AI has become part of my daily work.

I use it to build presentations, analyze campaigns, challenge event concepts, sharpen messaging, research markets, and turn rough ideas into something tangible much faster. I regularly move between ChatGPT, Claude, Gemini, and Perplexity, not because one is necessarily better, but because each is useful for different tasks.

AI helps me do more in less time. It gives me the ability to explore more directions, move from idea to execution faster, and increase the output of a lean marketing team.

I also see its limitations every day.

AI confidently gets facts wrong, produces generic copy, misses important context, and occasionally destroys a perfectly good design. It can generate something polished in seconds, but polished does not necessarily mean interesting, accurate, or effective.

Recent Gartner research on how marketers successfully use generative AI reinforces what I have experienced firsthand: the teams creating the most value are not necessarily using the most tools. They are the ones changing how they work, while understanding where technology ends and human judgment must begin.

Here are five principles that I believe make the difference.

1. Start With the Problem, Not the Tool

“We need to use AI” is not a strategy.

The starting point should be a real problem: research takes too long, content is not being reused effectively, outreach cannot be personalized at scale, or valuable knowledge is scattered across the organization.

Most marketing teams began with relatively simple tasks such as drafting posts, translating content, suggesting subject lines, or summarizing documents. These are useful experiments, but they are only the beginning.

The real value comes when AI solves a recurring business problem, not when it simply gives the team another place to type a prompt.

2. Use AI to Think, Not Only to Produce

Some of the most valuable work I do with AI never becomes external content.

I use it to challenge assumptions, identify missing perspectives, compare different approaches, and argue against my preferred direction. When planning an event, for example, it can help me question whether the concept is genuinely distinctive, whether the audience mix makes sense, or whether the value proposition is strong enough.

For a lean marketing team working across events, content, community, portfolio support, partnerships, and brand, this creates significant leverage. We can test more ideas and move from a rough concept to real execution much faster.

But producing more is not the same as achieving more.

AI accelerates the work. It does not decide which work is worth doing.

3. Measure Outcomes, Not Output

Creating twice as many posts is not necessarily an achievement.

A presentation completed in half the time is only valuable if its message is clear. More personalized emails matter only if they lead to better engagement. More campaigns are not progress if they do not reach the right people or support the business.

Gartner’s research emphasizes the importance of establishing a baseline and measuring the actual effect of AI. That means looking at time saved, shorter review cycles, stronger performance, lower costs, and new capabilities, not simply the volume of content produced.

The most important question is not “How much did we create with AI?” It is “What became faster, better, or possible because of it?”

4. Keep the Human Touch

At Glilot Capital, our marketing is built around relationships and trust, with founders, investors, CISOs, portfolio companies, and partners across the technology ecosystem.

AI does not know the full history behind those relationships. It does not always understand why a message might work for founders but not for CISOs, the dynamics between partners, or when a technically correct answer is still the wrong thing to say.

That context still comes from people.

Anything published under our name needs a human owner responsible for its accuracy, quality, and tone. AI can support judgment, but it cannot take responsibility.

As content becomes easier to produce, the marketer’s value moves away from creating the first draft and toward asking the right questions, recognizing what is genuinely interesting, and protecting the credibility of the brand.

AI accelerates marketing. The human touch still makes the difference.

5. Create for People and for AI

One of the most important shifts highlighted by Gartner is that content increasingly serves two audiences: people and AI systems.

Customers may discover a company through Google, LinkedIn, an event, or a recommendation. But they may also ask an AI assistant to explain a market, compare vendors, identify experts, or recommend a solution.

This means content must be clear, structured, credible, and specific enough to be understood by both humans and machines. Traditional SEO alone is no longer enough. Brands need to become trusted sources that AI systems can recognize, interpret, and reference.

The fundamentals of strong marketing have not changed. Clarity, relevance, authority, and trust still matter. The way people discover them is changing rapidly.

Making AI Real

Soon, every marketing team will have access to powerful AI models. Access itself will not create a sustainable advantage.

The advantage will come from everything surrounding the model: proprietary knowledge, unique relationships, strong workflows, brand trust, and people who know when to trust the output and when to challenge it.

