Content + AI

Colleen Jones: AI and The Content Advantage – Episode 39

Colleen Jones Now in its third edition, Colleen Jones's book "The Content Advantage" has become a classic in the content-profession literature. The new edition of the book continues to highlight content intelligence and content effectiveness and adds a new focus on the impact and use of AI in content programs. It also takes a fresh look at the enduring concepts of digital disruption and digital transformation, both of which have been accelerated by the arrival of new AI technology. We talked about: her work at Content Science and how it informs the forthcoming third edition of her book, "The Content Advantage" her take on the concepts of "digital disruption" and digital transformation, both of which have been accelerated by the arrival of AI the title she'd give a movie about pace of organizational adoption of AI: "Slow and Slower" how elevating content concerns to the C-suite has garnered better results for companies lke the pharma giant Pfizer how AI can accelerate the implementation of content visions, strategies, and experiences how AI can improve content intelligence and aid in the assessment of content effectiveness how the structure, framework, and methodology for assessing content effectiveness remains the same in the age of AI her push to get organizations to use digital transformation as the lever to take an end-to-end view of their content how she consciously crafts the language she uses to talk about her consulting services - for example, using the term "end-to-end" instead of "omnichannel" a correlation that she's identified between operational maturity and AI implementation how AI might improve the process of improving content performance her optimism about the prospects for content professionals in the new AI-dominated tech world Colleen's bio A content expert and Star Wars fan, Colleen Jones is the founder of Content Science, an award-winning content firm where she has advised or trained hundreds of the world's leading organizations to become content Jedis. She has worked with many of the Fortune 50, the largest U.S. web properties, the largest nonprofits, and several U.S. government agencies. She also served as the fractional head of content at Mailchimp during its high-growth period before its $12 billion acquisition by Intuit. A member of Mensa, Colleen shares insights about content, AI, and business by writing for Entrepreneur, MediaPost, Forbes, and Content Science Review and by speaking at events around the world. She has earned recognition as a top content change agent by publications such as Technical Communication and a top voice for content strategy and artificial intelligence by LinkedIn. As a top instructor on LinkedIn Learning, Colleen's courses have reached hundreds of thousands of professionals. Connect with Colleen online LinkedIn Resources mentioned in this interview Content Science The Content Advantage, third edition November 2024 Video Here’s the video version of our conversation: https://youtu.be/lumGk_5EH6Q Podcast intro transcript This is the Content and AI podcast, episode number 39. The arrival of generative AI has upended many corners of the content world. As a long-time content consultant and researcher, Colleen Jones is very aware of this phenomenon. But Colleen is equally aware of the enduring value of intelligent, effective content, and the fact that all content efforts must ultimately engage and motivate actual human beings. When applied thoughtfully and strategically at an organizational level, AI can help achieve all of these goals. Interview transcript Larry: Hi, everyone. Welcome to episode number 39 of the Content + AI Podcast. I am really delighted today to welcome to the show Colleen Jones. Colleen is the president of Content Science, and also the author of the forthcoming book, The Content Advantage, in its third edition. It's been out for quite a while and the new edition has a lot of new additions about AI. Welcome, Colleen. Tell the folks a little bit more about what you're up to these days. Colleen: Thank you so much, Larry. It is great to be here, and fantastic to connect with you again. Content Science, we have been doing all kinds of interesting things in and around content. We do a lot of professional services as part of that. We do a lot of research and analysis, and we get the opportunity to do it for clients, but also independently, just delve into things that are of interest to us or that relate to trends that we're seeing. We've been continuing that over the past several years, and I'm really excited with the third edition of the book to bring some of those updated insights, facts, stats, all that kind of good stuff into our current, very interesting situation with AI and content. Larry: Yeah, and I think that very interesting in air quotes is appropriate. And one of the things, I can't remember, I read the second edition of your book maybe five years ago, so I can't remember if digital disruption figured as prominently then, but that's how you open the third edition of the book, is with this notion of digital disruption, which I think is really apt in the age of AI, but I think it's also just in general, it's related to digital transformation and a number of other phenomena that are going on. Can you talk a little bit just about what your concept of digital disruption, and how it applies especially to content practice? Colleen: Yeah, absolutely. You know what? I mentioned it in the second edition without really having any clue of just how much disruption would happen between the second edition and the third edition, so made it much more prominent in this third edition. And really, what that is about is the pace, the acceleration of change driven by technology. And right now, what's really driving that is artificial intelligence. At a macro level, big picture view, when disruption happens, that really drives the need for change. Business models might need to change just the way a current business model is executed, might need to change all kinds of implications. Colleen: That really is what digital transformation is trying to address. And I know a lot of people see that as jargon, but in the business world it is taken kind of seriously, a lot of big budget around it. And with my book, The Content Advantage, I am really trying to tie in content to business decisions. I thought it was important to mention both digital disruption and digital transformation, and really kind of make the case for how important content is to both of those concepts. Larry: It just occurred