For me, storytelling is pure alchemy. It has been a powerful medium of human expression for millennia, imparting meaning and passing down wisdom and knowledge.

Storytelling has played a vital role in human evolution. It facilitated the transmission of critical information needed for survival, such as food sources, water locations, and dangers like predators and illnesses. Humans are social animals, and through time stories have fostered social cohesion and helped communities navigate complex relationships.

Likely because of these compelling benefits, humans are wired to engage with stories. Listening to a story activates not only the language-processing parts of the brain but also regions associated with experiencing—so you don’t just hear, read or see it, you feel it. Chemicals like cortisol, dopamine and oxytocin are released during storytelling, influencing memory retention, emotional engagement and empathy. These elements make storytelling a profoundly impactful form of communication.

Generative AI, driven by large language models (LLMs), can produce stories. But AI storytelling requires significant human input. Users must guide AI through initial prompts and refine its outputs.

AI lacks human curiosity. It cannot explore or grasp nuanced contexts beyond its training data. It wants for emotional intelligence and the ability to feel emotions like heartbreak, joy or fear. Its decisions may be ethically questionable without human oversight. AI cannot draw from authentic lived experiences or convey the intricate emotions behind spoken words.

Enter some pretty high-profile AI–generated brand fails.

So why bother with generative AI at all?

Should we dispense with the use of AI in storytelling completely? Humans need to do so much of the legwork anyway. Germinating the idea and developing the perfect prompts. Checking what pops out and refining, changing, tweaking. Why not cut out the middleman or, rather, the machine?

Besides, aren’t marketing outfits supposed to be framing brands in a more authentic and human way nowadays? Doesn’t it seem perverse to turn to machines to tell stories that will make brands seem more…erm…human?

It pains me to say it, but it seems the jury is still out on this question.

According to a survey of 700 US consumers, humans consistently preferred AI-generated outputs over human content. This was not specifically in the form of stories, but the figures are compelling.

Humans and AI were set tasks. One was to write an introduction for a blog post about the best cat food for indoor cats. AI won 54% to 46%.

In the other five contests, AI trounced humans at writing social media ads (70% to 30% and 54% to 47%), writing a blog post paragraph (60% to 40%), writing a social media post (65% to 35%) and writing a product description (65% to 35%).

Semrush, who commissioned the survey, said in the report: ‘These results show that AI-written content can be effective and resonate with your customers. If you prompt your AI tools well enough, you can create engaging and high-quality marketing copy.’ But added, ‘However, our experiment does not suggest that AI is enough for content writing.’

Humans apparently even prefer AI-generated poetry according to a study from the University of Pittsburgh. Though the caveat here is that the readers taking part in the study were non-expert poetry readers. The authors say: ‘because AI-generated poems do not have such complexity, they are better at unambiguously communicating an image, a mood, an emotion, or a theme to non-expert readers of poetry.’

However, the ‘complexity and opacity’ of human-written poetry is, for experts at least, the main appeal. The results would have been quite different if the participants had been poetry buffs.

AI can serve as a powerful tool, particularly in areas such as content marketing. It can make storytelling more accessible, personalised and scalable to the general consumer.

However, an AI-generated novel is unlikely to win the Pulitzer Prize any time soon. Stories crafted from genuine human emotion and experience will continue to resonate most deeply.

Until, that is, Blade Runner-style replicants start exhibiting human emotions and empathy. But that’s another story…

Welcome to the podcast blog for B2B decision-makers. We’ll dive deep into the podcast world and bring you insider secrets on how podcasts can boost business and elevate your brand. Get expert tips on podcast strategies, right here, right now. Let’s kickstart this blog with your host, sorry…writer, Laura Ansdell.

From niche entertainment to a mainstream media channel, podcasts have become a powerful tool in the B2B marketing mix. Edison Research revealed in April 2024 that over one-third (34%) of the U.S. population say they have listened to a podcast in the past week—up from 31% in 2023. That means an estimated 98 million people in the U.S. alone now listen to podcasts weekly.

Furthermore, 55% of business owners and founders are listening to podcasts daily. One only needs to look at some of the popular business podcasts to get a sense of the eye-watering numbers. The Tim Ferriss Show, for example, has surpassed one billion downloads.

Elevated engagement

Podcasts build deeper engagement than many other content forms. A BBC report found that branded podcasts achieve an elevated state of engagement for brand mentions. This also drives brand metrics across the board, helping to boost awareness (89%), brand consideration (57%), brand favourability (24%) and purchase intent (14%).

