The State of AI in Demand Generation in 2024A Practical Approach

2023 saw a massive uptake of marketers adopting generative AI in their demand generation strategies. Just six months after the release of OpenAI’s GPT4, 73% of marketers were already leveraging generative AI internally (Statista, 2023). According to INFUSE Insights, content creation grew to become the single most popular use case, with marketers using AI to create everything from email and social media copy to complete blog posts.

Despite this, the majority of users demonstrated a preference for human-generated content (Statista, 2023), or at least content that was easily identifiable as such. It is only reasonable to expect the divide to widen in the coming years, as the marketplace and, consequently, outreach becomes flooded with AI-generated content. The next generation of large language models (LLMs), including those that power ChatGPT, Google Bard, and others, will likely be trained on AI-generated content themselves. As a result, the debate concerning content saturation and its quality will surely continue to intensify.

Whilst demand generation begins as a long-term strategy that centers on raising brand awareness, success lies in building strategies that engage prospective buyers throughout the entirety of their buyer’s journey to contribute to a positive client experience.

In the current landscape, blogs and articles are the top-performing content format, with a significant increase from 26% in 2022 to 48% in 2023. This is followed closely by webcasts, whitepapers, and eBooks at 46% (up from 24% in 2022), and videos at 29% (a slight increase from 23% in 2022).

The resurgence in the popularity of blogs can be attributed, at least partly, to the prevalence of AI and its ability to drive scale. However, to stand out in a sea of AI-generated content, the emphasis for marketers will be on producing content that is engaging, offers genuine value upfront, and is highly targeted.

Although depending on AI can be problematic, marketers must also be ready to leverage the more sophisticated use cases of AI in areas like agile marketing and lead nurturing. These include analysing data at scale, including audience and target account predictive modeling, automating repetitive workflows, using next-generation chatbots, and pulling data insights to inform account based marketing (ABM) and account based experience (ABX) strategies.

A lack of data is rarely an issue in most companies. Conversely, sourcing or identifying relevant, high-quality data that can inform strategic decision making is a key challenge.

This is also a focus of demand generation, as performance depends heavily on the ability to identify relevant data insights. To inform programs and make the right investments, marketers need to scale analytics to align with their broader strategies. For example, vanity metrics, such as website visitor counts, mean nothing in isolation. Rather, an emphasis on metrics that translate into tangible results and revenue should be a priority, such as revenue generated from marketing-sourced opportunities.

AI has emerged as a powerful tool for scaling and augmenting data quality in its own right. Automated data cleansing can help maintain the accuracy and integrity of databases by identifying and rectifying inconsistencies, duplications, and other issues. For example, AI can make quick work of pruning large Client Relationship Management (CRM) databases from mismatched or outdated client contact information.

Utilising accurate historical data can help organisations develop informed predictions regarding the likelihood of specific prospects converting into clients, thereby helping to shape detailed strategies for moving them through the sales funnel in a more effective way.

In cases where data generated by real-world buyer engagement is unavailable, synthetic data is rapidly emerging as a way to fill those gaps (Massachusetts Institute of Technology - Sloan, 2023). Driven by AI, this data, which is used as a stand-in dataset, can fuel iterative testing of demand generation tactics (or scaling existing initiatives).

Marketers can use synthetic data to augment their database rather than replace them entirely, especially in cases where real data is limited or biased. As such, synthetic data can serve as a tool for gaining insights into the performance of different marketing scenarios. Best of all, this happens in a risk-free environment, allowing marketers to extensively test campaigns before officially launching them.
Thanks to the power of cloud computing and the highly scalable storage and processing resources that come with it, AI tools can enable near-real-time analytics. This allows marketers to score and route leads to optimise engagement strategies at the right time and place to capitalise on valuable opportunities.

AI now plays a pivotal role in optimising lead management processes to ensure organisations can react swiftly and effectively to client needs. This focus on using AI to improve operational efficiency and automation is a top priority for 24% of decision makers (Pavilion, 2023). Below is an example of a simplified lead flow to illustrate its impact to the buyer experience:

Lead nurturing largely depends on three fundamental abilities:

Effective engagement depends on gaining visibility into the buyer’s behaviour across every touchpoint to inform strategies and identify sources of friction in their journey. Newer and more sophisticated algorithmic solutions can help in all three areas by augmenting marketers’ abilities to scale campaigns and adapt quickly to changes in buyer behaviour. For example, analysing historical engagement data can identify trends and patterns to predict likely future outcomes.

