Interview
Interview The Great SaaS Reset in the Age of AI
marketing 26 Mar 2026
You’ve overseen a growing portfolio of SaaS companies at saas.group. What is fundamentally changing in the SaaS landscape right now?
One of the biggest shifts is that we are seeing software move from being a system of record to becoming a system of action. Historically, most SaaS tools were there to organise information and support decision-making, the actual work still sat with the human. Today software is starting to take on more of that work directly. Instead of just surfacing insights or structuring workflows, AI-enabled products can increasingly execute tasks end-to-end. That’s a very different value proposition.
It changes everything from product design to pricing to market size. When software starts doing work instead of just supporting it, the ceiling for growth increases massively. We are no longer just competing for software budgets but also competing for labour budgets. That expands the total addressable market dramatically, but it also raises the bar for what “good software” looks like.
SaaS valuations have recently dropped significantly. Why do you think the market is struggling to price these businesses right now?
Investors are still applying old frameworks to a new reality. Classic SaaS multiples were based on predictable recurring revenue, high gross margins, and relatively stable cost structures. AI breaks some of those assumptions, especially around cost, where inference and compute introduce variability.
At the same time, AI-native companies may grow faster but have less defensibility early on. So you get this mismatch: traditional SaaS looks slower, AI looks riskier, and neither fits neatly into existing valuation models.
There’s a lot of talk about “the death of SaaS.” Do you buy into that narrative?
No, but I do think “lazy SaaS” is dead. If your product is just a thin layer of features, you should be worried. But if you’ve spent years building workflows, integrations, trust, compliance, and reliability, that remains incredibly valuable. In fact, I think we will see a wave of customers returning to mature, well-built products after experimenting with quick AI tools that don’t hold up in production environments.
The sector is certainly evolving and we shouldn’t expect SaaS companies of the future to look like the past. I believe that the companies that adapt effectively amidst the AI revolution will be bigger, more impactful, and more valuable than anything we’ve seen before. But the transition period will be messy, and not everyone will make it.
Can ‘legacy’ SaaS companies compete against AI-Native companies?
Mid-market SaaS companies are often dismissed as legacy tools, but I would argue that this is one of the biggest misconceptions in the market right now, and they are actually best positioned for AI transformation?
These SaaS companies already have three crucial things AI-native startups are still trying to build: customers, revenue, and domain expertise. AI transformation isn’t just about building new products, it’s about embedding intelligence into real workflows that already exist at scale. In many cases, these companies also sit on years of structured and unstructured customer data. That becomes a natural foundation for building AI features that are actually useful and differentiated.
So while startups get a lot of attention, the real AI transformation is often happening inside these “boring” SaaS businesses, because they have something to transform. These aren’t small feature updates. They’re strategic shifts in how the product delivers value, and we’re seeing this play out across multiple companies. Take AddSearch – they have pivoted from a traditional website search product to an AI-powered answer engine. Instead of just returning links, they now generate direct answers, which fundamentally changes the user experience and has driven meaningful growth. Keyword.com recognised early that search itself is changing. They launched a product to track brand visibility across AI platforms like ChatGPT, Perplexity, and Gemini. That repositioning opened up an entirely new growth vector.
With Prerender.io, the shift is even more structural. Originally, they helped JavaScript-heavy sites get indexed by traditional search engines. Now, as AI crawlers increasingly shape how content is discovered, their infrastructure is becoming critical for AI visibility as well. They’re evolving into a platform that ensures discoverability across both search engines and AI systems, while already operating at massive scale, serving billions of pages.
How are cost structures changing for SaaS businesses with AI?
This is one of the most underestimated shifts. Traditional SaaS had very predictable cost structures - mostly fixed costs, and high margins, but with the boom of AI, we are seeing variable costs tied to usage. That means margins can compress if founders are not careful, and also requires pricing to evolve, as you can’t charge a flat fee if your costs scale with usage.
