AI habits and what they mean

The current state of AI in the workplace

While there’s plenty of conversation about SaaS and tech companies rapidly adopting AI across their operations, from a marketing perspective, we should be even more interested in how individual employees are using large language models (LLMs).

From entry-level staff to senior leadership, people are increasingly turning to AI tools to accelerate their work, automate repetitive tasks, and enhance decision-making. Generative AI is being used to draft emails, summarise documents, write code, and analyse spreadsheets, in just seconds. For example, a field study on customer-support agents showed 15% productivity gains when using AI agents.

According to research done by Gallup, managers use AI tools for the following purposes:

What about the conversation you don’t tell your boss about?

Interestingly, some studies uncovered that 57% of employees hide getting support from these tools from their employers. There’s still a lingering fear that using AI might be perceived as cheating and workers likely using personal accounts to carry out work tasks, feeding them information about all aspects of their life.

The above leads us to think – should we care about what people are using AI for on a personal level too? Absolutely, it’s always been a mistake to think of personas for B2B as users whose entire media consumption revolved around their work title, but with the rise of AI, it’s just become more evident.

If you think about how vector embeddings, retrieval-augmented generation and memory create a hyper-personalised environment for users, with no distinction between personal and professional requests, its logical to have some interest in the usage of AI in general, so without delving into the risks of some of these, we’ll just leave some of the most heavily reported use cases in Marc Zao-Sanders report:

1. Therapy / Companionship
2. Organise my life
3. Find purpose
4. Enhance learning
5. Generate code (pros)

The full study is fascinating, as it reveals a more honest, complex picture of how AI tools are really being used, and challenges us to think creatively about how to place our brands within that context.

How B2B buyers make decisions in the AI era

Back to B2B. While online shopping doesn’t yet feature as one of the main uses for artificial intelligence, increasing reports of LLM-attributed closed deals suggest that decision-makers are no longer reliant solely on traditional search or analyst reports, they’re asking tools like ChatGPT to summarise supplier options, generate shortlists, or interpret technical documentation. This shift is shortening the consideration cycle and raising expectations for instant, tailored insights.

For marketers, this means surface-level content and generic messaging won’t cut it. If your product information isn’t structured clearly enough for an AI to parse, or your differentiators aren’t visible in summarised outputs, your brand risks being absent at key moments of buyer intent. In this AI-mediated environment, trust signals, technical depth, and strategic clarity are not optional, they’re essential.

Reaching audiences in LLM AI Search

Fundamentally, users are still ‘searching’ for answers and educating themselves before completing a purchase, it’s the platforms being used that have changed. So, whether we call it SEO, GEO or AIO, creating useful content and obtaining visibility remains key.

Content must be not only high-quality, but structured in a way that makes it easily retrievable, scannable, and summarisable by large language models. If your brand’s value proposition, proof points, or differentiators aren’t clearly stated and semantically linked, AI systems may overlook you entirely.

Additionally, off-site signals work in a similar manner as they did with SEO – digital PR and naturally inserted mentions in Reddit forums or social media platforms are a must to gain relevance in AI discovery.

So, what can we takeaway from this?

To stay competitive in an AI-first decision-making landscape, B2B marketers should take the following steps:

Accept change – It’s unrealistic to expect strong organic traffic from search while continuing to apply outdated tactics. Accepting that transformation is necessary will separate future-ready brands from the rest.

It’s time to learn – Marketing teams must build fluency in generative AI, large language models, and agentic AI. Just like learning GA4 or Google Ads, this is now a foundational skill for strategic success.

Create content for humans and AI – Once you understand how LLMs interpret information, it becomes easier to create content that performs. Develop clear internal frameworks to guide new content creation and revisit existing assets to make them AI-ready.

Break silos, get teams working together – Integrated strategies across paid, social, organic, and PR can create modular, multi-format content that reaches users across more touchpoints and funnel stages.

Information is power, start gathering data – Just like tracking rankings in traditional SEO, begin measuring how your brand appears in LLM-generated answers. New tools are emerging to help teams of all sizes monitor and respond to performance in AI-driven environments.