to me literally, as we were talking that in both the case of digital transformation and the adoption of AI, you get the sense that there's a lot of director and VP and management level people who are getting the charter from on high, "We have to do digital transformation, we got to do AI." And I think that's the level that most of us operate at. Is that a correct assumption on my part? Because you're way more in the management consulting side of this than I am, I think. Colleen: Yeah, I think that there's certainly that reactive stance of, "Hey, there's a lot going on here. We really need to take this seriously. Do we really need to get into implementing AI and so on?" AI, in some ways is a shiny object. It is getting a whole lot of attention. There's that kind of reactive stance, but then we're also seeing a little bit more of a strategic approach. Something that I think is interesting is that individual adoption of AI, what we've seen over the past couple of years, especially generative AI, that can be fast. Someone creates their own account and they can start generating content, refining their prompts and so on. But organization-wide AI adoption, it has been slow and it's getting slower. If I gave it a movie title, I think I'd call it, Slow and Slower. Colleen: And I think that's a good thing because there's a little bit of pause around all of the potential pitfalls that come with AI. I think there's more realization of just how much impact generative AI can have, how much it affects an organization because content supports just about every business function. So it's far reaching in terms of implications, and so it's an opportunity to get more strategic. I think the slowness isn't necessarily bad. It's an opportunity for organizations who are really looking at potentially implementing AI at a larger scale to think about doing that strategically. And it's a big opportunity for content leaders, professionals or allies of content leaders and professionals to be a big part of that conversation. That's what I'd really love to see more of. Larry: Yeah, I've talked to a couple of people on the podcast too, especially in the content design world, in that product content world where they're often like the perennial fight for the seat of the table on these product and teams. And when they demonstrate their AI chops, they often not only get a seat at the table, they have the C-suite calling them for advice about stuff. Are there any examples of that in your practice? I'm going to keep in mind throughout the conversation that you do all this kind of independent curiosity of your own research. Are you finding any places where content people have sort of an edge, like an edge in that AI gives them a competitive edge in terms of that seat at the table or influence in an organization? Colleen: Yeah, absolutely. We've worked with, over the past year, director-level content leaders and above who really are trying to update strategy and operations to factor in AI in the right way, which I think is super exciting and that's the right way to do it, doing everything from a series of AI readiness workshops to really kind of dig into, where are the opportunities, what are the gaps we have to be able to make the most of those opportunities? That type of thing.

Bill Rogers: AI-Powered Assistants, Chat, and Search for Content Platforms – Episode 38

Bill Rogers Bill Rogers is an experienced AI entrepreneur whose latest venture, ai12z, gives web content platform owners tools to build digital assistants and chatbots and to run gen-AI-powered searches. We talked about: his work at his latest startup, ai12z, which builds copilots designed to power content experiences his use of the term "copilot" as a generic AI capability, to distinguish it from branded uses of the word the two main capabilities of their copilot: question answering and ReAct (reasoning and action) his take on RAG architectures and how ReAct fits into them how integrating copilots into content and commerce architectures can guide users through complex interaction flows that are connected to third-party services how to ensure that users have confidence in AI systems and that the systems are technically secure the technical architecture that underlies their copilot platform how copilots help write queries to search utilities and other information and knowledge sources to help with tasks like complex product comparisons the variety of UIs their platform provides: search boxes, knowledge panels, etc. how interactions with copilots can inform an organization's content planning the importance of including image AI in this kind of platform, to both better understand the content and create more robust ALT text Bill's bio Bill Rogers is a visionary entrepreneur with a deep technologist background in AI and digital technologies. Recognized for significantly influencing the evolution of online experiences, Bill founded Ektron and served as its CEO. Under his leadership, Ektron emerged as a pioneering SaaS web content management platform, serving thousands of organizations globally. After Bill sold Ektron to Accel KKR, it merged with Episerver and became part of Optimizely. Bill then co-founded and led Orbita as its CEO, driving innovation in advanced conversational AI. Beyond these startups, Bill co-founded several other ventures and has had an expansive career in digital signal processing and robotics engineering. Bill holds a Bachelor of Science in Electrical Engineering from Boston University. Connect with Bill online ai12z bill at ai12z dot com Video Here’s the video version of our conversation: https://youtu.be/hJPnAvWXBlA Podcast intro transcript This is the Content and AI podcast, episode number 38. You wouldn't try to operate an airliner without a copilot, and you shouldn't operate a modern web architecture one function at a time either. That's the case that Bill Rogers makes for his latest AI startup, ai12z. His company builds AI copilots - in the generic, non-branded sense of that term - that enable robust search and discovery, streamline complex tasks like mulitfaceted product comparisons, improve accessibility, and even help with content planning. Interview transcript Larry: Hi, everyone. Welcome to episode number 38 of the Content and AI podcast. I am really delighted today to welcome to the show Bill Rogers. Bill is a longtime veteran in the content management and technology world. He founded a company called Ektron years ago, which was acquired by Episerver, which is now known as Optimizely. He ran a conversational AI platform long before ChatGPT came