Publishing
- Start with your senior leaders. In effect, you need to go through a managed change process with them first, to ensure they are all aware of the need for change, have a desire to implement it, and have the knowledge they need to do so. Your senior team has probably been through quite a few changes, but none of them will have gone through what you are going to experience with AI.
- Explain the business drivers making the use of AI essential. Don’t sugar coat this, but be mindful of not using “doom” scenarios. Your model should be Bill Gates’ “Internet Tidal Wave” rather than Stephen Elop’s “Burning Platform”.
- For every single communication, ask yourself whether it contributes to helping employees be able to think "I understand why this change is needed". If not, rethink that comms.
- Be clear and consistent in messaging – and have leaders deliver the message (but make sure they are clear about it themselves).
- Tailor your message. Customize communication for different groups within the organisation. Different stakeholders may have different concerns and questions, so addressing them specifically can be more effective.
- Inspire and engage your team members to participate in the AI adoption process.
- Identify and involve key influencers and champions who can advocate for AI and influence others.
- Highlight the personal and professional advantages of AI, such as learning new skills, increasing productivity, or advancing career opportunities.
- Create a sense of urgency and excitement around AI and its potential.
- Provide adequate and relevant training and resources for your team members to learn about AI and how to use it effectively. Make sure you document any process changes.
- Tailor the training to suit different learning styles, levels of expertise, and roles.
- Use a range of methods, such as workshops, webinars, online courses, or peer coaching.
- Encourage feedback and evaluation to measure progress and identify gaps.
- Support your team members to apply their AI knowledge and skills in their daily work.
- Create a safe and supportive environment where they can experiment, practice, and learn from mistakes.
- Provide guidance, feedback, and recognition to reinforce positive behaviours and outcomes.
- Make sure success stories are being shared, and that your teams are helping each other.
- Monitor and track performance and results to ensure quality and consistency.
- Celebrate and reward your team members for their achievements and contributions to the AI adoption process.
- Focus on improvements in employees’ experience, not just business benefits.
- Solicit and act on feedback to improve and refine your AI practices and policies.
- Reinforce the benefits and value of AI for your business and your team.
- Keep your team informed and updated on the latest AI trends and developments and encourage continuous learning and improvement.
HouseFresh and the challenges of affiliate content
You might have noticed a post from HouseFresh doing the rounds, especially if you have anything to do with creating content intended to generate affiliate revenue. It’s caused quite a stir, particularly among publishers.
My background is in product testing. My first job in publishing was in MacUser's testing labs, where we would regularly have 10–20 products in and – literally in some cases – take them apart to decide which one was best. Next door was the PC Pro labs, which did the same thing, on an even bigger scale. On a visit to New York a few years later, I went to the testing labs of a US publisher: even bigger, with people who looked like they should be wearing lab coats picking over the bones of machines. The product testers were real experts, often devising unique tests designed to stretch the products in ways which matched up the real-world pounding they would take.
But those were proper group tests. What HouseFresh is writing about is not those. Their focus is the “best” article, written specifically to deliver affiliate clicks and sales, and designed to hit a specific keyword.
The HouseFresh article rips the lid off some of the worst aspects of content written to deliver sales though affiliate links (I refuse point-blank to call it “comtent”, which has to be one of the worst words ever invented). Their biggest complaint is that a lot of the pages you will see which rank highly on Google for affiliate-led keywords are written by people who have never had the products in their hands, let alone tested them. They may have done desk research, which involves, at best, scouring spec sheets for hidden details and, at worst, just scouring the user reviews on Amazon. But that doesn't tell you all that much about a product and whether it's any good or not.
Of course, this is really Google's fault because it is rewarding low-quality content by ranking it highly. This content, which is far cheaper to produce than a real group test, can be churned out quickly. A quick writer can do one or two a day, while a group test might take two weeks to organise, test and write. Use an LLM and you can probably make that process even faster. Just make sure to write your prompt to make it include phrases like our lab tests and our experts said to satisfy Google's pretty surface-deep view of how content based on real-world experience works.
HouseFresh’s hope is that Google will improve its algorithms and start rewarding content which is of higher quality, but I have my doubts. I suspect that the company’s focus is on creating “answers engines”like Gemini, rather than the traditional ten blue links. And even if it can improve its algorithms to prioritise in-depth reviews, gaming the SEO system will often look like a better option to many of the kind of publishers HouseFresh is attacking: the ones who have bought well-known brands but now use them to churn out lower quality content.
There are, and will be, exceptions, mostly from publishers who have a heritage in creating brands, rather than the ones that buy brands just for their heritage. But the sheer volume of content created by others could drown them out—especially as LLMs make it easier to generate entire sites within days.
