She addressed how scholarly publishers are incorporating Artificial Intelligence (AI) into various aspects of their businesses – and how marketers in particular are using AI to stay ahead in the ever-evolving marketing landscape.
Here are our top takeaways.
While the buzz around and use of Generative AI has picked up recently, AI has been used for over 15 years in marketing across many industries, such as e-commerce (think: Amazon). Publishers are using (or can use) AI to improve author and reader experience, optimize their workflows, improve discovery and reporting on research impact, and for societal good and research innovation.
The main uses of AI in marketing are to:
- Auto-tag content using in-depth and high-quality topic and concept tagging
- Track audience interest in topics, types of content, products, formats, etc.
- Micro-segment and hyper target audiences (e.g., find and target authors in niche topical areas)
- Personalize outreach and content recommendations
- Create predictive models (e.g., find and target authors with a propensity to publish based on past behavior and look-alikes)
- Orchestrate omnichannel journeys (e.g., nurture authors along the funnel from awareness to submission to advocacy)
- Improve analytics and marketing performance, testing and assessing performance and putting marketing resources behind what works
- Create text, graphic, and multimedia content with more efficiency (using generative AI)
Marketing and the AI Roadmap
Colleen referred to this headline from Harvard Business Review to sum up her view on how AI would impact marketing’s role: “AI Won’t Replace Humans – But Humans With AI Will Replace Humans Without AI”. Colleen urged marketers to start building their AI use cases and roadmap. To prepare for this change, marketers will need to focus on new technology and the acquisition and/or training of new skills. She stressed that it was critical for marketers to have a seat at the “AI table” for the following reasons:
- Marketing has always been at the vanguard of AI, and many of the strategies to improve customer experience and meet core business goals (such as author submission) rely on marketing strategies, marketing skills, and marketing technology. If Marketing is to be the voice of the customer and an organization’s insight engine, it must be advocating for the responsible and effective use of AI.
- With its focus on building positive customer experience, Marketing can advocate for the safe and responsible use of data and ensure that recommended content algorithms avoid creating echo chambers.
- Marketing leaders are well positioned to champion the organization’s brand. As we wrote in the Strategic Combination issue of C&E’s monthly newsletter, even in a world of AI, a “human voice will remain essential for community engagement.” Marketers need to ensure their brand remains true to its values and relatable in the world of automation and generative content creation.
- A September 2023 BGC report, How People Can Create—and Destroy—Value with Generative AI, surfaced a risk with generative AI, which is “a tradeoff between individual performance gains and collective creativity loss.” Marketers can play an important role in ensuring a culture of creativity exists within organizations.
Colleen also offered some specific advice around a few core areas:
AI Requires a New Messaging Strategy
- AI is changing the ways in which marketers create positioning, messaging, and copy.
- Generative AI is only as good as the prompts you put in. A core message library that speaks to the value delivered (and intrinsic values) of your brand and its product(s) should serve as the basis for AI prompts.
- Marketing should always review and edit AI-generated content. It may contain inaccuracies that need to be fixed, and a human hand is critical to add a sense of empathy and authenticity.
- AI allows marketers to send the right message to the right audience at the right time, and in the right channels. Marketers need a core message library to leverage AI technology to test and personalize messaging.
Different Approaches to Different Tech
- AI tools have different strengths and weaknesses and are at varying degrees of maturity. Test different tools and use the right tool for the right task.
- Colleen broke technology into two camps – cheap and cheerful technology and more complex technology that involves integrations and has higher annual costs.
- For cheap and cheerful technology, she urged marketers to experiment with free trials and tools that allow monthly subscriptions. An attendee helpfully added that organizations worried about proprietary IP being ingested by the LLM can have walled-off versions of LLMs such as ChatGPT.
- For more complex technology, she cautioned that a much more disciplined approach to evaluation and adoption is required.
What A Disciplined Approach Looks Like
- Planning: Start by inventorying and prioritizing use cases. Invest in a capability audit to understand where you have technology gaps. Outline the size of the prize (e.g., business case) and roadmap to senior leadership. Marketing leaders need to think in terms of their technology stack, not individual tools. Further, careful consideration needs to be given to the strategy, organizational design, and process changes required to successfully deliver against this roadmap.
- Vendor Selection: Conduct an RFI (request for information) or RFP (request for proposal) process to find a vendor that best meets your needs. For very complex tools, consider a proof of concept to “test drive” the tool before committing to a multi-year contract.
- Agility: While marketing transformation is often a multi-year journey, there can (and should) be numerous wins along the way. As an example, Colleen noted that a single customer view (SCV) is not a use case – it’s a longer-term goal. Progressively moving towards “SCV nirvana” should not preclude organizations from bringing in data to activate high-value use cases that do not require a full SCV.
Commit to Change Management
Technology can’t solve anything without the right people and the right strategy behind it. Structural changes may be needed to leverage AI effectively – including reskilling and training, along with effective change management to bring employees along in the AI transformation. Some employees may fear that their jobs will be displaced by AI. A focus on change management and reskilling is critical to alleviate these concerns, helping employees to see the benefits of AI and to use AI tools creatively to support and augment their work.