The focus on voice, I mean that literally, in most podcasts may explain their emotional impact. A 2017 study from Yale University found that voice-only communication elicits greater empathy than visual communication.

Brand voice can also be amplified through the podcast medium. People tend to trust individuals more than companies, and podcasts allow brands to convey their human side. Through authentic storytelling, brands can share their origins, challenges, and successes in a relatable way. This storytelling approach resonates with listeners, creating emotional connections that influence purchasing decisions.

Spotify’s 2024 Podcast Trends Tour found that 52% of creators feel they can be more authentic on Spotify than on social media platforms. Meanwhile, 63% of listeners trust their favourite podcast hosts more than social media influencers. This trust provides fertile ground for brands to deepen relationships with their audiences.

Networking and community building

Podcasts are a powerful networking tool. They provide opportunities to connect with high-level decision-makers, existing clients and potential leads. Hosting experts or industry leaders as guests can cultivate relationships and open doors to collaborations or partnerships. These meaningful conversations benefit both the host and the audience. Listeners gain industry insights, while brands solidify their position as thought leaders. This dual benefit strengthens connections and expands a brand’s influence within its niche.

Showcasing thought leadership

For B2B marketers, thought leadership is critical—The Superpowers Index, a 2024 study into B2B buying behaviour, found that thought leadership is now the third most important decision driver for B2B buyers, up from twentieth last year. Podcasts are an excellent channel for thought leadership in B2B contexts.  According to Edison Research, 74% of podcast users listen to learn new things. This makes podcasts an ideal medium for experts to share their knowledge with an eager audience and build credibility among prospective clients.

Humanising the brand

Brand podcasts can amplify voices within a company. Team members can share their expertise highlighting their contributions and nurturing a positive company culture. Listeners appreciate hearing from the people behind the brand, which humanises the organisation and builds relatability. Podcasts can also be a platform for celebrating diversity and inclusion, allowing employees to share their unique experiences. This approach motivates the workforce and strengthens the brand’s image as an inclusive and forward-thinking organisation.

Convincing ROI

Producing podcasts is relatively cost-effective compared to other marketing channels and each episode can be repurposed across platforms. For example, audio transcripts can provide a source of content for blogs or articles. Soundbites or quotes can increase visibility in social media. And repurposed content with targeted keywords can boost search rankings. Podcasts can include clear calls-to-action (CTAs) to drive engagement, such as encouraging listeners to subscribe to a newsletter, follow the brand on social media or leave a review.

The power of podcasts for B2B

For businesses looking to stand out in today’s competitive landscape, podcasts offer a unique blend of authenticity, engagement and ROI. Whether you’re starting conversations with industry leaders or building trust with prospective clients, podcasts are a compelling way to elevate your brand, nurture relationships and support long-term growth. In short, podcasts are no longer just an option—they’re a must-have in the B2B marketing toolkit.

The rise of AI needs no introduction. We’ve all watched AI slowly infiltrate every aspect of the business world, especially with big data’s rise. Then, suddenly, we witnessed ChatGPT become a household name in record time, and every other organisation is rushing to compete. It feels like we’ve watched history in the making, but no one is quite sure where it all will lead.

Sometimes it seems like AI lurks around every corner, completing every sentence on every platform. Whether that feels creepy or convenient is an individual sentiment, but for the most part, the technology seems empowering. It is enabling people to do things today that they wouldn’t have done before.

Almost everyone I know is using AI to produce written content these days. It’s not uncommon to hear small business owners excitedly talking about how they’ve taken marketing content into their own hands. Even people who typically shy away from new software and technology are creating all sorts of new content to support their personal brands and businesses. If that isn’t empowerment, I’m not sure what is!

AI: from writing to storytelling

If we turn our attention to the greater art of storytelling, we need to look beyond the content we write for the Internet or the books we author. There is also visual and audio storytelling. Telling stories is about transmitting knowledge and connecting with the viewer or listener… it’s how we remember things, visualise things, and sometimes how we pass along survival skills from one generation to the next. No matter, AI is going to influence the stories we tell with music, books, videos, and imagery in incredible ways.

And what about movies—the ultimate audio-visual storytelling format? Joe Russo, co-director of several Marvel movies and streaming series, recently predicted that generative AI would be able to create full-length feature films in about two years’ time. While it may take some time to fully automate box office hits, AI is sure to change the way we tell stories and consume them.