out called Orbita, and he's currently the CEO and founder at ai12z. So, welcome, Bill. Tell the folks a little bit more about what you're up to these days. Bill: Thank you, Larry. Yes. So, at ai12z, what we're doing is we're focused on building essentially a copilot, enabling websites and mobile applications, the ability to take advantage of AI to help drive experiences. Larry: Nice. And that's a nice, succinct description of what you do, but a lot of websites have chatbots or things like that. How does a copilot... Well actually, first let me back up because copilot is an interesting term. I first became aware of it when GitHub did their coding assistant thing, and then Microsoft has a whole suite of branded products called Copilots. But we're talking about a generic capability. Is that correct? Bill: That's correct. I think the term copilot, Microsoft has used quite a bit, but it is a generic term. We actually like to refer to it as a website AI assistant. And if you think about it, in the days of Ektron, we had this phrase, "What do you want your website to do?" And now we are talking about, "What do you want your AI to do for your website?" Larry: Interesting. Human needs haven't changed that much, but we have all these new capabilities. I guess what are one or two use cases that have jumped out early in your journey that are really helping people? Bill: So, when you think about, "What does copilot need to do?" So, one of the obvious things is this ability to be able to answer questions. And so when you talk about years back, when people were building chatbots, the challenge was creating the knowledge for that question and answering took a tremendous amount of work because you'd have to curate each piece of content that you're going to answer a question with. You had to create an intent model. Just an awful lot of work. Bill: Today, we have a CMS connector, we ingest the data and we can answer any question that your content actually have. You don't have to redo anything with your content in order to make it usable for question and answering. So, that's the first step, just question and answer. Bill: Then there's this concept of ReAct, which is reasoning and action. You enable these agents to do things. It can talk to backend systems like CRM systems or it could talk to any system that you have in your system. You just make a REST API available for it, and all of a sudden we can now use this data to create workflow to accomplish tasks that used to take an awful long time to go do and create, and it doesn't need to be that way at all anymore. Larry: Yeah, I know a lot of conversational designers and I've watched them work in Voiceflow and tools like that and hand crafting all those query... all the questions and answers basically, and the intent discernment stuff that they do. There's a lot to that. And so that ReAct, that sounds like a really intriguing... it's like you can get your fingers into any other system that you have. And this kind of reminds me of a... Is this in the family of a RAG architecture where you're... Bill: So, a RAG architecture would actually be just an agent to a ReAct system. So, let's just describe RAG. To the users, RAG is a way for you to, instead of using the knowledge of the LLM, you are using the content of your own content and you're answering... the LLM is answering questions based on that content. So, you have typically a vector database that when you ask the question, it gets the content and based on the content that it gets, the LLM will analyze that content and build a summary answer to it, actually very, very robust. And so that's a core piece to it. Bill: What ReAct does is that there's a large language model that does the reasoning. It thinks about what came in as a question and says, "Can I just answer that question or do I call one of my agents to help me answer the question?" And so, one of those agents can be ReAct... I mean, can be the RAG. Bill: So, why that becomes very exciting is that let's say that you want to compare two products. Your RAG has the information about each product in the system. The reasoning engine knows if you said... We'll use an example, sports example. If I said I wanted to compare the stats of Bobby Orr and Derek Sanderson, that's very tough for RAG because that one compare question, are you going to find content in your system that actually does do the comparison? And you're likely not. And so what will happen is that the reasoning engine says, "I'm going to go call the RAG for Bobby Orr, and then I'm going to call the RAG for what's the stats of Derek Sanderson." Bill: It gets the answer of those two information and then it combines the answers to do the comparison, and you get an amazing comparison around that concept. So then you take that step to the next level with a reasoning engine. And the reasoning engine, you tell them about all the tools that you have available to it: email, SMS, CRM, and the list goes on. Google Maps, Google Places. And you then say something to it like, let's say you're a hotel and you said, "What is the directions to the hotel from the airport?" Bill: And so the reasoning engine, from its system prompt, knows the address of where the hotel is and it knows where the nearest airport is, and it'll actually call an agent called Google Maps and it passes to that, the address of the airport, address of the hotel and IT generates the Google map with the full map and the link so that you can actually... so you see all the directions just like you would in Google Maps, but you can click on it and now it's in your mobile phone. Bill: So, you can see how a hotel can start looking at a reasoning engine as enabling all these third party services. Like if you said, "What are my activities?" Then the system is intelligent enough to say, "Oh, I have these eight activities, would you like to learn more?" And it gives you call to actions to learn more. And you then click on learn more and you see something about golf that you were interested in. It tells you about golf and you said, "Would you like to book a tee time?" You click book a tee time, a form has to come up to collect who are you and it collects your first and last name, your room number. And then it says, "Do you want to pick a date and a time?" So the time slots, when you pick a date, the slots are going to change. So now you pick all the information and then it might say, "Do you want to rent a club car?" Bill: And then it collects that data and it'll analyze it, send you an email, register it with the system of record that this booking has occurred.