Companies that figure out how to balance performance, cost, and pricing will have a big advantage.
At saas.group, you focus on acquiring SaaS companies with strong product-market fit. Has AI changed your acquisition strategy?
It has reinforced our core thesis more than anything. We look for businesses that have already done the hard work building robust products, strong customer relationships, and defensible positions. AI can amplify those strengths, but it can’t replace them, which is why we are looking to acquire a product that already has depth and then working with founders to layer AI on top to unlock new growth. AI native startups move faster and can rethink everything from first principles, but incumbents have distribution, data, and customer trust.
How should valuations evolve in this new environment?
For a long time, SaaS benefited from assumptions such as high multiples on ARR, heavy adjustments for stock-based compensation, and a willingness to prioritise growth over almost everything else.
That is now being replaced by a much more grounded framework. Investors are increasingly looking at real profitability, on a GAAP basis, and asking harder questions about cost structure, for example around sales and marketing efficiency, and now AI-related compute costs as well.
At the same time, AI is introducing new variables. Revenue may be less predictable if it’s usage-based, and margins can be more dynamic because of inference costs, meaning you can’t rely on simple rules of thumb anymore.
Valuation is becoming less about applying a multiple and more about understanding the underlying business, with a focus on true earnings quality, defensibility and efficiency.
What advice would you give founders building SaaS companies today?
This is a tougher environment for founders, but also a more exciting one. The bar is higher, and so is the upside. The old playbook of scaling sales and marketing spend ahead of revenue is under pressure. Capital is more expensive, and investors are looking closely at efficiency.
It’s no longer enough to show growth, you need to show that your business actually works as a business. That means real margins, disciplined spending, and a clear path to GAAP profitability.
At the same time, AI is raising expectations on the product side. So the challenge is doing both: building something meaningfully better while also running a tighter, more efficient company.
The founders who win will be the ones who combine product ambition with financial discipline.
How Copilot CoWork is Exposing Marketing Teams' Blind Spots
marketing 26 Mar 2026
1. Microsoft recently introduced Copilot CoWork within Microsoft 365. From your perspective, how does this development change the way marketing teams approach campaign planning and collaboration?
AI digital assistants are evolving beyond being individual productivity tools. As the name suggests, Microsoft’s new Copilot CoWork is a team-level tool that works across the shared 365 environment. By pulling context from emails, Teams chats, shared files, transcripts and meeting notes, the wider team can work from the same AI-generated insights at once.
Marketing teams can draw on data that’s been accumulated over many years, so (for instance) campaign planning can draw on insights that may have been missed or forgotten as the (human) team make-up has changed. You might argue this makes the AI a participant as much as a tool.
2. AI tools like Copilot CoWork can now assemble research and prepare materials directly inside documents and spreadsheets. How significant is this shift for day-to-day marketing operations?
Collaborative AI tools that work directly within documents offer the kind of automation that directly reduces manual effort across the marketing team. This is significant because it offers those much touted day-to-day efficiencies that will save users time and ultimately cut costs for the businesses that use them.
3. You mentioned that AI systems rely heavily on signals from emails, files, and internal data. What challenges might organizations face if their processes are undocumented or their systems are poorly integrated?
There’s an adage in data, which goes “something” like this - rubbish in, rubbish out. However, the irony is there’s likely lots of gold scattered across organizational data that the AI won’t be able to find if systems and/or internal processes are disconnected.
When AI tools don’t live up to expectations, it’s usually for this reason. AI doesn’t fix your foundations, it exposes them.
One of the benefits of a Microsoft 365 set-up is that data exists within a single ecosystem and CoWork can handle a degree of mess. But it can’t solve poor filing, so if teams aren’t, or haven’t previously, aligned on naming and storage conventions, there won’t be a single source of truth for the AI to work from.
While the best case scenario would be that everything’s kept in a single ecosystem, the reality is that’s not how tech stacks work for marketing teams: CRM, marketing automation, analytics, campaign data, DAM will all sit outside of Microsoft 365.