There are so many ways to tell a story (and so many ways AI is influencing all of them), but let’s get back to the main question. Does the rise of AI put us at risk of losing storytelling as we know it? Perhaps we should consider it an evolution, not a ‘loss’, but the issue is still complicated.

Maintaining our story construction skills

Humanity went through quite a lot to be able to tell stories the way that we do now. Art has become so much more advanced through art education and digital technology. In addition, today, literacy is commonplace and even taken for granted in many parts of the world. Reading and writing skills are a fundamental part of the curriculum in schools around the world.

While I don’t believe reading skills are in grave danger, you could argue that AI might deal some serious damage to writing skills. We spend a lot of time studying basic maths skills in school, but isn’t it easier to rely on a smartphone calculator? Past generations seem to have all the answers to basic household tasks that younger generations frequently consult Google and YouTube to learn about.

So, wouldn’t it make sense that, as humans rely more and more on intelligent writing technology to compose stories—or any content—our writing skills might atrophy? 

While AI can be a fantastic tool for storytelling, if we let our writing skills go and start to rely too heavily on technology, there’s the possibility that humans might forfeit their ability to drive the story where they want it to go.

Beyond writing, there are artists, singers, actors, dancers, illustrators, animators, choreographers, directors, producers, set designers, and so many other people behind modern-day stories. While writers feel like an easy target in this stage of AI development, some roles seem far more difficult to replace with technology. As we develop new ways to tell stories, we won’t necessarily lose the ways we tell stories today.

But will we lose all originality?

Tricky question. AI operates on patterns that exist in data. It was built to learn from the data it has been fed and replicate those patterns. That leads to several other questions about what data each AI platform is fed. The fact that different AI tools are based on different data sets makes them inherently unequal. (Even more alarming is how the tools may amplify inequality on a social level, but we’ll save that for another day.) 

The good news here is that AI has not lived or experienced emotions, and there’s plenty of room for writers to keep making a personal impact on storytelling. The bad news is that there’s a danger that we’ll lose some of that originality—and the element of storytelling altogether—in projects that put creatives up against tight deadlines.

More time for creativity, strategy, art, and joy? Maybe not.

Speaking of tight deadlines, let’s talk productivity.

2023 is almost half over, and I don’t know how that’s possible. Time is a precious commodity! One of the beauties of AI is that it allows us to save and reallocate time. With all the time we’ve freed up in structuring stories because our favourite AI tools did some of the work for us, might we be able to use our creativity even more than we used to? Or will we get lazier?

Will we use AI as our primary writing resource and use our skills of strategic thinking to create new ways to tell stories? Will we develop new creative concepts, create new storytelling formats, and enjoy the process even more? 

OR will changing expectations suck all of the fun out of creation as artists, creators, writers, and other storytellers are expected to produce more in less time? 

In the name of transparency, I’ve used AI writing tools to create entire articles in the recent past. But once I get the prompts right and have the final product in front of me, reading those articles generally makes me feel empty. The writing might be excellent, and the final product might be in the perfect tone of voice, but somehow I always feel a little left out. As a writer, I’ve always enjoyed the transformation process, and when AI does all the work, from start to finish, that process is gone. 

That process doesn’t require starting from scratch. Even when someone comes to me with a direction or even a full outline, and I’m not expected to create something from nothing, the evolution from general ideas to a full story is a process that brings me a lot of joy. One personal fear of mine is losing the ability to participate in that evolution—even if it does save a few hours of my day.

The fiery death of writer’s block

For mental health’s sake, let’s venture where there is absolutely nothing negative to say. AI is a writer’s block killer!

Storytelling is often a messy process. Many times writers get stuck and just don’t know what to say. I, for one, choose to look at AI as the fiery, painful death of writer’s block. AI has certainly gotten me out of a dark and frightening hole on multiple occasions over the last few months, and I’m sure other writers feel the same way. Perhaps that’s why so many tools are—wisely—named ‘writer’s assistants’.

Beyond writing, I believe that AI is a tool that should go into every professional storyteller’s toolbox. Even the storytellers who reject the notion of using AI could benefit from starting to use it sooner rather than later. As with most recent technologies that suddenly gain popularity, there’s a benefit to getting in and understanding the technology early.

AI: a new future for storytelling?

