How to defend your company in the AI era

Learn how some companies are adapting to AI changing the game, and what you can do to not be replaced.

Written by Harald Roine

Founder & CEO

Introduction

In an era where AI can replicate your product and emulate your service in seconds, and undercut your price by up to 10x, your only defenses are things AI cannot replicate.

Deep trust with your market and proprietary data.

As founders, we need to adapt to a constantly shifting landscape and build real moats to withstand what is coming.

The ideas presented here come from five years of work in AI adoption and business infrastructure. This perspective is shaped not only by marketing, but by operating as a venture studio.

We expect marketing dynamics to change. There will be a regression to the mean, an exodus from traditional channels, and rapid growth in micro-communities.

Part 1: The great deflation

As of writing this in February 2026, market leaders like Salesforce and Hubspot are down 43 and 73% respectively. Stock prices are tanking.

Many believe this is driven by AI and LLMs improving daily, making development faster, cheaper, and enabling a small team to rebuild your software in a short sprint.

However, another factor is that the game of marketing has changed.

Back in 2011, reaching your audience through digital channels was straightforward. You could post on Facebook or other social platforms, run a few ads, and get results.

Today it’s completely different.

Over the past five years, we have seen the emergence of “GTM engineering” and “outbound hypergrowth.” The barrier to entry in marketing is approaching zero.

A university student can spend a month binge-watching YouTube and learn the fundamentals of sending large-scale outbound campaigns, running social selling on LinkedIn, or setting up paid ads across LinkedIn, Facebook, and YouTube.

Combined with the explosion of courses and “gurus” promoting the marketing agency model as the best opportunity for Gen Z, we are seeing a surge in new suppliers.

Taken together, this is driving hyper-competition and faster saturation of tactics and hacks. Marketing is becoming more difficult.

Five years ago, I received only a few cold emails each week. Now, I get 15 to 25 per day.

Why?

It is now easy to find contact details through platforms like LinkedIn, Apollo.io, ZoomInfo, and other data providers.

Clay has accelerated this even further. It has become one of the leading platforms for building go-to-market data, creating prospect lists, and enriching them with detailed insights.

Access to ideal prospects, AI-driven personalization, and automated outbound systems across email, LinkedIn, WhatsApp, and other channels is now widely available.

As a result, the amount of noise in the market has increased dramatically.

This brings us to today and how the landscape is shifting.

AI and LLMs are improving rapidly, becoming faster and cheaper. Reverse engineering and rebuilding software is now far easier.

At the same time, access to tools for finding, qualifying, and reaching audiences has expanded. Anyone can launch outbound campaigns, which leads to saturation and declining effectiveness.

We regularly speak with founders, CEOs, marketing leaders, and sales professionals who say the same thing. It is harder to get through. That is expected when people receive ten times more outreach than before.

Another layer is the rise of AI employees.

At the start of 2026, OpenClaw, which evolved from Clawdbot and Moltbot, launched as an open source project. Within weeks, it reached more than 160,000 stars on GitHub. Created a video on it here

This opened many people’s eyes to autonomous agents that act as employees, execute real work, and improve over time.

Back in 2021, I defined four core components any autonomous AI system needs.

An LLM as the generative engine.
Memory to retain context and past actions.
Planning and orchestration to set objectives and map execution.
“Hands” to interact with tools and take action.

OpenClaw brought these elements together in a single framework, and the response was immediate.

I see this as an early step toward driving the cost of both intelligence and execution close to zero. LLM pricing continues to fall rapidly, and capability is increasing at the same time.

This leads to a core question.

How do you defend a company in a market that changes faster than most teams can keep up with?

"We've used the systems shared in the thesis to reach tens of millions of people, generate 170,000 comments and 65,000 B2B leads from LinkedIn as the only platform in 2025. All without being spammy, salesy, pitching all the time - or rely on paid ads. Now we share the macro trends that shaped our strategy, so you too can see how you can use it for your company."

- Harald Roine

Founder & CEO - BURO Ventures

Part 2: The first wall of defence

What I will cover going forward is based on first-hand experience, the research paper Attention Is All You Need from Google, and where we see AI taking the future of companies.

There are several layers of defence you can build. The most obvious one is owning your distribution.

You need the ability to reach your audience when you want, with what you want, without relying on third-party platforms such as ads, agencies, or social networks.

Since 2011, most companies I have spoken with follow a similar distribution model.

They run ads across platforms like Meta, LinkedIn, and Google. (guide on retargeting ads)
They rely heavily on outbound activities such as cold calling, cold email, and LinkedIn outreach. (guide on LinkedIn outreach here)
They try to convert attention into revenue as quickly as possible.

Some invest in content and long-term audience building, but most do not.

As a result, many companies rent attention through ads and agencies. This creates risk because the platform owner can change the rules or increase costs. Owning your infrastructure removes that dependency.

A CRM like HubSpot, combined with basic tagging and pipeline tracking, is not enough. You need centralized infrastructure and direct ownership of your audience and data.

You need a centralized infrastructure that you own.

Your marketing strategy should at minimum do one thing: build trust. Trust is the primary currency in B2B. It is becoming harder to earn as competition increases, content volume rises, and the overall noise grows.