So, while CoWork will be able to offer a great picture of the planning, it won’t have the same visibility of what’s happening in the tools where that plan is executed, the performance is analysed, or of the customer data that’s captured.
The risk is that whatever CoWork presents back to you will look confident and polished regardless - so be very careful if your stack extends beyond 365.
4. Could the adoption of tools like Copilot CoWork reveal deeper operational issues within marketing teams? What kinds of gaps do you expect AI to expose?
The biggest challenge is CoWork’s inability to access anything outside the realms of the 365 ecosystem. That aside, the sorts of issues that come up time and again are inconsistent naming conventions across files and folders, poor version control protocols and outdated templates that haven't been retired.
But the other major challenges are uniquely human - knowledge trapped in individual inboxes rather than going into shared spaces, and approval chains that exist in people's heads - but nowhere in the system.
Most teams will be guilty of having unofficial, albeit well-established, workarounds that may have been in place for years. We might all ignore the file in the ‘finals’ folder on the shared drive because Sarah likes to keep track of the finals in her own folder… Good for Sarah, but how does CoWork know that?
5. Copilot is also incorporating capabilities from other AI systems such as Claude. How do you see this multi-AI ecosystem shaping the future of workplace productivity tools?
The honest answer is that nobody has yet figured out what a multi-AI ecosystem will look like. CoWork does now integrate other AI models but many teams are still using ChatGPT or Claude separately, plus their martech platforms will have their own AI agents built in. Making this complexity work seamlessly could take time.
In the meantime, teams need to be very clear on what tool should be used for particular scenarios: General-purpose AI is strong for broad research, analysis, and drafting, while platform-specific agents work better for workflow execution within the stack for things like automating approvals, generating asset variations, and optimising campaign targeting.
Conversely, the worst possible scenario is a free-for-all in which people just use whatever's in front of them and nobody tracks the overlap, the costs can quickly add up when multiple tools accumulate.
What we can safely predict is the AI market will consolidate as the big platforms keep absorbing more AI capabilities, and the general-purpose tools will become more integrated.
6. With more AI platforms being integrated into everyday tools, do you think organizations risk creating overlapping systems and losing visibility over their total AI investments?
As it stands, two-thirds of IT leaders have reported unexpected charges on consumption-based AI, but that’s not surprising. The overlap between general-purpose tools and platform-specific agents is growing, and it’s hard to keep on top of the total picture because the pricing models are so varied - credits, consumption, per-seat, bundled and so on.
Visibility is key because you can’t rationalize if you don't know what you've got. A practical starting point is to map every AI capability across your stack: what it does, which team uses it, what it costs or what pricing model it sits on, and what business outcome it supports. Next, look for the overlaps. You'll almost certainly find you're paying for similar capabilities in multiple places.
The harder question is whether to consolidate or keep both. The answer will depend on whether the platform-specific version does the job better than the general-purpose one. The extra cost can be justified when the specialist tool is genuinely better for that workflow.
7. Many organizations assume AI will automatically speed up workflows. Why might deeply embedded AI tools sometimes slow teams down instead of improving efficiency?
Introducing a tool like CoWork won’t necessarily slow things down, but it won’t automatically speed them up either. The issue lies in managing people’s expectations.
Within the context of a marketing team, slowdown doesn’t happen because of the CoWork, it’s more likely to come from the AI’s lack of visibility of anything outside of 365. Let’s say you’re working on a campaign review pack, CoWork can build you a polished looking document in a matter of minutes, but it won’t be accurate because much of the key data is held elsewhere. That means someone then has to retroactively fill in the gaps and review it for any errors or hallucinations.
The reality is it would have been quicker for the team to fill in the pack manually as they went along. Using AI when it can accurately access all the right information will speed up workflows, but otherwise it can’t work miracles.