Jeff Coyle: Creating New Content-Marketing Opportunities with AI – Episode 37

Jeff Coyle Generative AI tools and LLMs bring the need for a new kind of content awareness in organizations of all sizes. While some have focused on content creation, Jeff Coyle has grown and accelerated his content-marketing capabilities by leveraging the content discovery and operations improvements that AI can deliver. We talked about: his decade-long history in working with NLP, AI, and content his overview of the rapid progression of AI technology over the past two years the importance to businesses and enterprises of doing a data inventory to understand their unique strengths the exponential increases in both the capabilities of the AI services he uses and their affordability the importance of creating high-quality content in this new AI landscape how to capture your org's knowledge and use it to fuel your content plans how journalists are crucial for capturing that knowledge his take on the current state of content-industry employment the importance of aligning content and its performance to organizational KPIs the crucial differences between how you wish people would consume your content versus how they are consuming it and how they might be the ongoing difficulties of marketing attribution and how new predictive models that AI affords can help address them how a "process inventory" is even more important than a conventional content inventory Jeff's bio Jeff Coyle is the Co-founder and Chief Strategy Officer for MarketMuse. Jeff is a data-driven search engine marketing executive with 20+ years of experience in the search industry. He is focused on helping content marketers, search engine marketers, agencies, and e-commerce managers build topical authority, improve content quality and turn semantic research into actionable insights. His company is the recipient of multiple Red Herring North America awards, multiple US Search Awards Finalist, Global Search Awards Finalist, Interactive Marketing Awards shortlist, and several user-driven awards on G2, including High Performer, Momentum Leader and Best Meets Requirements. Prior to starting MarketMuse in 2015, Jeff was a marketing consultant in Atlanta and led the Traffic, Search and Engagement team for seven years at TechTarget, a leader in B2B technology publishing and lead generation. He earned a Bachelors in Computer Science from Georgia Institute of Technology. Jeff frequently speaks at content marketing conferences including: ContentTECH, Marketing AI Conference, Content Marketing World, LavaCon, Content Marketing Conference and more. He has been featured on Search Engine Journal, Marketing AI Institute, State of Digital Publishing, SimilarWeb, Chartbeat, Content Science, Forbes and more. Connect with Jeff online LinkedIn MarketMuse Twitter jeff at marketmuse dot com Video Here’s the video version of our conversation: https://youtu.be/Ij18O07YnYc Podcast intro transcript This is the Content and AI podcast, episode number 37. The label "generative AI" has led many to focus on using this new tech for content creation, while the real benefits may lie in different capabilities that LLMs and other AI tools afford. In his work, Jeff Coyle has enthusiastically adopted AI, using it to identify new content repurposing opportunities, to capture and leverage unique organizational knowledge, and to dramatically reduce the costs of content operations, discovering along the way new opportunities for content professionals. Interview transcript Larry: Hi, everyone. Welcome to episode number 37 of the Content and AI podcast. I'm really delighted today to welcome to the show Jeff Coyle. Jeff is the co-founder and Chief Strategy Officer at MarketMuse. We talked on my other podcast, Content Strategy Insights, a couple of years ago, and I'm really excited to have him back because one or two things have changed since then. Welcome, Jeff. Tell the folks a little bit more about what you're up to these days. Jeff: Oh, thanks, Larry. And I am glad to be back. I am the co-founder and Chief Strategy Officer for MarketMuse, as you mentioned. I'm working on building artificial intelligence and content strategy offerings so that teams can make better decisions about what content they create or what content they update and then execute a lot faster. And so I'm sure we'll get into the details, but my background, I've been in the search space, building products, building search engines, building lead management systems, or selling them for 25 years. And I've been in SEO for about that long as well. There's probably nothing in the SEO space that you could ask me about that I haven't tackled or got knocked over by and got back up and then tackled. But yeah, I'm looking forward to this discussion. Larry: There's so much going on in that world. I really want to stay focused on the AI stuff that we might have to slip into SEO a little bit because