For more than ten years, we have focused our own and our clients’ marketing on providing value. Content marketing, in simple terms. An exchange where the audience gives attention in return for something useful.

This usually takes the form of a resource, guide, PDF, webinar, recording, template, checklist, cheatsheet, tool, prompt, or similar asset. Something tangible that helps them solve a problem or learn something practical.

When you give something away, several things happen. It lowers resistance because the audience receives immediate value. It also activates well-documented psychological effects, including reciprocity. When someone receives value, they are more open to engaging in return.

Consistency strengthens this further. Repeated exposure builds familiarity and credibility over time. After enough touchpoints, people form a clear opinion. They either trust you or they do not. That polarity is useful. It filters out poor-fit customers and allows you to focus resources on the right ones.

The difference between having this and not having it is significant. Conversations become easier, and the unit economics improves. Inbound meetings driven by trust feel closer to order-taking than traditional selling.

This is the foundation of what we believe will be a core defense for companies going forward.

The trust flywheel.

Most companies focus on the top of the funnel and lead generation. They often ignore what happens after the leads are created.

This system nurtures leads with relevant content, practical resources, and consistent value. As people engage with your content and brand, their behavior is tracked. Every click, reply, and visit becomes a signal.

Based on that engagement, each lead receives a score. High engagement indicates stronger intent. Low engagement signals early-stage interest. Leads are then segmented, and the communication cadence is adjusted. Highly engaged prospects receive more direct and frequent contact. Lower-engagement leads stay in lighter, value-driven nurture flows.

Each segment receives the right content at the right time. Distribution happens across posts, ads, email, and other owned channels.

The underlying idea is simple. Interest develops in stages. Someone starts with curiosity. They explore. They engage. Over time, they commit.

It is a bit like Alice in Wonderland. She starts by being curious about what’s down the rabbit hole. She begins to think about it, then considers taking a closer look. She peers into the rabbit hole, and eventually dives deep into it.

The trust flywheel accelerates someone’s dive into the rabbit hole when they show interest. It also pulls them back out when that interest fades. Automatically.

When you have the ability to provide value, systematically engineer attention, and segment your market based on engagement level, trust, and fit, you have your first wall of defense. You own the relationship and the distribution channel.

Part 3: The second wall of defence

Another wall of defence is data. In our way of describing this, we’ll focus on two areas; marketing and product.

The core idea is that you need to build proprietary data that enables a reinforcement learning loop. A loop that improves every part of what you do. It makes your marketing more tailored and optimized, and it improves how your product performs.

Let’s start with marketing.

For marketing, we go back to the first wall of defence: the trust flywheel. The system that engineers attention and trust.

By owning your data and aggregating engagement signals from your audience, you are sitting on a gold mine.

Which posts perform
Which emails perform
Full access to all your long-form content
Engagement trends
How quickly someone progresses
Client profiling
How everything ties back to KPIs and performance

All of this is gold, especially when you look at it from an engineer’s perspective and ask, “How can we use AI to supercharge this?”

In short, AI can identify trends, create plans, optimize them, draft content, and generate reports.

More concretely, imagine that every Friday AI analyzes the past week’s performance. It compares the results to monthly and quarterly benchmarks and identifies gaps in your marketing. It detects that a lower-value audience segment grew faster than your top-tier ideal audience, and pinpoints which content drove that shift. It then analyzes which pieces generated the highest-quality leads and proposes a plan, what to resurface, what to create next, and how to shift the focus back to quality. Then it executes. It drafts the content, places it in a review board for your team to approve or edit, and schedules everything that is ready to go. Now imagine this running every single week.

Personally, this makes my marketing brain go into overdrive. In 2025 alone, we generated 170,000 comments, 10M+ impressions, and 65,000 leads from LinkedIn alone, without spending $1 on ads. We published roughly 1,000 posts.

We now know exactly which content to double down on. AI can draft and produce it at scale, as long as we review it and ensure the quality meets our standard.

Now, let’s bring that back to your company.

Data should inform decisions and serve as the layer that enables AI to handle planning, drafting, and execution. Let it do the heavy lifting.

Without data, you cannot operate this way. You fall back on gut instinct and surface-level observations.

We built a free platform where you can build your offer, research your audience, write posts, emails and ads, as well as video scripts. Register here.

Next is the product.

Just as with marketing data, we want to apply the same approach to the product itself, the delivery behind what we sell. This often starts with clearly defined SOPs, standard operating procedures that outline exactly how work gets done. Step 1 to X.

If the full process is defined with prerequisites, tasks, subtasks, outputs, and KPIs or benchmarks, AI can run a similar process. It can assess how long it takes to draft XYZ, identify the required inputs and expected outputs, and follow the defined steps. With a clearly structured system, it can emulate the full process.

Bringing this back to a concrete example. We build trust flywheels for companies, marketing infrastructure designed to systematically build trust and engineer attention.

This has two components: the technical infrastructure and the content layer, resources, emails, posts, and similar assets. There are also two phases: setting up the engine and running it.