8. What practical steps should marketing teams take to prepare their data, processes, and systems before introducing AI tools like Copilot CoWork?
CoWork lives inside of 365 so that’s the right place to start. And that typically means fixing bad habits - auditing shared file structure and naming conventions so the AI can find what it needs. Then document key workflows, even roughly, so CoWork has a process to follow rather than guessing. Finally, clean up data sources by retiring outdated templates and assets that could confuse the AI.
The next step is to agree on a policy for sign-offs, as a team agree which decisions need human sign-off and which can be AI-assisted. That might sound simple but, in my experience, it rarely exists in writing.
You then need to look at what happens at the boundaries of the 365 ecosystem, these handoffs are usually where the biggest process gaps lie. Documenting those, even at a basic level, will make a big difference to how useful the AI can be.
When it comes to evaluating success of the roll-out, it makes sense to prove value in a small area, learn from what the AI exposes, fix the foundations, then scale. Pick something contained, like campaign briefing or reporting prep, run CoWork against it, and see what it surfaces. That's how you build confidence and demonstrate ROI without trying to overhaul everything on day one.
Why B2B Growth Is Becoming System-Driven Rather Than Campaign-Driven
marketing 26 Mar 2026
Q1. Many B2B marketers still structure their strategy around campaigns and funnels. Why is that model starting to break down?
Picture a buying committee researching solutions anonymously across multiple sites. The traditional funnel assumed buyers moved through a predictable sequence, from awareness to consideration to decision, but that’s rarely how B2B purchasing works anymore. Buyers now move across multiple environments simultaneously, including research sites, partner ecosystems, content platforms, and peer networks. A single purchase journey often involves 6 to 10 stakeholders and unfolds over months, with dozens of touchpoints spanning channels and organizations.
Q2. You’ve said that B2B growth is becoming “system-driven rather than campaign-driven.” What does that actually mean in practice?
Campaigns still matter, but they are becoming outputs of a larger system rather than the primary unit of strategy. A system-driven approach means marketers build a connected operating layer that goes beyond traditional ABM platforms by incorporating predictive prioritization and real-time account-level momentum. Rather than simply targeting predefined account lists, the system continuously scores and re-ranks accounts based on evolving engagement, intent, and pipeline signals.
Instead of manually planning and launching individual campaigns, the system interprets sustained account-level signals, such as repeated research activity across stakeholders, content consumption patterns, and sales interactions, focusing on continuous engagement patterns over isolated intent spikes. In practice, this allows marketers to respond to real buyer behavior, including which accounts are gaining momentum, which stakeholders are actively researching, and where engagement is deepening, rather than relying on predefined campaign timelines.
Q3. Why is cross-journey measurement so difficult for B2B marketers today?
Most marketing technology was built to measure performance at the channel or campaign level, not at the account level across long, complex B2B buying cycles. That approach breaks down when multiple stakeholders engage across different environments over extended periods of time.
Marketers actually need the ability to connect signals across the entire journey, from early research to partner engagement to eventual conversion. Without that connective layer, teams end up with fragmented attribution models that miss how influence actually accumulates across accounts.
The challenge is not a lack of data. It is the lack of systems that can unify identity across accounts and embed that intelligence directly into activation and measurement, allowing marketers to interpret how engagement builds and converts across the full B2B ecosystem.
Q4. How does programmatic advertising fit into this shift toward connected growth systems?
Programmatic is evolving beyond a simple media buying channel.
At its core, programmatic is a decisioning infrastructure, but in a B2B context, that means continuously evaluating when and how to engage accounts across long, non-linear buying cycles. It enables marketers to time their engagement based on shifting account-level momentum, stakeholder activity, and signals of progression, rather than fixed campaign windows.
As marketing becomes more system-driven, programmatic increasingly acts as the operating layer that connects data, AI decisioning, and activation across channels. Unlike traditional ABM platforms, which often focus on account selection and segmentation, programmatic operates dynamically, activating and adjusting engagement in real time based on live signals and evolving account behavior.
