But when you look beyond the narrative, there is a noticeable gap between what companies say and what they actually do.
At Remora, we are seeing this first-hand across venture-backed and PE-backed B2B SaaS businesses. AI is a priority on paper. In practice, it is often fragmented, underutilised or misunderstood.
This gap is where opportunity sits.
The illusion of adoption
Most companies believe they are “using AI” because they have experimented with tools. They may have generated some content, automated a few workflows or trialled a chatbot.
That is not adoption. That is exploration.
We saw this dynamic years ago in a previous business we worked on together. Zafar was one of the first in the team to push beyond experimentation and deploy AI in a way that actually changed how we operated. It was not about novelty. It was about improving decision-making, reducing manual work and enabling the team to move faster. That distinction still holds today.
Real adoption shows up in measurable changes to how a business operates. It impacts conversion rates, sales cycles, cost structures and customer experience. It becomes embedded in systems, not layered on top of them.
The difference is material.
Where AI is actually creating value today
Strip away the noise and a few high-impact use cases consistently stand out in B2B environments.
1. Pre-sales intelligence and qualification
One of the most immediate gains comes from improving how leads are qualified and routed.
Instead of static forms or generic chatbots, AI-driven interfaces can interpret intent, answer product-specific questions and guide prospects towards the next step. This includes booking demos, surfacing relevant case studies or matching users to the right product within a suite.
The result is not just more leads. It is better leads entering the pipeline with clearer intent.
This is an area we are actively exploring at Remora, particularly around AI-led website experiences that act as a first layer of sales support rather than just a passive channel.
2. Content that actually converts
AI has flooded the market with content. Most of it is low quality and interchangeable.
The real opportunity is not volume. It is precision.
When used properly, AI can help map content directly to buying stages, personas and objections. It can accelerate research and structuring, but the strategic layer still matters.
From my perspective, this is where many teams fall short. The thinking has to come first. AI should sharpen it, not replace it.
3. Sales enablement at scale
Sales teams are often under-supported when it comes to tailored messaging and materials.
AI can bridge that gap by dynamically generating responses, proposals or summaries based on specific prospects. It can also analyse calls and interactions to identify patterns, objections and opportunities for improvement.
Zafar is currently deep in this space, exploring how AI can support marketing and sales teams more directly through implementation, not just tooling. The focus is on practical deployment rather than theory.
This is where efficiency gains start to compound.
4. Operational efficiency
There is a quieter but equally important use case in internal operations.
From automating reporting to streamlining campaign execution, AI can reduce manual workload across marketing and revenue teams. This frees up time for higher-value activities such as strategy, positioning and experimentation.
We have both seen how quickly these gains add up when applied consistently.
Why most companies are still stuck
If the opportunities are clear, why is adoption lagging?
There are a few recurring issues.
Lack of a defined use case
Many businesses start with the tool rather than the problem. They implement AI because it feels necessary, not because they have identified a specific bottleneck or opportunity.
Without a clear use case, initiatives drift.
Fragmentation across teams
AI efforts often sit in silos. Marketing might be using one set of tools, sales another, product another.
This creates inconsistency and limits impact. The real gains come when AI is aligned across the revenue engine, not isolated within functions.
Over-reliance on generic tools
Off-the-shelf solutions are easy to access but rarely tailored to a company’s product, customers or data.
This leads to outputs that feel generic and fail to differentiate.
No clear ownership
AI initiatives often lack a defined owner. They sit somewhere between marketing, product and operations.
Without accountability, progress stalls.
Bridging the gap
Closing the gap between intent and execution does not require a complete overhaul. It requires focus.
At Remora, our approach is grounded in what we have seen work in practice.
1. Identify one high-impact use case
Rather than trying to implement AI across the entire business, focus on a single area where it can deliver measurable value.
2. Integrate, do not bolt on
AI should not sit as a standalone feature. It needs to be integrated into existing workflows and systems.
3. Iterate quickly
The first version will not be perfect. That is expected. What matters is the speed of iteration and learning.
The competitive angle
AI is not just about efficiency. It is about positioning.
In crowded markets, buyers are looking for clarity and confidence. If your digital experience can answer questions instantly, guide users intelligently and reduce friction, it creates a tangible advantage.
We are already seeing this become a differentiator in the businesses we work with.
What this means for investors and operators
For investors, AI capability is becoming a meaningful indicator of operational maturity.
For operators, the opportunity is to move ahead of the curve. Most competitors are still experimenting. Few have embedded AI in a way that drives consistent results.
Zafar’s early work in this space, combined with what we are building now at Remora, reinforces a simple point: the advantage does not come from talking about AI. It comes from applying it in a way that drives outcomes.
Where Remora fits
This is an area where we are increasingly working with clients.
Not to implement AI for the sake of it, but to identify where it can directly support growth. That often means improving how prospects interact with a business online, how leads are qualified and how teams operate behind the scenes.
It is a practical approach, grounded in outcomes rather than hype.
Because ultimately, AI is not the strategy. It is an enabler.
If there is one takeaway, it is this: the gap between talking about AI and actually using it is still wide.
Closing that gap is not about doing everything. It is about doing the right things, in the right places, with intent and that is where the real advantage lies.
