journey, delivering advanced AI-enabled products and services, but many businesses are at the early stages and are struggling with where and how to best apply AI in their business. AI is complex, requiring new skills, tools, and technologies.

To accelerate AI development and integration, organizations can benefit from a trusted partner that has AI expertise across the complete technology stack. The right AI solution provider can help determine the best AI strategy for a company’s specific business model and provide comprehensive, unified services, advanced infrastructure, and tools specifically designed for AI.

Discover the latest AI technologies. Join Microsoft at the NVIDIA GTC AI Conference March 18–21. Learn more.   

Companies across the world are turning to Microsoft to help them transform their business with innovative, secure, and responsible AI. At the forefront of artificial intelligence, Microsoft has delivered cutting-edge advances in vision, speech, language, decision-making, machine learning, and supercomputing infrastructure for more than 30 years. Hear how Microsoft AI solutions are helping organizations around the world achieve more in the video below.

Accelerating AI application development

Microsoft recently unveiled yet another round of AI services that can help businesses accelerate AI production, whether by adding intelligence to existing applications and processes or creating new ones from scratch. These new services include the following:

• Azure AI Studio, now in preview, empowers organizations and developers to innovate with AI. The platform, accessibly and responsibly designed, provides a one-stop shop for developers to seamlessly explore, build, test, and deploy AI solutions using state-of-the-art AI tools and machine learning models, all grounded in responsible AI practices. Developers can build generative AI applications, including copilot experiences, using out-of-the-box and customizable tooling and models with built-in security and compliance.
• Azure OpenAI Service offers industry-leading coding and language AI models and the latest advancements in generative AI for content creation, conversational AI, and data grounding.
• New GPT-4 Turbo in Azure OpenAI provides a leap forward with lower pricing, extended prompt length, and structured JSON formatting, delivering improved efficiency and control.
• GPT-4 Turbo with Vision is a new large multimodal model (LMM) developed by OpenAI that can analyze images and videos and provide textual responses to questions about them.
• DALL·E 3 is the latest image generation model from OpenAI, featuring enhanced image quality, more complex scenes, improved performance when rendering text in images, and more aspect ratio options.

Powering AI workloads

Microsoft is also reimagining every aspect of their data centers to deliver the agility, power, scalability, and efficiencies AI workloads demand. Microsoft’s pioneering performance for AI has ranked them as the number-one cloud in the Top500 List of the world’s supercomputers and powered innovations like a new battery material. AI trailblazers are building and training the most sophisticated models in the world on Microsoft Azure AI infrastructure.

Here are some of Microsoft’s latest infrastructure advancements:

• Custom-built silicon tailored for the Microsoft cloud offers optimized performance for AI and enterprise workloads. Azure Maia, an AI accelerator chip, is specifically designed to run cloud-based training and inferencing for AI workloads, such as OpenAI models, Bing, GitHub Copilot, and ChatGPT. Azure Cobalt is a cloud-native chip optimized for performance, power efficiency, and cost-effectiveness.
• New Azure Boost enables greater network and storage performance at scale, improves security, and reduces servicing impact for specialized AI clusters or  general-purpose compute workloads.
• Microsoft copilot for Azure simplifies operations and management with an AI companion that can help users design, operate, optimize, and troubleshoot infrastructure from cloud to edge.
• New Azure NC H100 v5 virtual machine series built with NVIDIA H100 Tensor Core GPUs provide greater memory per GPU, increasing performance for mid-range AI training and generative AI inferencing. Microsoft will also add the latest NVIDIA H200 Tensor Core GPU to its fleet to support larger model inferencing with no increase in latency.
• NVIDIA AI foundry service supercharges the development and tuning of custom generative AI applications for enterprises and startups deploying on Microsoft Azure.

Experience these advancements at NVIDIA GTC

Companies can experience Microsoft’s latest AI services and technologies and learn how to power their AI transformation at the NVIDIA GTC AI Conference March 18 to 21 in San Jose, California (and virtually). Through in-person and on-demand sessions, live discussions, and hands-on training, attendees will

• Get to know the core Azure AI services and technologies that power some of the world’s largest and most complex AI models and applications.
• Discover how to accelerate the delivery of generative AI and large language models (LLMs).
• Explore how Azure AI studio and purpose-built cloud infrastructure can accelerate AI development and deployment.
• Learn from best practices and customer experiences to speed AI production.