Instead of simply executing media buys, it becomes part of the infrastructure that helps orchestrate the entire buyer journey.
Q5. Where does AI fit into these connected growth systems?
AI plays an important role in making these systems adaptive rather than static. Modern B2B buying environments generate enormous amounts of behavioral data, including intent signals, engagement patterns, partner interactions, and content consumption. AI helps interpret those signals at scale, distinguishing sustained account-level momentum across stakeholders from isolated engagement signals, and identifying when and where engagement should happen. The real opportunity is not simply automating tasks. It is enabling systems that continuously learn from buyer behavior and adjust engagement strategies in real time.
Q6. Many organizations are still organized around channel teams and campaign planning cycles. How does that structure need to evolve?
One of the biggest shifts will be organizational. When growth becomes system-driven, the focus moves away from managing individual channels toward managing the infrastructure that connects them. Marketing, sales, and partner teams increasingly rely on shared data and shared signals, reducing the risk of mistimed sales engagement in long B2B buying cycles where outreach can either accelerate or stall deal progression.
Success becomes less about optimizing a single campaign and more about coordinating how the entire go-to-market system responds to buyer activity.
Q7. What capabilities should marketers prioritize if they want to build these connected growth systems?
There are three foundational pieces.
First is unified data that brings together customer, intent, and engagement signals across the buying journey. Second is decisioning layers that can interpret those signals and determine where engagement should happen. Third is activation infrastructure that can execute across channels and partner ecosystems without requiring manual coordination for every campaign.
When those layers work together, marketers can move away from broad, one-size-fits-all lead generation strategies toward data-backed, tailored engagement. By leveraging analytics and insights to inform decisioning, teams can execute more precise, coordinated cross-channel strategies that reflect how B2B buyers actually behave.
Q8. Looking ahead to the next few years, how will B2B marketing look different as these systems mature?
The biggest shift will be that marketing becomes more continuous and less episodic.
Instead of launching campaigns in bursts, engagement will happen through persistent systems that respond to buyer signals across the entire ecosystem. AI will play a central role in this shift, powering predictive models that assess account progression, inform targeting, and dynamically allocate budget based on where momentum is building.
As these systems mature, marketing will also move away from lead generation as a primary KPI and toward pipeline progression and revenue impact. Success will be defined by how effectively teams can identify, prioritize, and accelerate high-value accounts through the buying journey.
The lines between media, data, and measurement will continue to blur. The organizations that succeed will be the ones that can respond in real time to complex, multi-stakeholder buying journeys, treating marketing not as a series of campaigns, but as a coordinated operating system for growth.
The Travel Loyalty Gap: How Programs Can Restore Relevance for Members
marketing 17 Mar 2026
1. For years, loyalty programs sold aspiration premium cabins, upgrades, exclusive experiences. When did that promise start to feel disconnected from what travelers actually value?
The disconnect emerged gradually, then accelerated. Years of devaluations, blackout dates and opaque redemption processes eroded member confidence long before travelers consciously noticed. The promise of a free flight began to ring hollow when points required kept climbing while availability windows kept shrinking. What crystallized the gap was a fundamental shift in how people travel. The aspirational model assumed infrequent, significant trips worth saving for. Modern travel looks nothing like that; it fragments across work trips, weekend getaways and spontaneous plans. According to our research, 85% of travelers rent a car at least once per year, with nearly half renting three or more times. When members realize their accumulated points cannot deliver value matching their actual travel frequency, the emotional contract breaks. The aspiration no longer feels attainable; it feels like a distant promise unlikely to be honored.
2. Many programs still emphasize points accumulation, even as redemptions become harder, more restricted or quietly devalued. Has the industry optimized too heavily for financial engineering at the expense of emotional loyalty?