that's an old practice of mine way back in the day. Jeff: Sure. Larry: But the first thing I wanted to do, you mentioned the details and do want to get into the details, but what I would love to get, because you're somebody who's been in this world for 20 years and you were talking about LLMs and Prompt Engineering six months before ChatGPT hit the scene. You're clearly embedded in this world. I would love to get your top-level overview of the commercial landscape around just data and data sourcing and the services around LLMs and GPTs and that whole world. Can you give us just a quick high-level overview? Jeff: Yeah. Like you said, and I've been doing natural language processing and the artificial intelligence components for now, gosh, about a decade. Thinking about ways that I can do it. I mean, I was trying to figure out how to use language technology to automatically classify documents into categories and into taxonomies, literally in a project 10 years ago. And then before that, thinking about search engine indexing and search engine strategies and building vertical search engines, building intranet search engines, and then the implications of how to use that to be really great at building content and being really great at SEO. Right now we're in a very unique, and the world is moving so fast that I think everyone really, really needs to focus on the new features and components that come with some of these language model releases. Jeff: We just saw from, and this dating this in the late summer in 2024, we saw from recent releases with Llama some of these things that have been closed and not accessible. Now you can see the way that things are working, right? The way that they're open, the waiting, things that you can tweak. You're able to learn from what's being released a lot more than you were in the past. And that's amazing just by itself. The advancing models that come out, even if you don't modify them yourself, they're progressing so fast that if you have a process in place that's using natural language processing technology or large language models, every time a new model's releasing, you're talking about savings of factors of 10 minimum. I mean, I have processes that every time something new comes out, I'm able to knock down 90% of the costs, right? When you're talking about the data side of it, there is massive, massive diamonds built into anyone that has any proprietary data source right now. Jeff: Inside your business, if you're a mid-market to small enterprise to enterprise, you should be doing a data inventory. What do we have that's special? What do we have that could be used for someone else's benefit based on how fast this market is moving, whether the use case, if you don't understand the use case, come find somebody like me. Come find somebody like Andrew Amen from 923 Studios, find somebody who is all about knowing how to make use cases with data and turning those things into potential gold mines for your business. If you have a database of customer data, if you have a database of real estate data, if you have a massive search engine index, you can use those things to do magic and you can do it on the cheap now. And it keeps getting cheaper and cheaper. And that's where I don't think people are catching up right now. Jeff: They're not catching up to how truly fast and how truly cheap it is to do things that would've cost millions of dollars. And I'm not being hyperbolic there. Millions of dollars only three or four years ago. And I'm a kid in a candy store with these things, right? I mean, I did a proof of concept that would've cost me about a half a million dollars just two or three years ago. And I shocked myself because the total cost of the entire project was a dollar. I mean it was literally a dollar. And I was like, I'm paying more for the coffee that I'm drinking right now than that cost. And I'm like, well, could we scale this? I'm like, hey, let's spend $70. And we did and I'm like, the magnitude of the things that we're doing for the cheap, it's truly staggering. And so I think everybody's really got to think what makes them special, what data do they have or what data do they know about? Maybe it's a partner, maybe it's a peer, maybe it's a data provider, and you can turn it into a partnership and say, hey, you have this thing. We could really do something special with it. That's the new economy with artificial intelligence and with content that nobody's talking about. Larry: Yeah. And as you say that, I'm thinking it's probably a rich multi-sided environment too. I'm just picturing, like you just said, if you have the data and people with the data have more opportunities, but people with ideas about what to do with that data, there's also the world of data products, but also just data as a supply for other people's stuff. It just seems like there's so much going on there. And you mentioned the use case.

