极乐视频鈥檚 2024 AI Landscape: The Communicator鈥檚 Guide to Finding AI Tools You Can Trust, is the first in a series of reports focusing on the top enterprise-ready AI solutions for marketing and communications professionals. The Enterprise AI in Focus interview series follows on this report, with contributors sharing their insights on the AI landscape, and what lies ahead. In this third series installment, Noel Christopher, Senior Vice President Trust Product, and author of the Analytics and Social Listening section of the report, discusses her findings and what she sees ahead for the industry.
Q: What prompted your focus on enterprise generative AI tools for analytics and social listening?
Noel Christopher: First and foremost, analytics is core to our work at 极乐视频. Teams turn to us on a regular cadence, relying on nuanced, data-driven insights to track trends across social media platforms. This is key to their work, day in and day out. On a broader level, for over 15 years, social listening vendors have been at the crux of this innovation, with integrated AI solutions, utilizing natural language processing (NLP) technology and image and video analytics. It's natural that those in the space would be early adopters of generative AI (Gen AI). Gen AI offers powerful capabilities to enhance workflows and extract richer insights by simplifying complex tasks like Boolean query creation and real-time data synthesis. So that's a little of the why. On a personal note, I also spent about 10 years of my career working in the space as the keeper of strategic data integrations and licensing. So, I have a highly specialized perspective and interest in this specific craft, and the enterprise-readiness of the tools on the market.
Q: In the context of this report, what did it mean for a generative AI tool to be enterprise-ready, and what were the primary criteria you used to determine whether a tool fulfilled this requirement?
NC: The answer to this is few-fold, as there are several key factors that determine whether an AI tool is enterprise-ready. First, data compliance and risk mitigation are critical. Vendors that invest in social platform licensing deals and prioritize AI ethics stand out. Many of the top-performing platforms in our research have clear documentation on their AI practices, making them trustworthy for large enterprises. Another important criterion is scalability. The sophistication of the platform was a key indicator for me of its maturity and reliability. Could it handle potentially enormous datasets, and support global operations for larger organizations, such as Fortune 100 or 500 companies?
Q: What are some common misconceptions about AI in social listening and analytics?
NC: A common misconception is that AI will entirely replace human analysts. Yes, generative AI definitely speeds up workflows and simplifies tasks. But there still needs to be human oversight to ensure accuracy. Verifying that AI-generated insights align with the real narrative is essential to ensure that the data makes sense, is interpreted correctly, and matches the narrative. Some platforms provide good transparency into how AI arrived at its conclusions. But again, human expertise is needed in making sense of the data and applying it strategically. To sum it all up, you're not going to be able to just have a machine run build your dashboards tomorrow, run everything, and service your clients without having an analyst鈥檚 hands as part of what I like to call the analytical sausage-making process.
Q: What trends in AI-driven analytics and social listening tools did your research uncover?
NC: One significant trend I鈥檝e seen first-hand is the automation of query creation. Honestly, this has been a thorn in the side of every single vendor in the space for years. I鈥檝e watched my colleagues struggle to try to navigate questions of, how relevant is the data? Can we recall enough content? Is it the right content for the brand?
Building complex queries has always been a time-consuming chore for analysts, but generative AI streamlines this process and takes out the guesswork by automatically testing data and refining queries in real time. This is a tremendous innovation and saves teams weeks of manual work while at the same time improving the data relevancy.
Another trend that surprises me is the sheer pace of development of AI chatbots for marcom use cases. Many platforms are testing out and leveraging large language models (LLMs) like OpenAI鈥檚 GPT, or Anthropic, to see which is the best to tailor responses to specific queries, further demonstrating how dynamic and scalable generative AI is becoming in analytics and social listening.
Q: What key features or capabilities do these tools offer to make them particularly valuable to marcom teams? Focusing on data analytics and social listening?
NC: Data synthesis is a game-changer for marcom teams, and to me, it is one of the most important capabilities. These tools can not only aggregate and distill massive amounts of data but also provide actionable insights鈥攊dentifying emerging risks, suggesting next steps, and even tailoring responses to specific industries or personas.
Anomaly detection has also improved significantly, allowing platforms to more accurately highlight critical developments in real time. This is particularly valuable in identifying emerging trends or risks that might otherwise go unnoticed in traditional analytics platforms. These advancements are a big unlock to allow a lot more vendors to compete within that particular use case.
Q: What are some of the key considerations for decision-makers when selecting AI-forward social analytics tools?
NC: Knowing the data that is going into the tool itself is a major consideration. Decision-makers should ensure that the AI tool can integrate not only platform-specific data but also first-party or custom data from external sources. Some tools, like Talkwalker and Quid, allow users to upload this external data. This capability enables users to leverage additional data for more robust and tailored insights.
Another factor to consider is the platform's versatility. Enterprise marcom teams need tools that support multiple use cases鈥攆rom monitoring brand sentiment to competitive analysis鈥攁cross various regions and markets. A multifaceted tool is more likely to deliver value across different business functions.
Essentially, decision-makers need to consider the safety element, the data readiness and the flexibility of the use cases that they're able to deliver for an enterprise brand.
Q: What do you see in the future for generative AI in analytics and social listening?
NC: The future of generative AI in the analytics and social listing space, from the viewpoint of my research and professional experience, will see continued investment. But I am also curious to see where the rubber meets the road for them in this respect. Cost could become a limiting factor, especially for smaller vendors. The computational demands of processing vast data sets with AI models are high, I worry this might hinder some companies securing additional and continued funding needed to keep up with demand.
Looking ahead, as generative AI technologies mature, we鈥檒l likely see smarter, more intuitive tools capable of delivering even more nuanced insights. Companies that innovate by combining multiple AI models for different use cases will have a competitive edge. However, those that remain stagnant risk falling behind as the space rapidly evolves.
See 极乐视频鈥檚 2024 ranking of enterprise-ready analytics and social listening solutions by downloading the full 2024 AI Landscape report today.