The evidence suggests yes. Programs have become adept at issuing points, selling them to partners and managing liability on balance sheets. But financial optimization and member engagement are not the same objective; in many cases, they have become competing priorities. When redemption thresholds rise while earning rates stagnate, members notice. The commercial logic makes sense in isolation: unredeemed points reduce liability, devaluations protect margins. Yet collectively, these decisions signal that programs exist to extract value rather than deliver it. CarTrawler research reveals that 75% of loyalty program members would redeem points for car rental if available; a clear indication that demand for practical redemption exists but remains unmet. Emotional loyalty requires consistent reciprocity. Members who feel rules keep changing in the program's favor eventually disengage, not with complaints, but with silence and by directing their loyalty elsewhere.
3. We're seeing what some call a "redemption shift," where flexibility and immediate utility matter more than delayed, high-value rewards. What is driving this change in traveler psychology?
Several forces converge here. Consumer expectations have reset around instant gratification; people now expect value delivered immediately, not years, or even months, from now. Economic pressures have sharpened focus on practical savings over aspirational rewards. According to arrivia's 2024 Travel Loyalty Outlook report, 43% of consumers want discounts on everyday purchases from their loyalty programs. The member who redeems points for an everyday car rental before a weekend trip experiences value before the journey begins. CarTrawler data reinforces this shift: 68% of members say earning points on car rental bookings would make them more likely to book through their loyalty program. That immediate, practical utility reinforces program relevance in ways a distant points balance cannot. The psychology has shifted from "saving for something special" to "getting value now."
4. As travel becomes more fragmented spanning work trips, short breaks, local mobility and last-minute plans are loyalty programs failing because they are still designed around the "big trip" rather than the everyday journey?
Many are. Legacy program architecture reflects an era when travelers took fewer significant trips annually. That model assumed loyalty operated on a long cycle: earn over months, redeem occasionally. Ground transportation accompanies nearly every trip, yet most programs treat it as an afterthought. CarTrawler research found that 63% of travelers consider car rental an important part of their overall travel experience; yet this high-frequency touchpoint remains underleveraged. The member who earns points from a co-brand card but rarely flies sees their balance sit unused for years. Programs designed around the everyday journey, the car rental at the destination, the rideshare from the airport, create continuous engagement rather than asking members to wait for an annual redemption that may never come.
5. If the next decade of travel loyalty is less about points and more about practical value, what will distinguish the programs that rebuild trust from those that continue to lose relevance?
The programs that thrive will measure success differently. Rather than tracking points issued, they will focus on value redeemed. According to McKinsey, redeemer members spend 25% more than inactive members; evidence that facilitating redemption drives commercial outcomes. Technically, rebuilding trust requires modern infrastructure capable of real-time partner integration and flexible redemption. Strategically, it requires acknowledging that practical, everyday rewards create more touchpoints than aspirational redemptions that most members never reach. Everyday car rental represents exactly this opportunity: a high-frequency reward that accompanies nearly every trip and delivers immediate value. The programs that rebuild trust will demonstrate value, fitting naturally into members' real travel behaviors. Those optimizing for financial metrics while confidence erodes will manage databases of disengaged accounts rather than communities of loyal travelers.
The Physical Rebound: Why OOH is the ‘Secret Sauce’ to Fixing Your Broken Digital Strategy
marketing 17 Mar 2026
By Greg Wise, Co-Founder at OneScreen.ai
Let’s be honest: a majority of us, especially marketers, are currently trapped in a digital toxic relationship.
We go about our days staring at dashboards, stressing over pixels, and hoping that the latest algorithm update does not impact our reach. We’ve retreated so deeply into the “digital-only” bunker that we have lost sight of a simple truth: our customers are human beings. They live in physical spaces. And, despite what screentime often suggests, they really do look up from their phones.
That is why Out-of-Home (OOH) media, i.e., the “old school” world of billboards, bus wraps, and posters, has quietly become the best way to amplify a digital strategy. It’s a revolt against the digital grain; a “touching grass” moment in a world without the sun.