As I have pointed out, I believe businesses based on this kind of affiliate-led content will also be disrupted over the coming few years by conversational AI. Once people have the option of having a conversation with a smart recommendation engine to tailor buying advice to exactly their needs, “best XXX” articles based on desk research or mining Amazon reviews just won’t be good enough.
Google is a choke point for the affiliate content business, but it’s not the only one. The second is Amazon, where most publishers derive a large chunk of their affiliate revenue. Although reliable numbers are difficult to find, Datanyze estimates Amazon has around 48% of the market share in affiliate networks, and anecdotally, I suspect the amount of revenue that brings in for publishers is higher still. Every publisher I know has sought to reduce their exposure to Amazon, especially after the effective demise of its Onsite Associates programme (known internally as OSP), changes in policy from Amazon would have a massive effect on publishers. But the reality is that if Amazon turned off the taps, or even reduced them, publishers with big investments in affiliate content production would be in trouble.
Would Amazon do this? It depends if you believe that Cory Doctorow’s enshittification cycle applies to it:
First, they are good to their users; then they abuse their users to make things better for their business customers; finally, they abuse those business customers to claw back all the value for themselves.
Are we at the point where Amazon starts to claw back the revenue it shares with its “business customers” – affiliate partners? Currently, probably not. But it’s worth thinking about the longer term, too. Already, 61% of US shoppers begin their buying journey on Amazon regularly. That’s traffic which Amazon has to pay no extra commission on, and so it’s something that it would love to do more of.
Plus, of course, Amazon has hundreds of millions of reviews of its own that it could tap to automatically create recommendations for users, including by layering conversational AI on top of it to allow users to get “intelligent” recommendations. The potential is there for Amazon to be a trusted source of reviews, as well as the retailer of choice for online.
The “good” news is that presently, Amazon is struggling with its own grey goo of content in the form of fake reviews generated by AI. It’s responded with more AI to try to trim them out. But the key question is really what happens at the point it decides that the money it spends on delivering affiliate revenue would be better off spent on ads, or on-site AI, or whatever else.
Ironically, the kind of content mills which HouseFresh is railing against would be less bothered if Amazon does ever scale back on its focus on affiliates: they are, most likely, pretty aware that the brands they are using are near the end of their life, and if the affiliate cash cow moves on the private equity companies will have long since made a sizeable return.
The relationship between Amazon and publishers, like that of Google and publishers, is some kind of symbiosis. Amazon gains revenue from the clicks that publishers drive their way. Publishers get a slice of that money, enough for them to survive and grow. But the key question is whether that symbiosis is obligate – where each depends on the other for survival – or facultative, where each benefits but could survive alone. If it’s the former, affiliate content has a long and profitable future. If it’s the latter, then eventually, publishers who go all-in on it may have a problem.
How to roll out AI in a creative business
I talked recently about how changing the culture of learning in your business will be important if you will make the most of AI. But no matter what, you’re going to have to roll it out – and you need to do that in a structured way.
Remember, this isn’t just an ordinary technology roll out: it’s a change management process that will have a lot of impact on your business. One framework which can help, and that I have found incredible powerful for managing change at scale is the ADKAR model of change management.
This model consists of five stages: Awareness, Desire, Knowledge, Ability, and Reinforcement. Each stage focuses on a different aspect of the change process, from creating a clear vision and generating buy-in, to acquiring the necessary skills and (importantly) sustaining the change over time, something that’s often neglected.
So how might you use ADKAR when looking at an AI rollout?
Awareness
At this point, your focus is to communicate the need and benefits of AI for your business, such as improving efficiency, enhancing customer service, or gaining insights. Explain how AI aligns with your vision, strategy and values, and what challenges it can help you overcome. Use data and evidence to support your case and address any concerns or misconceptions.
Remember, too, that this stage is about the need for change, not that change is happening. The most important outcome for this stage is that everyone understands the “why”.
Key elements of building awareness
Desire
Building desire is all about cultivating willingness to support and engage with the change, and for AI, it’s incredibly important. While AI is a technology, it requires cultural change to succeed – and changing a company culture is very hard. Without building desire, any change which threatens the existing culture will fail.
There are many factors which influence whether you can create a desire for change. Personal circumstances will matter, and the fear with AI is that employees will lose their jobs. That’s a big barrier to building desire.
And, in some cases, those fears will not be misplaced, so it’s critical to be clear about your plans if you are to win enough trust to create desire. Consider, for example, making a commitment to reskill employees whose roles are affected by AI, rather than giving bland statements about avoiding redundancies “where possible”.