Featured sessions

• S63275 Power Your AI Transformation with the Microsoft Cloud
• S63277 Unlocking Generative AI in the Enterprise with NVIDIA on Azure
• S63274 The Next Level of GenAI with Azure OpenAI Service and Copilot 
• S63273 Deep Dive into Training and Inferencing Large Language Models on Azure
• S63276 Behind the Scenes with Azure AI Infrastructure

Visit the conference schedule to view the full list of Microsoft sessions at NVIDIA GTC.

This content was produced by Microsoft Azure and NVIDIA. It was not written by MIT Technology Review’s editorial staff.

Register for NVIDIA GTC today and learn more about Azure AI and NVIDIA | Accelerated Computing in Microsoft Azure.

Generative AI: Differentiating disruptors from the disrupted

Generative AI, though still an emergent technology, has been in the headlines since OpenAI’s ChatGPT sparked a global frenzy in 2023. The technology has rapidly advanced far beyond its early, human-like capacity to enhance chat functions. It shows extensive promise across a range of use cases, including content creation, translation, image processing, and code writing. Generative AI has the potential not only to reshape key business operations, but also to shift the competitive landscape across most industries.

The technology has already started to affect various business functions, such as product innovation, supply chain logistics, and sales and customer experience. Companies are also beginning to see positive return on investment (ROI) from deployment of generative-AI powered platforms and tools.

While any assessment of the technology’s likely business impact remains more forecast than empirical, it is necessary to look beyond the inevitable hype. To examine enterprises’ technological and business needs for effective implementation of generative AI, 300 senior executives across a range of regions and industries were surveyed. Respondents were asked about the extent of their corporate rollouts, implementation plans, and the barriers to deployment. Combined with insights from an expert interview panel, this global survey sheds light on how companies may or may not be ready to tackle the challenges to effective adoption of generative AI.

The overarching message from this research is that plans among corporate leaders to disrupt competition using the new technology—rather than being disrupted–—may founder on a host of challenges that many executives appear to underestimate.  

Executives expect generative AI to disrupt industries across economies. Overall, six out of 10 respondents agree that “generative AI technology will substantially disrupt our industry over the next five years.” Respondents that foresee disruption exceed those that do not across every industry.

A majority of respondents do not envision AI disruption as a risk; instead, they hope to be disruptors. Rather than being concerned about risk, 78% see generative AI as a competitive opportunity. Just 8% regard it as a threat. Most respondents hope to be disruptors: 65% say their businesses are “actively considering new and innovative ways to use generative AI to unlock hidden opportunities from our data.”

Despite expectations of change, few companies went beyond experimentation with, or limited adoption of, generative AI in 2023. Although most (76%) companies surveyed had worked with generative AI in some way in 2023, few (9%) adopted the technology widely. Those that used the technology experimented with or deployed it in only one or a few limited areas.

Companies have ambitious plans to increase adoption in 2024. Respondents expect the number of functions where they aim to deploy generative AI to more than double in 2024. This will involve frequent application of the technology in customer experience, strategic analysis, and product innovation.

Companies need to address IT deficiencies, or risk falling short of their ambitions to deploy generative AI, leaving them open to disruption. Fewer than 30% of respondents rank each of eight IT attributes at their companies as conducive to rapid adoption of generative AI. Those with the most experience of deploying generative AI have less confidence in their IT than their peers.

Non-IT factors also undermine the successful use of generative AI. Survey respondents also report non-IT impediments to the extensive use of generative AI. These factors include regulatory risk, budgets, the competitive environment, culture, and skills.

Executives expect generative AI to provoke a wave of disruption. In many cases, however, their hopes to be on the right side of this innovation are endangered by impediments that their companies do not fully appreciate.

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.

Transforming document understanding and insights with generative AI

At some point over the last two decades, productivity applications enabled humans (and machines!) to create information at the speed of digital—faster than any person could possibly consume or understand it. Modern inboxes and document folders are filled with information: digital haystacks with needles of insight that too often remain undiscovered.

Generative AI is an incredibly exciting technology that’s already delivering tremendous value to our customers across creative and experience-building applications. Now Adobe is embarking on our next chapter of innovation by introducing our first generative AI capabilities for digital documents and bringing the new technology to the masses.

AI Assistant in Adobe Acrobat, now in beta, is a new generative AI–powered conversational engine deeply integrated into Acrobat workflows, empowering everyone with the information inside their most important documents.