Cennydd Bowles: Design and Tech Ethics for Our AI Future – Episode 36

Cennydd Bowles Like most designers who work in technology, Cennydd Bowles has reflected at times on the impact of his work and its ethical implications. After a couple of decades of information architecture and interaction design practice, Cennydd stepped back from his design work to explore philosophy and ethics in depth. His explorations have led him to extensive academic study as well as speaking gigs and writing on the subject, including a book, Future Ethics. We talked about: his transition from interaction design to tech ethics his origins in the information architecture world and his career, including a stint at Twitter how we as designers have missed predictable mistakes and patterns that ethicists have long known about how he got hooked on philosophy and ethics his 2018 book on the connections between the worlds of philosophy and design, Future Ethics the ethical issues that can arise in even a seemingly harmless practice like A/B testing his prediction that AI will in the not-too-distant future permit almost fully automated product development and the risks that that brings how the difficulties of measuring trust might exacerbate the trust issues that arise with AI the "magical" nature of AI his observation that "the problem with magic is it's intentionally deceptive" a new orchestrator role that he sees coming with AI his pessimism about the prospects for humans over the long term in the AI economy how Cory Doctorow's notion of "enshittification" manifests in the design and AI world what he sees coming: "rapidly iterating mediocrity rather than considered excellence" the power, albeit diminished recently, of employees to influence ethical decision-making within organizations three books he recommends (links below) his advice to designers to listen to and connect with philosophers and learn from their prior work on ethics Cennydd's bio Cennydd Bowles is a technology ethicist and interaction designer, author of Future Ethics, and a recent Fulbright Visiting Scholar at Elon University. Cennydd’s views on the ethics of emerging technology and design have been quoted by Forbes, WIRED, and The Wall Street Journal, and he has spoken on responsible innovation at Facebook, Stanford University, and Google. Connect with Cennydd online LinkedIn Cennydd.com Tech ethics books Future Ethics, Cennydd Bowles Design for Real Life, Eric Meyer and Sara Wachter-Boettcher Ethical Product Development, Pavani Reddy Ethics for People who Work in Tech, Marc Steen Video Here’s the video version of our conversation: https://youtu.be/MbfK7AnPa-0 Podcast intro transcript This is the Content and AI podcast, episode number 36. In the flurry of activity launched by AI-technology investment, ethical considerations have been left largely unexplored. Cennydd Bowles is an accomplished interaction designer who has spent the last several years studying and writing and speaking about tech ethics and responsible innovation. What he sees unfolding now concerns him, leading him to predict that the near-term future is more likely to bring "rapidly iterating mediocrity rather than considered excellence." Interview transcript Larry: Hi, everyone. Welcome to episode number 36 of the Content and AI Podcast. I am really delighted today to welcome to the show, Cennydd Bowles. Cennydd is a technology ethicist and interaction designer based in the UK. Welcome, Cennydd. Tell the folks a little bit more about what you're up to these days. Cennydd: Hey, Larry. Well, so let's see. I've just got back from America, so for the last six months, I've been in Elon University, North Carolina as a Fulbright visiting scholar. This is really a large part of my transition, essentially, from the days of UX and product design within industry, and transitioning from that into academia, and particularly philosophy, philosophy of technology, and ethics of technology. Cennydd: These days, I'm now essentially figuring out what's next. I'm finishing up a master's dissertation right now on the topic of the ethics of A/B testing, which I've got a lot of experience seeing inside companies, and think maybe I can offer something about looking at the ethics of it. After that, well, probably a lot more writing, probably a book or two. Then I think I'm probably heading down the academic path, so probably a PhD in some sort of philosophy, of technology, or computer science somewhere in that kind of space. Larry: Oh, great. I'll have to check back in. I'd love to see where... Getting into the details of this. You just mentioned, well, I guess I would love to talk just a little bit more about your transition, because you've been an interaction designer for a long time. I can't remember exactly how long, but we've talked about this and a little bit about your transition, but can you talk a little bit more about what motivated you to go from interaction design into ethics? Cennydd: Yeah, you bet. Yeah, so I started off as an IA back when that term was far more sort of current, I suppose. I read the Polar Bear Book, which some of your listeners may well know. This is Louis Rosenfeld and Peter Morville's book. I started, I guess, in about 2002, so it's been 20 plus years that I've been designing digital products. I don't like the idea that you can design the experiences, but interaction design, UX design, whatever you want to call it, for a range of companies, a lot of consulting, a bit of freelance. Cennydd: I also worked for Twitter for three years, where I was heading up the design team in London. It was after Twitter, actually, that I started to consider, well, maybe there's something that we're missing here as a community, and maybe there's something I can offer. It wasn't that I was sort of filled with horror and revulsion for what I'd seen inside Silicon Valley. It wasn't that I looked back on my career and said, "Wow, I've made a lot of mistakes." Cennydd: Of course, I have, and a few things I wish I could have ethically questioned at the time, but then that's common for all of us. I had an interest in the topic. Just even as a teenager, I was just interested in ethics as a concept, but I have no training in it. My undergrad was in physics, I had a masters in IT as well. I didn't really have any kind of philosophical or ethical background. When I left Twitter, I had sold, I got some shares, and I sold them, not huge amounts, it was a Silicon Valley thousandaire rather than millionaire, but I didn't have to rush into the next thing. Cennydd: I could afford to say, "Okay, what do I want to do? What's going to be the next right step for me?" I thought, well, I don't want to rush into a job immediately. I want to poke at this ethics thing. I think there's something here, and I don't understand it, and maybe there's something I can do to try and raise that, the profile of ethics within the design community and the technology community. I started reading. Cennydd: I got myself a reader's card for the British Library, and I sat there, and I tried to read philosophy. That's quite hard to do without any background in it. There's a reason why it's seen as a complex topic. It took me a while to find the right types of things, but eventually, I stumbled across some work that blew me away. I thought it was just fascinating, complex, and perceptive, some of the work that I was reading by philosophers and ethicists, and also writers, and artists, and critics. Cennydd: They'd been looking at the social impact of technology for decades. What occurred to me is that we just hadn't been listening. We'd been in this space, not really heeding their advice, not really listening to some of the warnings that they might've shared, and just convinced we were the smartest people in the room, and that we would figure it out for ourselves along the way. We're not the smartest people in the room, I'm afraid. You read some of this work, and you recognize a lot of the mistakes and the patterns that you see within the modern tech industry. Cennydd: It just put its hook in me, and eventually it got to the point where I said, "Actually, I think this is the direction I want to go. I don't think I want a regular kind of mainstream type design role anymore. I think I want to see what I can do to act as a translator, essentially, between the disciplines of design, and technology, and product, and the world of philosophy." That culminated in a book which I released in 2018, which is called Future Ethics. Cennydd: Then ever since then, I've been trying my best to make a living consulting on responsible design and technology, doing some academic work, talking, writing, speaking, all that kind of stuff, to try and influence the industry, frankly, to raise its standards, to consider ethics as more central to what it does. I think I've been partially successful in that. There's definitely been a change in how those discussions are happening since 2015, '16 when I started in that space. Cennydd: I'm not saying we're anywhere near winning that particular battle, but I think we're starting to see some slow change. I think that's going to be my continued role. Larry: Nice. We were talking before we went on the air that your current study, you're working on your master's dissertation, you said, and you're working specifically on A/B testing. I wonder, that seems like a really good, is that a good lens into tech ethics in general? Cennydd: I think it can be. I think one thing that makes it a good, almost sort of microcosm of how the tech industry thinks about ethics, or fails to think about ethics, is that A/B testing is very rarely questioned as something that's commonplace. Well, of course, we A/B test everything, and I've been in companies since 2007 when we A/B tested... Not really, there hasn't been a lot of focus on, "Well, should we A/B test,

Sharon Ni: Merging Conversation Design and Content Design – Episode 35

Sharon Ni One of the most engaging aspects of generative AI products is their conversational interfaces. This has led many content designers working on AI products to develop skills in conversation design. Sharon Ni works on both conversational AI products and script-driven chatbots in her content design role at Cisco. She has developed her conversational design and technical AI skills by attending conferences, hackathons, and other events, by networking extensively, and by experimenting constantly with AI and conversation tech. We talked about: her work on chatbots and AI tools at Cisco an overview of the content design guidance chatbot she built her addition of "conversation designer" to her resume the evolution of the people ecosystem she works in, which now includes more engineers and data practitioners the professional development that she's done to prepare her for working with AI and collaborating with her more technical collaborators how participating in hackathons and other events has helped her advance her AI skills some of the tools she uses in her work, including spreadsheets, Miro, and Voiceflow her personal interest in building chatbots and how it's helped her in professional work the content design repository where she stores the conversational content she works with how she helps her colleagues understand how to best use AI her new responsibilities around assessing the technical feasibility of her advice to "just do it," to start building your own AI projects and connecting with others who share your interest Sharon's bio I love writing products. I hate writing about myself. So here’s five quick things about me and my work in AI: I’m a content designer at Cisco. Currently working on the Cisco AI assistant and Cisco.com chatbot. I like trying and building different chatbots myself - I recently built a content style guide chatbot that can help people review their copy and find guidelines. I’m a fierce advocate for content research and like to use data to inform my content design decisions. I have a background in Psycholinguistics and received a master’s degree from Middlebury College in 2023. Huge fan of this podcast. Connect with Sharon online LinkedIn Video Here’s the video version of our conversation: https://youtu.be/4HgM2hp5hpM Podcast intro transcript This is the Content and AI podcast, episode number 35. One of the main attractions of generative AI products is their conversational interfaces. This basic characteristic has drawn many content designers into the adjacent field of conversation design. In her work on chatbots and conversational AI products at Cisco, Sharon Ni has applied conversation design techniques and also learned a lot about the engineering side of AI, sometimes even advising her colleagues on the technical feasibility of their product ideas. Interview transcript Larry: Hi everyone. Welcome to episode number 35 of the Content + AI podcast. I am really delighted today to welcome to the show, Sharon Ni. Sharon is a content designer at Cisco, is doing really interesting stuff with AI and other technologies there. Welcome, Sharon. Tell the folks a little bit more about what you're