Businesses investing in LinkedIn for B2B lead generation may be undermining their own results by applying outdated prospecting habits to social selling. That is the central warning in a fresh release from LinkedIn training specialists Maverrik, as reported on Pressat. The company, drawing on more than a decade of work with sales teams, has identified ten recurring mistakes that stop LinkedIn activity from turning into real sales opportunities. The key takeaway: more activity on LinkedIn does not automatically produce a stronger pipeline.
The core problem: treating LinkedIn like cold email
According to Dean Seddon, Founder and CEO of Maverrik, one of the most significant challenges is the assumption that traditional sales techniques will deliver the same results on social platforms. He puts it plainly: "Too many sales teams treat LinkedIn like another place to send cold emails. They invest". The source article is cut off at that point, but the framing is clear: a platform built around professional relationships is being used as a broadcast channel. Sales reps who open with a pitch, push a product before any rapport exists, or send templated connection requests tend to see their messages ignored or declined. The result is that effort rises while conversion stays flat.
Seddon's point connects to a broader pattern. Sales teams often measure LinkedIn by volume, such as connection requests sent, InMails delivered or messages queued. Volume is easy to track, but it says little about whether a prospect actually engaged. Shifting the measure from output to response quality, meaningful conversations and qualified meetings is one of the simplest corrections a team can make. A useful test is whether each outreach message would still make sense if it arrived in a prospect's inbox as a standalone email from a stranger.
Messaging, process and the premature use of automation
The Maverrik release groups its ten mistakes around three themes: poorly structured prospecting processes, ineffective messaging, and the early adoption of artificial intelligence and sales automation. Each carries a distinct cost. A weak process means reps do not know which prospects to target, when to follow up or how to hand a warm lead to the next stage. Weak messaging means profiles and outreach fail to explain why the sender is relevant to the reader. Premature automation is perhaps the most counterintuitive risk: when tools send generic sequences at scale, the personalisation that makes LinkedIn work disappears, and prospects recognise the pattern quickly.
The recommended correction is not to abandon automation or AI but to sequence them correctly. Teams should first establish a clear ideal-customer profile, a process that defines each stage of engagement, and messaging that reads as human. Only then should tooling be layered on to speed up research, scheduling or follow-up. Organisations that skip this foundation risk scaling a broken approach. This is the sort of structured thinking that PhewDo aims to support for teams building repeatable outreach.
Comparing how sales-focused tools approach the same problem
Many vendors now promise to fix LinkedIn prospecting with AI. The table below shows how three commonly referenced players differ in focus and how the mistakes above map onto practical capabilities. Vendor positioning changes often, so readers should verify current features on each company's site, including Microsoft, whose LinkedIn business products are one route teams use, and OpenAI, whose models are widely used to draft outreach.
| Approach | Primary strength | Risk highlighted by Maverrik |
|---|---|---|
| Generic AI writing tools (e.g. OpenAI models) | Fast drafting of outreach copy | Generic output that loses personalisation |
| Sales engagement and automation platforms | Scaled sequences and tracking | Automation applied before the process is sound |
| Manual, relationship-led social selling | Authentic, context-rich conversations | Slower, harder to measure without clear KPIs |
Capability → How PhewDo does it
| Capability needed | How PhewDo does it |
|---|---|
| Clear prospecting process | Defines target profiles and stage-by-stage steps before any outreach |
| Human-sounding messaging | Helps teams write personalised openers tied to each prospect's context |
| Responsible automation | Adds tooling once the process and messaging are proven |
| Measuring real results | Tracks replies and meetings rather than raw activity volume |
What a fresh-start audit could look like
For teams worried they are making these mistakes, a practical starting point is a short audit of the last 30 days of LinkedIn activity. Review how many connection requests were accepted, how many conversations reached a second message, and how many led to a call. Compare the openers against the test of relevance: does each one reference something specific to the prospect? Check whether automation is sending identical copy to unrelated audiences. Finally, confirm that a single owner is accountable for the process. Most of the ten mistakes identified by Maverrik can be spotted in this kind of review without any new software.
Frequently asked questions
Why does sending more LinkedIn messages often fail to generate more clients?
Maverrik argues that many teams apply cold-email tactics to LinkedIn, so higher volume brings more ignored messages rather than qualified conversations.
Should sales teams avoid AI and automation on LinkedIn altogether?
No. The guidance is to adopt AI and automation after a clear prospecting process and human-sounding messaging are in place, rather than before.
What is the quickest way to spot LinkedIn prospecting mistakes?
Review recent activity for acceptance rates, follow-up conversations and whether openers are personalised, since these reveal most of the issues in Maverrik