Accelerating productivity across popular document formats

As the creator of PDF, the world’s most trusted digital document format, Adobe understands document challenges and opportunities well. Our continually evolving Acrobat PDF application, the gold standard for working with PDFs, is already used by more than half a billion customers to open around 400 billion documents each year. Starting immediately, customers will be able to use AI Assistant to work even more productively. All they need to do is open Acrobat on their desktop or the web and start working.

With AI Assistant in Acrobat, project managers can scan, summarize, and distribute meeting highlights in seconds, and sales teams can quickly personalize pitch decks and respond to client requests. Students can shorten the time they spend hunting through research and spend more time on analysis and understanding, while social media and marketing teams can quickly surface top trends and issues into daily updates for stakeholders. AI Assistant can also streamline the time it takes to compose an email or scan a contract of any kind, enhancing productivity for knowledge workers and consumers globally.

Innovating with AI—responsibly

Adobe has continued to evolve the digital document category for over 30 years. We invented the PDF format and open-sourced it to the world. And we brought Adobe’s decade-long legacy of AI innovation to digital documents, including the award-winning Liquid Mode, which allows Acrobat to dynamically reflow document content and make it readable on smaller screens. The experience we’ve gained by building Liquid Mode and then learning how customers get value from it is foundational to what we’ve delivered in AI Assistant.

Today, PDF is the number-one business file format stored in the cloud, and PDFs are where individuals and organizations keep, share, and collaborate on their most important information. Adobe remains committed to secure and responsible AI innovation for digital documents, and AI Assistant in Acrobat has guardrails in place so that all customers—from individuals to the largest enterprises—can use the new features with confidence.

Like other Adobe AI features, AI Assistant in Acrobat has been developed and deployed in alignment with Adobe’s AI principles and is governed by secure data protocols. Adobe has taken a model-agnostic approach to developing AI Assistant, curating best-in-class technologies to provide customers with the value they need. When working with third-party large language models (LLMs), Adobe contractually obligates them to employ confidentiality and security protocols that match our own high standards, and we specifically prohibit third-party LLMs from manually reviewing or training their models on Adobe customer data without their consent.

The future of intelligent document experiences

Today’s beta features are part of a larger Adobe vision to transform digital document experiences with generative AI. Our vision for what’s next includes the following:

• Insights across multiple documents and document types: AI Assistant will work across multiple documents, document types, and sources, instantly surfacing the most important information from everywhere.
• AI-powered authoring, editing, and formatting: Last year, customers edited tens of billions of documents in Acrobat. AI Assistant will make it simple to quickly generate first drafts, as well as helping with copy editing, including instantly changing voice and tone, compressing copy length, and suggesting content design and layout options.
• Intelligent creation: Key features from Firefly, Adobe’s family of creative generative models, and Adobe Express will make it simple for anyone to make their documents more creative, professional, and personal.
• Elevating document collaboration with AI-supported reviews: Digital collaboration is how work gets from draft to done. And with a 75% year-over-year increase in the number of documents shared, more collaboration is happening in Acrobat than ever. Generative AI will make the process simple, analyzing feedback and comments, suggesting changes, and even highlighting and helping resolve conflicting feedback.

As we have with other Adobe generative AI features, we look forward to bringing our decades of experience, expertise, and customers along for the ride with AI Assistant.

This article contains “forward-looking statements” within the meaning of applicable securities laws, including those related to Adobe’s expectations and plans for AI Assistant in Reader and Acrobat, Adobe’s vision and roadmap for future generative AI capabilities and offerings and the expected benefits to Adobe. All such forward-looking statements are based on information available to us as of the date of this press release and involve risks and uncertainties that could cause actual results to differ materially. Factors that might cause or contribute to such differences include, but are not limited to: failure to innovate effectively and meet customer needs; issues relating to the development and use of AI; failure to realize the anticipated benefits of investments or acquisitions; failure to compete effectively; damage to our reputation or brands; service interruptions or failures in information technology systems by us or third parties; security incidents; failure to effectively develop, manage and maintain critical third-party business relationships; risks associated with being a multinational corporation and adverse macroeconomic conditions; failure to recruit and retain key personnel; complex sales cycles; changes in, and compliance with, global laws and regulations, including those related to information security and privacy; failure to protect our intellectual property; litigation, regulatory inquiries and intellectual property infringement claims; changes in tax regulations; complex government procurement processes; risks related to fluctuations in or the timing of revenue recognition from our subscription offerings; fluctuations in foreign currency exchange rates; impairment charges; our existing and future debt obligations; catastrophic events; and fluctuations in our stock price. For a discussion of these and other risks and uncertainties, please refer to Adobe’s most recently filed Annual Report on Form 10-K and other filings we make with the Securities and Exchange Commission from time to time. Adobe undertakes no obligation, and does not intend, to update the forward-looking statements, except as required by law.

