The year in review: Integration, unification and collaboration

Over the last twelve months we’ve seen developments focus on a few key areas.

What’s ahead for SITech?

As well as improvements in existing SITech features, some new categories and areas of innovation emerged in 2024/ 25. These are worth keeping an eye on as they develop.

The majority of social listening tools are AI-powered, integrating features such as sentiment analysis, topic detection and automated reporting. 2024/ 25 has seen many SITech providers enhance these features, often by incorporating GenAI for deeper analysis and contextual understanding of posts and brand mentions.

The last 12 months has seen the rise of truly autonomous AI agents. Some SITech providers are now enabling companies to build and deploy their own agents that can essentially act as an additional team member: making decisions, planning, designing workflows and executing tasks at scale. This is a significant AI innovation to watch.

Over the last year there have been a few mergers and acquisitions. Hootsuite and Talkwalker merged in April 2024; Buxton merged with Elevar in 2024 and then with Audiense in 2025; Onclusive concluded its integration of Digimind, rebranding as Onclusive Social in 2025, and Meltwater acquired Infomart in June 2024. In addition, LinkedIn discontinued their employee advocacy tools and Meta shut down CrowdTangle in, replacing it with Meta Content Library.

Reporting and visualisation tools have focused on making insights more accessible to internal and external stakeholders. Crisis management features have also improved to allow more proactive listening and better sharing of information, for example through enhanced alert systems.

AI Integration

Vendor shifts

Enhanced Collaboration

Agentic SITech

Combating misinformation and disinformation

Audio-based listening

AI-native and full stack tools

Tackling data quality

As podcasts and audio content on social platforms continue to grow in popularity, SITech players are moving towards greater integration of audio analytics into social intelligence platforms. This isn’t just enhanced audio listening as part of social video. There are some players dedicated to audio only.

For the third year in a row, the State of Social Listening shows data quality is a major challenge. This has become worse with the rise of bots and increasing amounts of GenAI-created content, making people question the authenticity of the data they’re analysing. As a result, more tools and features are focusing on reducing this.

We’re also seeing the increasing threat of mis- and disinformation for brands, as GenAI-based technologies allow it to be spread at scale. This is driving innovation in tools designed to identify inauthentic activity, detect misleading or inappropriate content and verify engagement authenticity. There has also been a rise in tools focused specifically on risk and reputation intelligence.

The most significant emerging trend is the shift from AI-powered (where AI is integrated into a platform), to AI-native tools. Here, AI forms the foundation of a platform’s entire solution. This means data processing, insight generation and UI are designed to leverage AI and ML throughout the product, not just as a feature layer.

AI vs GenAI

As advancements in AI have developed, the term has been confidently thrown around to describe many different things. However, we need to be careful how we use it. We sometimes need to be specific otherwise we risk overselling.

AI is a big field. It incorporates many things: machine learning (ML), robotics, computer vision, natural language processing (NLP)…but these days, when people talk about AI, they’re often referring to generative AI (GenAI). This can create original content.

A lot of SITech is starting to incorporate GenAI into their platforms. But many tools incorporate, or rather are built on, other types of AI. NLP and computer vision are foundational for analysing text, images and video. This isn’t new. Of course it’s important, and there is a lot of exciting innovation happening in these spaces too. But if you’re buying a tool that promotes its AI-capabilities thinking it’s going to generate insights for you, you might be disappointed. Always clarify if you’re unsure.

A word on AI…

AI-powered vs AI-native

Another increasingly important clarification we need to make is the difference between AI-powered and AI-native social listening platforms. They might sound interchangeable, but they’re not.

AI-powered tools are traditional social listening and analytics platforms that have integrated AI features as add-ons. Their key characteristics are:

AI features integrated into conventional platforms as add-ons
AI functioning primarily within specific feature silos
Traditional workflows enhanced but not fundamentally redesigned

AI-native tools are built with AI as a core architectural component. They’re designed for AI to handle fundamental processing, decision-making and insight generation. Their key characteristics are:

AI is intrinsic to every component of the technology system
The system continuously adapts. It doesn’t follow fixed rules
Resources are built to scale with AI capabilities in mind

From a user perspective, the main differences are around functionality and the extent of automation. AI-powered tools are ideal for traditional social listening use cases and can easily enhance existing workflows without fundamentally changing them. AI-native tools offer faster speed to insight, can support AI agents or virtual analysts and power adaptive workflows that can evolve and scale over time.

Agentic Tools in Social Intelligence

One of the biggest shifts in the last 12 months has been the move towards agentic social intelligence tools: platforms that no longer support analysts, but can now act as analysts (or as community managers, and other roles relevant to social intelligence). These tools deploy autonomous (or semi-autonomous) agents that can perform tasks, generate insights and take actions with minimal human input.

While agent-based functionality is most visible in AI-native platforms, it’s beginning to appear in AI-powered ones as well, typically in more guided forms (e.g., GenAI summarisation, smart prompts).

Agentic tools are emerging across several categories:

Social Listening & Monitoring: agents can automatically generate weekly insight summaries, flag anomalies in sentiment or topic spikes and suggest Boolean expansions for your social listening setup
Trends & Foresight: agents can track narrative evolution and help compose early warning memos when specific language or topic clusters gain traction
Social Analytics: agentic systems can interpret drops or spikes in engagement, forecast growth trends and offer “what happened here?” explanations
CX/RM: agents can route customer complaints or praise across teams and recommend personalised follow-up responses or resolutions
Marketing Strategy: in campaign reporting, agents can now automatically generate wrap-up decks, analyse attribution pathways and recommend creative adjustments.

For social intelligence users this signals a shift from dashboard-driven work to co-piloted workflows, where tools don’t just surface data but actively interpret and act on it. This isn’t a one-size-fits-all development. While some teams may embrace agents as virtual colleagues (especially in the middle of staffing or budget constraints), others may want to introduce them gradually for very specific, low-risk use cases.

Agentic social intelligence is still evolving, but the implications are clear in 2025: these platforms are no longer just tools, they’re collaborators.