At Glilot Capital, Make It Real is how we think about turning ambitious technology into meaningful impact. The same principle applies to AI in marketing.

Making AI real is not about impressive demos, generic content, or adding another tool to the stack. It is about integrating AI into the way we think, decide, and operate, and connecting it to outcomes that genuinely matter.

AI gives us more speed, scale, and leverage. The human touch gives that power direction, context, and meaning.

That is where the real advantage is created.

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There are companies you become excited about because of the market, and there are companies you believe in because of the people. With Way Security, it was both.

When I first met the founders, I was struck by the depth of their thinking, the clarity of their ambition, and the way they approached a complex and crowded space with a genuinely fresh perspective.

They weren’t trying to build another identity point solution. They were asking a more fundamental question: what should identity security look like when the enterprise itself has changed?

That conviction led me to spearhead Way’s seed investment, and today I’m incredibly proud to see the company come out of stealth.

What made the opportunity so compelling wasn’t just the extraordinary quality of the team, but the scale of the architectural shift they had identified.

Identity Security Has Been Treated as a Collection of Separate Problems

For years, identity security has been treated as a collection of separate problems.

Organizations deployed one set of tools to manage workforce access, another to govern privileged accounts, another to monitor identity threats, and still others to manage cloud permissions, machine identities, contractors, and third-party access.

Each product addressed an important part of the identity problem. But the overall architecture remained fragmented.

That fragmentation was already difficult to manage. AI is now making it unsustainable.

Today’s identities are dynamic and that creates a fundamental mismatch between the identity infrastructure most enterprises have today and the identity reality they are being asked to secure.

This Is the Problem Way Security Was Created to Solve

This is the problem Way Security was created to solve.

Way Security starts from a truth most of the industry ignores: enterprises already own the right identity tools. What’s missing is not another product. It is the ability to enforce those tools on every application and every identity, including the legacy, homegrown, and non-standard systems where enforcement has always broken down. Way Security connects the IAM stack an enterprise already runs to the apps and identities it could never reach, and makes every control hold there: authentication, provisioning, governance, hygiene. Nothing replaced. Everything is enforced.

That’s not an incremental improvement. It’s a change in the operating model.

Why This Matters Now

AI doesn’t create the identity problem, but it dramatically accelerates it.

AI agents are being connected to enterprise systems, granted access to sensitive data, and allowed to act on behalf of users and business processes. In many cases, organizations don’t yet have a consistent way to understand what those agents can access, how their permissions interact, or what happens when their behavior changes.

At the same time, human and machine identities are becoming increasingly interconnected.

The distinction between workforce identity, workload identity, application identity, and agent identity is becoming less useful from a security perspective. Attackers do not care which organizational team owns a credential or which product manages an entitlement. They look for the path that gives them the reach they need.

Security architecture must evolve accordingly, and that’s the paradigm shift Way is building toward.

A Category Validated, but Not Yet Won

The emergence of additional companies lately in this space is meaningful validation.

It confirms that the market increasingly recognizes identity as a foundational layer of enterprise security. rather than a collection of isolated administrative workflows.

But categories aren’t won by huge marketing campaigns.

They are won through architectural clarity, product depth, customer trust, and the ability to turn an ambitious thesis into something security teams can deploy and rely on.

Why My Conviction in Way Is Especially Strong

This is where my conviction in Way is especially strong.

From the beginning, the Way team has shown an unusual combination of technical depth, product judgment, humility, and ambition.

They listen closely to CISOs, but they don’t simply build a collection of customer requests. They use those conversations to refine a clear view of where the market is going and what the architecture needs to become.

They move quickly, challenge their own assumptions, and remain intensely focused on the problem.

That combination matters enormously in a category this complex.

As a seed investor, the greatest privilege isn’t just backing a company before it becomes visible. It is supporting founders while they define a category, sharpen the product, engage early customers, and make the difficult decisions that will shape the company for years to come.

I feel fortunate to be on that journey with the Way team.

And I know this is only the beginning.

Way Security raised a $20 million seed round from Insight Partners and Glilot Capital, CEO Yossi Barishev tells Axios Pro exclusively.