So, is AI the death of storytelling? The answer isn’t clear-cut, and there are so many other elements—from education and training to social considerations—to explore. AI brings both potential threats to the art of storytelling and exciting opportunities for its evolution.

As consumers, will we want to read stories with a label that says ‘human-written content’ on the cover, similar to the way we label minority-owned businesses on search engines and product packaging? Or will we look at stories as stories, no matter how much human involvement was required?

Perhaps the most interesting story of all is one that we’ll tell, as a society, through our increased use of AI. I hope we will view AI as a partner, not a replacement. Looking at AI as a partner will help us keep human empathy and retain the personal joy in telling stories. And, as with any great partnership, two voices are better than one. And with all that extra computing power by our side, I have a feeling we’ll create so many amazing new things and witness a breathtaking evolution in storytelling

How new technology is disrupting HR in 2020

Human Resources (HR) is rapidly changing because of culture, work types, business structures, and technology. Workload in HR teams is increasing with staff mobility and increasing quit-rates. And low unemployment rates are making it harder to acquire talent.

As competitors use the latest tools, you’re more at risk of your preferred candidate already accepting another position with a faster-moving organisation. People trained in HR more than ten years ago will have either upgraded their skills or will be failing to take advantage of the new opportunities. We’ve provided this industry roundup to clarify what is happening now, and get you pointed in the right direction.

HR is currently buzzing with stories about robotic process automation (RPA), machine learning (ML), and artificial intelligence (AI). So let’s start with a clear understanding of these terms.

 

Robotic Process Automation

Long before AI came onto the scene RPA had started eating away at the repetitive tasks done in HR. Don’t let the word ‘robotic’ distract you, its use here is closer to the source meaning ‘forced worker’ than the mechanical men of Hollywood. Advanced RPA systems can seem almost human in what they do, but under the hood, they’re basically an if-this-happens-then-do-that system.

They increase efficiency, reduce drudgery, and don’t make typos or other errors. If you ever find yourself repeating the same steps, based on rules you could write down, responding only to information that is or could be in a computer, you should use RPA to do it for you.

 

Machine Learning

If AI were a toaster, then ML would be those orange glowing wires that do the work. It is at the heart of AI, and is what gives it those almost-magical powers. You don’t need to understand how it works, but knowing what it does will put you in a strong position to decide when it can help you. So bear with me for a moment.

To do human-like tasks which are complex and hard to define, computers have to be trained rather than programmed. This requires a lot of data, and that data needs to be paired as ‘when it looks like this’—’we call it that’. What makes it so powerful is the examples in the data can be very complex. Like styles of artwork, or resumes. Each resume in the training data set needs its ‘we call it that’ data connected to it, like ‘good worker’ or ‘unreliable worker’.

Unfortunately the set of training data needs to be huge. So if you don’t have thousands of resumes for both good and bad workers, you won’t be able to use ML to train your AI. The smaller that set of data, the more likely your ML process will mistake correlation for cause. We know that workers with even-numbered ages are not better than odd-numbered ages. But machines don’t, and could make this false correlation using a small data set. Because we never know what’s going on inside ML processes, these mistakes result in false biases that we don’t know about.

 

Artificial Intelligence

The term AI is misleading. It would be better to call it artificial pattern recognition. Arguably human intelligence could also be called ‘making the appropriate response to everything we experience by recognising the patterns’. But AI only recognises very specific sets of patterns that they’ve been trained on. It is a rapidly developing technology, and the term includes the field, the systems being used, and the package that gets rolled out to customers. It relies on ML to do its work though, and can’t do anything that ML can’t achieve.

It is also misused, both by accident and on purpose, because it works really well in marketing. Like blockchain was in 2017, AI is the snake oil of 2020, so buyers beware. When evaluating whether an AI claim is genuine think of the ML that’s driving it and ask, what could they be using for training data, and how are they choosing the ‘we call it that’ information for each example?

 

Where robotic process automation and AI are used in HR

From now on, we’ll only be talking about RPA and AI, knowing that ML is supporting it. This technology is changing incredibly fast. And new players that offer software and services are pouring into the market faster than industry experts can keep up. It’s not possible to create an exhaustive list of what’s available, but websites like Capterra and G2 try to stay on top of things.

And it’s not just the new kids on the bloc that are adopting RPA and AI technologies. The old established players are also incorporating this technology into their platforms. It’s certainly not just HR that’s being disrupted, so broader software suites might also meet your needs, all the way up to Enterprise Resource Planning systems.