Sentiment analysis is another powerful technology and one that continues to improve rapidly thanks to the ability of AI to identify human emotions and patterns from natural language. AI-powered sentiment analysis goes far beyond simply searching for keywords denoting emotion to determining how prospective buyers and existing clients perceive a brand. It can analyse vast amounts of written content, such as online reviews and social media content, to get a far more thorough picture than ever before. Some tools can also analyse your audience at scale for a general overview of positive, neutral, and negative brand impressions.

Equipped with modern sentiment analysis engines, marketing teams will be better informed to create strategies that truly resonate with their intended audience. AI tools allow personalisation at scale for outreach, empowering dynamic content creation and segmentation that is more manageable for teams.

However, maintaining a presence on every possible marketing channel is not only resource-intensive but also an inefficient strategy for delivering engaging buyer experiences—hence the need to prioritise.

By leveraging AI to gain a clearer and more comprehensive picture of the buyer’s journey, marketers can identify the most effective channels for engaging buyers that represent the greatest value. Furthermore, AI can also help by intelligently orchestrating complex omnichannel nurturing campaigns, even to the point of making real-time adjustments based on engagement data. This is essential for enhancing the buyer experience, a top priority for 42% of B2B leaders leveraging AI in 2024 (Pavilion, 2023).

Leveraging AI not only refines buyer engagement strategies but also drives a tailored approach to nurturing potential clients. Here is an illustrative example of how a B2B cybersecurity vendor might use AI in lead engagement and nurturing:

Agile marketing is a fast-paced, adaptable approach that centers around changes in market conditions, buyer behaviours, and campaign performance. It emphasises flexibility and continuous improvement to adapt and scale rapidly to evolving buyer needs. As both acollaborative and iterative strategy, it depends heavily on data and automation to meet the demands for speed and scale.

Agile marketing takes its inspiration from similar concepts found in agile software development. 41% of marketing teams use agile tactics, such as short daily meetings, time-boxed work periods, and kanban boards (6th Annual State of Agile Marketing Report, 2023). In the same report, 82% of marketing teams that use agile strategies claim they are very confident in their ability to take advantage of emerging opportunities. The figure drops to 66% for marketers who are not fully agile.

AI is a key enabler of agile marketing, allowing organisations to enhance performance in real-time to reduce churn. For example, A/B and multivariate testing, though highly effective, have traditionally been time-consuming and often complex to implement. However, using AI to analyse results in real-time enables faster adjustments to boost campaign performance. Generative AI can go even further, by refining content on the fly, such as by utilising different headlines and subject lines or changing visual styles and imagery. This enables a broader exploration of demand generation strategies in a mere fraction of the time it would take to do so manually.

Another area of demand generation where AI excels, perhaps unsurprisingly, is targeted display. The ephemeral nature of many pay-per-click (PPC) advertisements, for example, necessitates a particularly fast-paced response to shifts in visibility and engagement. Failing to keep ahead can result in wasted budgets and misinformed bidding. However, by utilising AI to develop and automate real-time bidding strategies, organisations can base their campaigns on the latest insights into factors such as user behaviour, time of day, and market dynamics.

Perhaps the greatest power of AI is its ability to rapidly interpret thousands of data points, thereby providing near real-time suggestions on how to tweak campaigns. In 2024 and beyond, we will see more marketers using virtual assistants to advise on ad-hoc changes to everything from bidding strategy to ad placement and budget allocation.

For instance, AI can instantaneously detect engagement drop-off points and analyse data to help marketers identify bottlenecks in performance. These issues might be as simple as an unclear call to action or value proposition in a display ad, but this instantaneous visibility allows marketers to take corrective action quickly to continuously improve performance—an integral element of agile marketing.

AI can help companies understand, segment, and retarget leads with precision, including those acquired through Pay-Per-Click (PPC) campaigns. Here is an example of how:

Developing a structured approach is essential to harnessing the full potential of AI in driving demand. Below is a detailed six-step checklist to guide you through the process:

Below are the top three considerations for adopting AI to enhance demand generation efforts in 2024:

Marketers should not pursue adopting AI to replace human expertise and experiences. In an era when humanising your brand is a top priority, businesses must approach AI as a tool to augment and scale their capabilities. In demand generation, that means enhancing data analysis, informing real-time decision making, and providing strategic insight at a faster pace.

Over the last year, generative AI has been proclaimed for its ability to dramatically scale content creation. However, this has come at the cost of quality and authenticity. As a result, audiences will become more discerning, demanding a much higher caliber from content marketing to ensure quality and value as their number one priority.

Most AI models have been engineered to run in the cloud, thanks to the availability of immense computing power and storage capacity. This will help marketers deploy AI for real-time analytics to continuously enhance campaign performance and support more agile marketing strategies that minimise churn and maximise ROI.