We spend a lot of time watching what’s happening globally in our space, talking to clients, reading between the lines of what the bigger consultancies are publishing, and generally trying to stay ahead of what’s coming. It’s honestly one of our favorite parts of the job.

Here’s what’s been genuinely interesting over the last two years or so. Some of it will be familiar. Some of it might open up a useful conversation.

Behavioral triggers are replacing campaign calendars

This one is close to what we do, and it’s gaining real momentum. The batch-and-blast approach — where you decide what to say, segment your database and hit send — is becoming less and less effective. Not just because the tools have improved, but because customers have changed.

The businesses doing lifecycle marketing well are treating every communication as part of a longer conversation. Messages triggered by real behavior. Content shaped by where someone actually is in their relationship with the brand. And measurement that goes beyond open rates to look at retention, pipeline and lifetime value.

It’s a more considered approach to customer communication, and the results tend to reflect that.

AI has finally moved from the boardroom to the build — and the results are real

For a while, AI in CRM and lifecycle marketing was mostly a talking point. A pilot here, a proof-of-concept there, and a lot of confident presentations to equally confident leadership teams. Many organizations ran these experiments and then quietly struggled to extract any real value from them.

That’s shifting. The smarter agencies are no longer selling “AI transformation” as a concept. They’re identifying very specific use cases and implementing them with proper discipline. And in our own work, we’re seeing this play out in ways that are genuinely hard to ignore.

The most immediate wins have been in customer data and dynamic segmentation. AI is making it significantly faster and more accurate to identify meaningful patterns in customer behavior — who’s at risk of churning, who’s ready to buy again, who’s responding to what kind of message and when. Personalization that used to require weeks of manual segmentation work can now be configured, tested and refined in a fraction of the time. The output, when it’s done thoughtfully, is better communication and measurably stronger retention.

Churn prediction baked into CRM workflows. Propensity scoring that informs what gets sent to whom, and when. Onboarding journeys and renewal triggers that respond to real customer behavior rather than a pre-set schedule. The promise has always been real. The difference now is that the best operators are much more deliberate about where and how they apply it.

But it’s worth being clear-eyed about where this goes.

The early-adopter advantage is real right now. Businesses and agencies that have leaned in thoughtfully are ahead of those that haven’t. The problem is that moat is compressing fast. As the underlying models get better and more accessible, the advantage of having been early disappears — and it disappears for everyone at roughly the same time.

There’s also a more uncomfortable question sitting underneath all of this. Every time our industry uses these tools to solve a client problem — training models on our data, our frameworks, our accumulated knowledge — we’re contributing, in some small way, to systems that will eventually do a version of that work without us. We’re not sure that conversation is happening seriously enough yet. And we’re not sure there’s an easy answer to it.

The jobs question is the other side of this, and it deserves more honesty than it usually gets. AI is already covering significant portions of what marketing teams and agencies produce — and it’s doing it faster and at lower cost. The functional, executional middle of our industry is under real pressure. We say this not to be alarmist, but because we think pretending otherwise doesn’t serve anyone well.

What we believe survives — and where we deliberately focus — is the strategic, architectural, and interpretive work that requires genuine human judgment and deep domain expertise. Knowing what to build and why. Designing the system underneath the communication. Connecting the data to the outcome in a way that makes business sense. That’s harder to replicate, and it’s where the durable value lives.

For now, the tools are genuinely useful and we’re using them. But we’re doing so with our eyes open.

Agentic AI: worth understanding before you need to act on it

If AI automation was last year’s big conversation, agentic AI is this year’s. Systems that don’t just respond to inputs but take autonomous action within defined parameters. The leading CRM platforms are already making this more accessible, with tools that let non-technical people configure these agents without writing code.

Most businesses aren’t ready to deploy these with customers just yet, and that’s probably fine. The more mature use cases are internal ones right now. The firms doing this thoughtfully are spending more time on governance and measurement than on capability, which feels like the right order of things.

CDPs and CRMs are finally working together properly

For years, businesses have been trying to connect customer data to marketing execution, and watching the gap between those two things create real, practical problems. Fragmented profiles. Outdated segments. Personalization that doesn’t feel personal at all.

The tools have genuinely caught up. Platforms like Salesforce Data Cloud and Adobe’s Real-Time CDP are doing what we always hoped they would: building a single, living customer view that marketing, sales and service can all actually use. The unified customer profile has been a goal for a long time. It’s finally becoming a practical reality for more businesses.

The agencies doing well here tend to be the ones comfortable with both the technical architecture and the strategic activation. It’s a rare combination, but a valuable one.

Revenue Operations: CRM finally grows up

CRM used to be a sales tool. Then a marketing tool. Now the more progressive businesses are using it as the connective tissue across the whole revenue engine, from first touch to long-term loyalty. Marketing, sales and customer success working from shared data and a shared view of the customer.

RevOps (Revenue Operations) is the framework making this real, and it’s changing the nature of how good agencies engage with clients. Rather than being briefed on a campaign, they’re being invited into conversations about the whole operating model. Lifecycle marketing sits right at the center of that, which is exciting for the work.

First-party data strategy has become a core service offering

The direction of travel away from third-party data is clear, and it’s prompting a more interesting question: why would a customer willingly share their information with you? That question requires a better customer experience as the answer, not just a smarter form or a cookie banner.

The shift toward first-party data — built through genuine value exchange, loyalty programs, preference centers and smarter data capture — is something the best agencies are now packaging as a distinct strategy. Whatever you call it, it’s really just about building relationships that customers want to be part of. Which has always been the point.

RCS: the channel most businesses haven’t caught up with yet

SMS has been quietly underestimated for a long time. RCS (Rich Communication Services) — this should probably include the messaging apps like Whatsapp and Telegram —  is its more capable, evolved sibling, and the numbers around open rates, engagement and cost efficiency are hard to ignore. The lifecycle marketers who are ahead of this are already pairing SMS and RCS for high-intent moments in the customer journey.

Most businesses aren’t there yet. Worth keeping an eye on.

A note on SaaS: the model is under more pressure than most people admit

This one doesn’t get discussed enough in our space. The traditional SaaS model — off-the-shelf software, per-seat pricing, built-in workflows — is facing a structural challenge that goes beyond feature competition.

As AI agents get more capable, they increasingly sit in front of these platforms, abstracting away the interface and the workflow that made SaaS valuable in the first place. And as custom application development becomes faster and cheaper — to the point where a well-specified build no longer requires a large engineering investment — the case for paying for standardized software at scale gets progressively weaker.

We’re not saying SaaS is dead. The major platforms still matter, and the integrations and data layers they provide remain genuinely useful. But the assumption that the current martech stack is permanent is worth questioning. The businesses building flexibility into their architecture now will be better placed when that shift accelerates.

What the most interesting agencies have in common

Looking across all of this, the firms doing genuinely good work tend to be the ones operating across three areas at once: the data architecture (getting the right systems properly connected), the lifecycle design (building communication logic that responds to real customer signals), and the measurement (connecting all of it to real business outcomes like retention, revenue and lifetime value).

It’s not easy to do all three well. But the businesses that find partners who can are generally the ones making the most meaningful progress with their customers.

If any of this resonates, or sparks a question or two, we’d genuinely love to chat about it.

[PS. This posts featured image is a small nod to this author’s new home — Charleston, South Carolina. Photo by Leo Heisenberg on Unsplash.]