This is especially critical if you have a poor track record of managing change – so it’s vital that you are in touch with how your change management record really looks to your teams.
At this point, you should also identify your champions. Who, in the business, has a lot of influence? Who are the people who are at the centre of many things, who act as communicators? Who do other employees go to for help and advice? Are there people who, when a new project starts, are the first names on the list? They are not always senior, so make sure you’re looking across the board for your champions.
Even if they are not the most senior people or the most engaged with AI at this point, if you win them over and make them part of the project, you will reap the benefits.
Remember, too, that desire is personal to everyone. While making the business more efficient and profitable tends to get your senior team grinning, not everyone in your business is motivated by that. Focus, too, on the benefits for people’s careers, work/life balance, and especially with AI, freeing up time to do more creative things and less routine work.
And don’t, whatever you do, talk about how “if we don’t become more efficient, people will lose their jobs”. I’ve seen this approach taken many times, and in creative businesses, it almost never works. Desire is about motivating people to change, and fear is a bad motivator.
Key elements of building desire for AI:
Knowledge
If awareness is about the why, the knowledge stage is about the how: how are we going to use these tools? This is where you build knowledge of the tools and the processes by which you use them.
One mistake that I have seen made – OK, to be honest, I have made – is to focus too heavily on training people on how to use a tool, without also training on changes in the processes you’re expecting people to make. Every new tool, including AI, comes with processes changes. And, in fact, the process changes that the tool enables are where you achieve the biggest benefits.
Training people in the context of the processes they follow (and any associated changes) relates the training to what people do – and that’s why I would recommend role-based training, which may cut across teams. If you have large teams, consider further segmenting this according to levels of experience. But I would recommend that you train everyone if possible: people who are left out may end up feeling either that AI isn’t relevant to them (and it will be) or that they have no future in your new, AI-enabled business.
Key elements of building knowledge of AI:
Ability
So far, what we have done is all theory. This stage is where the rubber really hits the road because it’s where all that training starts to be implemented. And at this point, people will start to spot issues they didn't see before as they get the hang of new processes and get better at them. They will also find things you didn’t anticipate, and even better ways of using AI.
One aspect that’s critical at this stage is the generation of short-term wins. For a lot of your teams, AI is the proverbial big scary thing which is going to cost them their jobs – and even if you have had a successful “desire” phase, it can be easy for people to be knocked off course when that is at the back of their minds, or they are reading scare stories about how AI will mean the end of humanity.
Quick wins will help with this. They are positive, visible evidence about the success of people they know using AI, and in storytelling terms that is absolute gold dust. Remember, though, that the positives must be personal, and in a creative business they need to focus on improving the creative work. Shaving 10% of the time taken from a boring business process might be incredibly valuable to you, but it’s not all that compelling to a writer, editor, or video producer.
Key elements of building ability in AI:
Reinforcement
This stage focuses on activities that help make a change stick and prevent individuals from reverting to old habits or behaviours, and I think it’s both the most crucial stage of managing a change in technology or process – and the one that’s easily forgotten.
There are several reasons for this. First, commitment even among your senior team may be waning, leading to reduced encouragement from the top to continue along the path. The people who thought that your rollout of AI was likely to fail will probably be latching on to every bump in the road and turning them into roadblocks – ones that they “knew would happen”.
This is why it’s incredibly important to have all your senior team go through a parallel managed change process, to make sure they are all bought into what you want to achieve. AI is a strategic change on the same level of impact long-term as a complete restructure of your entire business, so there is no getting round managing that process for your senior team.
If you are starting to get resistance to AI deployment at this stage, check whether your senior team are still bought into it. In the worst case, some of them may be sending subconscious signals to their teams that they don’t have to keep going.
And now the bad news: in terms of budget, the reinforcement phase may cost as much as the training required in the knowledge phase because you need people looking after the AI roll out who are constantly engaging with your teams, understanding issues, celebrating success, and making sure that communications about how AI works for everyone, and – importantly – keeps everyone updated on new developments and changes.
For every new pitch, product or process, someone needs to be in the room asking how you can use AI to improve this, speed it up, or do interesting creative things. That is the only way they AI will become embedded in what you do, and not fade away – as so many corporate projects do.
Who is that person going to be? The likelihood is that in the “desire” phase, internal champions will emerge who can do that job. This offers the advantage of credibility, as it’s someone who is both personally familiar and professionally respected, but don’t make the mistake of assuming this role is something that you can tack on to a day job. Unless your business is very small, doing all this is a full-time role, for at least a year after you have “completed” the rollout of the technology.