Media businesses know AI will save them time and money, but when it comes to implementing projects and technologies they stumble. They understand why – but not how.
The trouble is this: we’re bombarded with noise about AI as a world-changing concept, and about the need to develop a long-term AI strategy. But there is work to be done today and we need to deliver for our day-to-day business. The answer to this tension is focusing on tools which give tangible results and which happen to be powered by AI.
It probably sounds counterintuitive for an AI expert to argue against strategy. But it isn’t about ignoring strategy or sleepwalking into risks – it’s about doing business better using simple and tangible tools. Organisations should “avoid making the AI aspect more important than the goal itself,” as one audience and content director, Alison Gow, puts it.
In fact, the main blockers for media businesses come from misconceptions about what a strategy should look like, and what AI actually is.
The strategy myth
Listen to discussions about the opportunities and risks of implementing AI and you’d be forgiven for thinking it’s an impossibly large and complex task. Well-meaning advice suggests organisations should do all of the following: set up a taskforce, make AI a company-wide initiative, draft clear guidelines for usage, research how to protect their IP, and so on. Another industry-leading report suggests businesses need “a strategic, holistic approach” based on “fundamental self-analysis of the organisation’s capabilities and future planning”.
This is good advice. But it could be at least 10 years until a strategy like this is implemented—and in the meantime, the organisation hasn’t learnt anything about its customers or generated new revenues. For media businesses like news websites and digital publishers, which rely on audience growth and engagement, losing these insights is fatal. It’s no wonder media organisations believe their greatest blocker to implementing AI is lack of strategy, with 57% calling it out.
But there is a basic misunderstanding here. Today nearly every digital tool for businesses is powered by some element of AI – you’re using it, whether you know it or not. So, an AI strategy can be as simple as a business goal achieved with a tool over a relatively short period of time. The misconception that it needs to be anything grander is curbing investment and stifling growth.
This is especially important for small and medium-sized media organisations which have, say, several 100,000 or a few million visits a month. These businesses don’t have the luxury of an AI specialist or team, and maybe employ only one or two people in a commercial or audience development role, so the focus must be on time- and money-saving tools today, not on time-consuming strategies.
Businesses which wait to develop the perfect strategy will find that such a thing is a myth. Instead, the mindset behind implementing trials of AI should be about committing to outpacing your competitors. The question isn’t whether or not to adopt AI, but which tools will give your business the strategic edge. For media businesses this opens the conversation way beyond using generative AI for content creation. There is a world of machine learning, automation, natural language processing, predictive and propensity modelling, and so on, which is where the true impact lies.
Goals and tools
So, where do you start? Let’s revisit my idea above of doing business better using simple and tangible tools. Pick a distinct business goal such as increased content engagement, enhanced personalisation, or increased conversions. Most digital publishers already know the metrics which track these goals, like time on page and bounce rate for engagement, or the percentage of return customers and retention rate which are impacted by personalisation.
Off-the-shelf tools give the quickest win. Beyond removing the need to hire specialists and build in-house, the benefit of buying-in is that providers have done the heavy-lifting already. The tools you embed should meet regulatory requirements and quality standards like ISO/IEC, be GDPR-compliant, and so on, meaning you won’t get stuck in the ethical and regulatory weeds. Even conducting a short trial over several weeks will help you to learn fast, gather data, and realise a ROI.
A trial using AI-powered tools like this can be fast and low-risk – worlds apart from what many imagine when they think of AI strategy. Clearly there is sophisticated work in the background, both in the machine learning tech itself and in elements like user training, real-time KPI tracking and comparative analysis, all of which your provider should help with.
But if there was an easy and tangible way to do AI, this is it. So, will it actually work?
Excellence in audience engagement
Let’s deep-dive into one particular use case of AI tools – improving audience engagement. As I mentioned earlier, media organisations are in the business of attention and engagement, so there is one key question they must answer: when a reader visits your website, how do you keep them there and turn it into revenue?
The answer comes from AI-powered interactivity which can generate revenue directly from users and via personalised advertising. One study of 600 media organisations found that more than 80% of user registrations happen on pages with onscreen engagement options. The richer data provided by engaged readers can then produce nine times more advertising revenue, according to the same research.
Take an example from leading Wimbledon-based sports media company Greenways Publishing, which serves passionate sports fans across six digital and print brands including The Football Paper and Racing Ahead. Wanting to monitor and improve engagement and drive newsletter sign-ups, the publisher ran a trial with Bridged Media, which provides AI tools for audience engagement and monetisation.
For the trial, Greenways launched sign-up journeys on four domains using the Bridged interface, which was programmatic and native meaning it didn’t interrupt the reader’s experience, and was also A/B tested in six variations. The short-term strategy resulted in a 6-times increase in email conversion rate and 44% increase in overall user engagement.
The AI-powered tool “made the website more engaging by asking the customers relevant questions, thereby increasing time on site, and the overall engagement” according to Neil Wooding, Greenway Publishing’s marketing marketing. In fact, the email collection was so successful that Greenways pulled down the paywall for two domains and gave the Bridged solution full control over the content to drive further engagement.
Clearly machine learning and AI are the transformative elements here, but the reason it works is because the publisher set a short-term strategy, helped by Bridged Media’s experts, and tracked results against the objectives.
I mentioned earlier that AI can deliver to various goals from personalisation to engagement to subscriptions – you can see from Bridged’s case study that all these elements are closely interlinked. An interactive interface, placed strategically by an algorithm, not only personalises the experience for the reader, but also generates hugely valuable data and revenue for the business.
How to embrace AI
Of course, each outlet and readership is different – and your return on investment is too. For an organisation to understand exactly what revenue they can generate, they have to trial it themselves.
The key takeaway is that AI strategies don’t need to be decade-long, multi-million-pound endeavours. They should be rapid, tangible and value-driven, all powered in the background by algorithmic analysis and data.
This is still AI, but not as many media businesses know it.
A version of this article was previously published on New Digital Age and Media Makers Meet.