Applicant tracking systems (ATS) work across many of the areas we cover below, so they’re worth a special mention here. While components of them might use AI, their basic function should not. The information that is used in an ATS is well structured, like who the applicant is, the position they’re applying for, and where they are in the process. RPA is incredibly useful with this sort of structured information. But AI’s powerful pattern recognition abilities are not required. So if software implies that it uses AI to do this, be wary, that might be snake oil you’re smelling.

 

Candidate Screening: Cast a wider net with less effort

Attracting talent
One area where AI has recently become competent is writing text based on a short prompt. GPT2 is an AI model that is so good at writing fluent English that it caused a stir when Open AI, the creators, chose not to release it to the public over fear of misuse. Since then, this area of AI usage has leaped ahead, and now trained AI models can write job advertisements for you based on the skills required.

During periods of low unemployment, or when trying to attract the top talent, it is not enough to target active candidates who are looking for work. Passive candidates who are already satisfactorily employed must also be drawn into the application process. This is where chatbots really shine. Also using AI for strong language skills, chatbots incorporate the ability to keep track of information that has been gathered and then craft questions to fill in the blanks. In this way, a casual visitor might be organically drawn into a job application process. Chatbots can even schedule interviews.

Resume Process
It’s a shame that sometimes the best candidate doesn’t even get a look-in because too many resumes had already arrived before theirs could be added to the pile. But with limited time and staff resources, it is impossible to look through an unlimited number of resumes to see if it is suitable.

It’s important here to clarify the difference between a suitable resume, and a suitable candidate. AI systems can do useful things like converting university names, qualifications, business names, and dates from a resume into data files. They can’t yet pick up on the tone of voice and determine if the candidate is a good fit for the company culture. But that’s okay. By weeding out the vast majority of unsuitable applicants, and then ranking the remainder, human skills can be reserved for the ones that are worth the effort. This means that instead of capping the number of resumes accepted, you can turn the tables and include previous resumes as well as sorting through resumes in online databases.

AI can also crawl through online data sources like social media platforms and background-checking services to ensure your time won’t be wasted by applicants who would eventually be revealed as unsuitable, or even damaging, to your organisation.

Interviewing: don´t overlook the best candidates
The amount of time it requires to interview each applicant can also result in the best candidate being overlooked, as well as being a huge drain on the HR team’s resources. Automation and AI can take away the burden of at least the initial interview process.

These include,

• determining which candidates to interview,
• arranging the appointment,
• conducting an online video interview,
• reviewing the responses,
• analysing facial expressions,
• and even generating transcripts.

The results of these interviews can add data about the applicant and move them forward or remove them from the Applicant Tracking System process.

 

Onboarding: An easy path to success

RPA and AI do more in the recruitment process than finding your best candidate, they also give the candidate a better experience during the process by automating communication that keeps in touch as they progress towards employment. But this is only the beginning of establishing a productive workforce. The onboarding experience, and the information that it provides has a measurable impact on future performance.

And because onboarding processes can be very repetitive, they’re perfectly suited to RPA. Again—AI driven chatbots can be used here to provide quick answers and access to information.

 

Career Development: Hang onto talent with a career path

Once the new employee has settled into their position, a whole new journey begins. Tata, one of India’s largest conglomerates, is implementing a system that analyses employee skills and their preferred career path to generate a training plan to get them to where they want to be.

And with all of this increase in recruitment technology, including tools for targeting passive candidates, it’s becoming increasingly important to measure and manage job satisfaction. It might seem incredible, but the global IT company IBM is able to use AI to predict employee flight risk with 95% accuracy.

 

Removing Bias: From machine and human processes

While there are often stories of how AI can be biased, both generally and during the hiring process, it is important to remember that in most cases this bias was learned through the training data that was based on current human processes. It is also worth understanding how AI can reduce bias in the hiring process by hiding things like the age, gender, and ethnicity of applicants. This is achieved by recognising and removing indicators from all of their data, like the names of schools that accept students based on gender, or replacing names that correlate with ethnicities. Removing irrelevant information before humans get to see it, removes the opportunity for bias, and makes it easy to prove it didn’t influence decisions.

 

Benefits

Using the most current and powerful recruitment technology does more than quickly and efficiently employ the best candidates from a wider pool. It enables you to provide them with a great experience through employment and beyond.

It all boils down to retaining the best talent at the lowest cost-per-hire.