This content was produced by Adobe. It was not written by MIT Technology Review’s editorial staff.

Responsible technology use in the AI age

The sudden appearance of application-ready generative AI tools over the last year has confronted us with challenging social and ethical questions. Visions of how this technology could deeply alter the ways we work, learn, and live have also accelerated conversations—and breathless media headlines—about how and whether these technologies can be responsibly used.

Responsible technology use, of course, is nothing new. The term encompasses a broad range of concerns, from the bias that might be hidden inside algorithms, to the data privacy rights of the users of an application, to the environmental impacts of a new way of work. Rebecca Parsons, CTO emerita at the technology consultancy Thoughtworks, collects all of these concerns under “building an equitable tech future,” where, as new technology is deployed, its benefits are equally shared. “As technology becomes more important in significant aspects of people’s lives,” she says, “we want to think of a future where the tech works right for everyone.”

Technology use often goes wrong, Parsons notes, “because we’re too focused on either our own ideas of what good looks like or on one particular audience as opposed to a broader audience.” That may look like an app developer building only for an imagined customer who shares his geography, education, and affluence, or a product team that doesn’t consider what damage a malicious actor could wreak in their ecosystem. “We think people are going to use my product the way I intend them to use my product, to solve the problem I intend for them to solve in the way I intend for them to solve it,” says Parsons. “But that’s not what happens when things get out in the real world.”

AI, of course, poses some distinct social and ethical challenges. Some of the technology’s unique challenges are inherent in the way that AI works: its statistical rather than deterministic nature, its identification and perpetuation of patterns from past data (thus reinforcing existing biases), and its lack of awareness about what it doesn’t know (resulting in hallucinations). And some of its challenges stem from what AI’s creators and users themselves don’t know: the unexamined bodies of data underlying AI models, the limited explainability of AI outputs, and the technology’s ability to deceive users into treating it as a reasoning human intelligence.

Parsons believes, however, that AI has not changed responsible tech so much as it has brought some of its problems into a new focus. Concepts of intellectual property, for example, date back hundreds of years, but the rise of large language models (LLMs) has posed new questions about what constitutes fair use when a machine can be trained to emulate a writer’s voice or an artist’s style. “It’s not responsible tech if you’re violating somebody’s intellectual property, but thinking about that was a whole lot more straightforward before we had LLMs,” she says.

The principles developed over many decades of responsible technology work still remain relevant during this transition. Transparency, privacy and security, thoughtful regulation, attention to societal and environmental impacts, and enabling wider participation via diversity and accessibility initiatives remain the keys to making technology work toward human good.

MIT Technology Review Insights’ 2023 report with Thoughtworks, “The state of responsible technology,” found that executives are taking these considerations seriously. Seventy-three percent of business leaders surveyed, for example, agreed that responsible technology use will come to be as important as business and financial considerations when making technology decisions. 

This AI moment, however, may represent a unique opportunity to overcome barriers that have previously stalled responsible technology work. Lack of senior management awareness (cited by 52% of those surveyed as a top barrier to adopting responsible practices) is certainly less of a concern today: savvy executives are quickly becoming fluent in this new technology and are continually reminded of its potential consequences, failures, and societal harms.

The other top barriers cited were organizational resistance to change (46%) and internal competing priorities (46%). Organizations that have realigned themselves behind a clear AI strategy, and who understand its industry-altering potential, may be able to overcome this inertia and indecision as well. At this singular moment of disruption, when AI provides both the tools and motivation to redesign many of the ways in which we work and live, we can fold responsible technology principles into that transition—if we choose to.

For her part, Parsons is deeply optimistic about humans’ ability to harness AI for good, and to work around its limitations with common-sense guidelines and well-designed processes with human guardrails. “As technologists, we just get so focused on the problem we’re trying to solve and how we’re trying to solve it,” she says. “And all responsible tech is really about is lifting your head up, and looking around, and seeing who else might be in the world with me.”

To read more about Thoughtworks’ analysis and recommendations on responsible technology, visit its Looking Glass 2024.

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.