During setup, we rely on initial content drafting. The more performance data we have across clients, the better we become at identifying patterns and what actually works. That data allows us to draft content based on proven performance, not opinion or short-term trends, but what delivers results consistently over time. This improves the quality of the initial build.

The second phase is running the engine. When the product continuously gathers data and AI analyzes it, we embed an optimization loop directly into delivery. We can then evaluate performance and improve the system autonomously, iteration after iteration.

In short, the more clients you serve, the more data you collect. The more data you collect, the better AI can optimize and emulate your processes. The product improves as the dataset grows.

More concretely, when you think in terms of optimization loops, required data, and defined processes, you can clearly identify bottlenecks and remove them. As you gather more data, your deliverables improve. Imagine having 500,000 data points from your market instead of operating on guesswork. Data on what the market engages with, when they engage, and what drives action. That dataset becomes proprietary. A new entrant cannot replicate it.

This becomes your second wall of defence.

Start thinking in terms of data, and optimization loops.

Where does this lead?

Without a proper data layer, AI remains a surface-level tool. With your data, it becomes leverage. Without context and memory, even the most advanced model cannot operate effectively.

Data turns AI into a moat. You can produce outputs others cannot, because they do not have access to your dataset. They do not have your client logs, your trust and attention metrics, your content performance data, or your product performance benchmarks.

That data layer, your second wall of defence, compounds daily. Every new data point strengthens the system. A competitor starting from zero cannot realistically catch up to a system that has been learning from its own data for two years.

Part 4: The offense

When you combine owned distribution through the trust flywheel with the data layer, the brain, you get something powerful.

This is one of the long-term directions I see for BURO. We are building an autonomous venture studio that spins out new solutions, offers, products, and services on a continuous basis.

Over time, the data across all areas compounds.

What is the best go-to-market strategy given niche, industry, product type?
What is the best sales mechanism?
What makes people buy?
And what’s the best delivery vehicle?

Over time, we will have enough proprietary data to increase the success rate of the bets we make. Done autonomously.

More companies should think in terms of compounding data and how to use it to spot opportunities, strengthen their value proposition, and stay ahead.

Data, however, is a means to an end.

When you have a systematic way to engineer attention and trust at scale, you can build an audience in any market.

An audience you own. An audience where you shape the narrative.

Next, I want to share a starting point.

Every company depends on pipelines. They need a predictable way to generate new clients.

For B2B companies, most lead generation falls into two categories: outbound and inbound.

Outbound includes any activity where you actively hunt for prospects. You identify your target audience and reach out directly. This covers cold email, cold calling, LinkedIn outreach, and even door knocking.

Inbound includes activities where your audience discovers you. You create content, distribute it, and attract interest over time. This includes podcasts, social media, blog posts, SEO, and paid advertising.

For most B2B companies, we usually recommend starting with one or both of these.

First, LinkedIn outreach. Run connection requests and messaging campaigns at scale. Use a tool like GetSales, connect multiple profiles from your sales team, and begin structured outreach.
Second, create up to 10 high-value resources, guides, tutorials, or tools that solve real problems for your audience. Share these on LinkedIn using a simple call to action such as, “I created this. Comment X and I’ll send it on DM.” Have the team engage with the post early to increase reach. Use a tool like LeadShark to automate delivery.

These are the first steps. In 2025, we focused only on the second approach and generated 65,000 leads for BURO.

Next comes setting up the flywheel.

For this, we use Encharge. It is one of the most flexible email automation tools available, especially when it comes to integrations and flow customization.

Inside Encharge, we set up a few core systems.

A looping email sequence. This is a 20+ email nurture flow that runs across all leads. Each email directs people to one of your resources. The goal of the email is only to drive engagement with the content. The call to action for booking or sales lives inside the resource itself. The sequence runs over 20 weeks and then loops.
Automated scoring. Every lead receives a score based on behavior. Opening emails, clicking, and visiting resources or pages increases the score. Low engagement over time reduces it.
Automated segmentation. Leads are grouped into cold, warm, and hot segments based on their score.
Retargeting sync. These segments are synced with ad platforms such as Meta and LinkedIn. Each segment receives different retargeting frequency and messaging. This accelerates movement through the funnel.

With this in place, the core flywheel is operational.

On top of this, we build the actual data layer so you can extract insights and continuously optimize the system.

With these elements in place, you have a strong starting point.

If you want to join a workshop with us and get our recommendations on when and how to implement this, click here. It also includes a video that walks through the full approach in more detail.

Conclusion: Adapt or fade

The gap between companies that leverage these systems and those that don’t is widening every month.

On one side, you have founders still relying on brute-force outbound, fighting for attention in a saturated market, watching their CAC rise while their efficiency drops.

On the other side, you have the "engineered" companies. They own their distribution. They possess proprietary data loops. They have automated the low-leverage work so they can focus on high-leverage decisions.

This is not just a marketing upgrade; it is a fundamental shift in how B2B companies operate.

The era of "lazy growth" is over. The era of engineered trust has begun.

We have spent five years building this infrastructure at BURO so you don’t have to start from scratch.

If you are ready to install this operating system into your business, let’s talk.

Click here to continue now

- Harald Roine

Founder & CEO - BURO Ventures

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