up to these days. Sharon: Yeah, hi Larry. Very nice to meet you and excited to be here. And as you mentioned, I'm currently working on Cisco AI system for security, which is part of the Cisco AI ecosystem. And I'm also working on a chatbot that's on the cisco.com website right now. Sharon: And other than that, I am also working with the Voiceflow team to build an AI powered content design, style guide chatbot that can help our design partners to find the right guidelines and also review copy based on the guidelines, basically. It's not going to write the copy for them, but it will provide recommendations based on the good examples and bad examples that I fed into the chatbot and also the templates. So yeah, that's what I do. Larry: Well, it sounds like you're definitely earning your paycheck, at least three major things going on there. I would love to start with the content design guidance chatbot that you mentioned, because that's like... I think that'll be of interest just probably a lot of people are working on similar kinds of things. Can you talk a little bit just in general about... You mentioned that it's not so much doing, writing for people, but it's more like style and voice and tone and stuff. Anyhow, can you talk a little bit about how that chatbot works? Sharon: Yeah. So basically, I injected all of our guidelines into this chatbot. I kind of rewrite it because you can't just put the same... the guidelines into the chatbot. It's not going to recognize it very easily. Sharon: And so I work with the Voiceflow team. They help me to write the code part. And then right now I'm just adding more examples from our product, the copy and our product, and also some good examples and we also need some bad examples so that the AI will be able to recognize it and learn from it. And also the templates that you have to provide with the... what kind of response you want this chatbot to produce in a certain format. The reason why I wanted to create this was because we always get a lot of repetitive questions from... Sharon: During our office hours or in our help channels, people are asking about whether or not it's okay to capitalize certain words or sentences. And also they're asking about some words that's already... they're in our guidelines. So that's why we wanted to create this chatbot so that people don't have to look through our guidelines. They can just type in using natural language and to find the right thing that they're looking for. Yeah. Larry: Right. And you're building that with Voiceflow. And it's interesting, you still have the job title: content designer. But you're doing an awful lot of conversation design. Sharon: I know. I know. Larry: Working with Voiceflow and all that. Was that a new skill to you? Because you've been doing this, what, a year or so that you've been working on these chatbots? Sharon: Yeah, like a year. Larry: So you've kind of upskilled to become a conversation designer as well as a content designer? Sharon: Yeah, I think so. And I think I started calling myself conversation designer very recently, because I feel like all my projects right now are AI or chatbot related. But also, at the same time I feel like the conversation design work that I'm doing, just wanted to be clear that might be different from what other companies are doing or other content designers are doing. Sharon: But I think basically right now I'm doing a lot of the writing for AI and also the writing for chatbots. But also, at the same time I'm working with a lot of design team, marketing, and also sales team to just think of those strategies for AI. So it's more like a new experience to me, but I find it really interesting and I had a lot of fun with it. Larry: Yeah. And you're reminding me of... There is... It seems like generalists, or not so much generalists, but people with versatile skill sets are really going to thrive it seems like in this age of AI, because what you just described... And not just skills, but also the ability to collaborate with new and different people. Like the conventional content design roles, there's the product and engineering and design colleagues where you just mentioned that you're working... Well, this has to do with the nature of the products. You're doing the sales and marketing folks. Larry: But you've also mentioned, I know in your AI work you're working with machine learning engineers and data scientists and stuff. Can you talk a little bit about how the people ecosystem around you has changed over the past year? Sharon: Yeah, yeah, definitely. I would say in the past I've never really worked really closely with the engineers in the past. Just for our team, we mainly work and support our designers. We're more like a service. And also, because we have a super, super small team. We only have three content designers in our team. So a lot of times we're not the one who actually created the copy at the very beginning. We're more like a reviewing, we're helping them to review and also to edit their copy. And also we have our office hours and help channel to help them answer UX writing related questions. Sharon: And right now, I think I'm more embedded in those AI projects from the very start of the project. And I'm doing more than just writing. I know people are talking content designer, only 10 or 20% of their time are doing the writing work, but right now I feel like it's less than that. Sharon: We're actually doing more thinking than writing, which is really interesting to me. And I'm in this AI design team and we have our designers and we have our engineers, machine learning experts, and also AI experts and data scientists. Sharon: We work really, really closely together because what we're doing right now is we're all trying to figure out together. We don't really know what we're doing. But it's great to be able to understand and also to learn from other people and to learn what they do and also what they know. And I learned so much from especially the engineers about AI and especially the technicality side of AI and also the limitation of AI, what we can do with AI, what we can't do with AI. So I think this is super important. Larry: Yeah, that's one thing that has come up in a lot of my conversations, is the level of technical skill that is required to do this is a little more than a lot of conventional content design roles. Was that a challenge for you or did it come naturally or how did you get up to speed to work with these more technical collaborators? Sharon: Well, it wasn't easy, I would say,