The Great De-Pixeling
For years, the “Marketer’s Gut” battled a losing fight against the “CFO’s Spreadsheet.”
Instinctively, you can feel that your brand is becoming soulless. You’re well-aware that people do not fall in love with a company after seeing some grainy banner ad on a weather app. You build a brand by occupying space in someone’s mind.
Here’s the problem, though: digital efficiency is hitting a wall. Ad costs are soaring as every brand competes to own the same three inches of glass inside a consumer’s pocket. We’ve mentally optimized our way into a corner, where ads are seen as annoyances to either swipe away or ignore.
OOH is the antidote. You can’t "AdBlock" a billboard on the highway. You can’t scroll past a wrapped bus while sitting at the station. OOH offers something that no digital ad can buy: Unmissable presence.
The Tech Behind the "Magic"
Something that we refer to as the “Guesswork Factor” has been a big reason as to why marketers have strayed away from the physical world. OOH has previously been seen as something to “spray-and-pray,” – you put up a sign and hope for the best.
But, it’s 2026, and technology has finally caught up with ambition. State-of-the-art adtech platforms now have real-world search capabilities. You don’t just “buy a billboard” anymore; we’re armed with with mobile movement data and proprietary intelligence to tell us precisely where a specific target demographic spends their time.
If you are targetting, let’s say, CFO’s of mid-market tech firms, don’t only search for “high traffic.” Focus on the exact transit lines, coffee shops, and office clusters where those particular people spend most of their time. Marketers can superimpose first-party data on physical maps to see where their customers live, work, and play. That level of “surgical precision” means that for every dollar spent in the real world, you’re backed by the same intel you would receive from a search campaign, minus the “rigged casino” of bidding wars.
Breaking the Phone Addiction
We talk a big game about “meeting the customer where they’re at,” but usually, what we’re talking about is “stalking them across the internet.” It’s not a journey, but rather a harassment campaign.
Real-world advertising breaks this cycle. If someone is waiting at a train station, they aren’t in “filter mode.” They aren’t purposely seeking to shut out the 5,000 digital messages they receive daily. By placing a brand in a physical space, you’re not just “reaching” someone; you’re proving that you are a tangible company.
OOH provides a brand with the street cred that digital ads have lost. It sends a message to the market: “We exist, we have substance, and we’re confident enough in our message that we will defend it publicly.”
The Contrarian Play: Touching Grass
As the majority of the marketing world obsesses over the latest AI tool to pump out thousands of blog posts that no one will ever read, the winning move is to go where there is no noise.
But the brands winning today are not those completely abandoning digital; they’re using the physical world to make their digital ads actually work. Think of OOH as the “hype man” for your online ads. When a consumer sees a billboard on their way to work, and then sees that ad on their phone later that evening, it’s not an intrusion; it’s a reminder.
This isn't nostalgia for the Mad Men era. It’s an epiphany that human attention is a limited resource. If you want a piece of it, you have to go where people are actually expending their energy, which is usually somewhere outside of a glowing rectangle.
Amplifying ROI (For Real This Time)
The most valuable part of this strategy is not the sign itself; it’s the amplification. But make no mistake: this is not OOH versus digital, “this or that.” Both tactics are vital to the modern marketing mix to come out victorious.
We now have the proprietary insight that allows us to close the gap from the street to the screen. When a data-backed OOH campaign is run, you can identify the “halo effect” in real-time. You’ll witness branded search volume increase in the exact zip codes where your ads are active. Your Facebook or LinkedIn CPA will plummet, because people will already know your name before they even lay eyes on your promoted post.
By eliminating the friction between “seeing an ad in the wild” and “clicking on an ad from your couch,” you’re not just spending money on brand – you’re turbo-charging the entire performance marketing engine. The top brands in the attention economy are not those with the biggest digital budgets; rather, they’re the ones who understand that even though the transaction takes place on a pixel, trust is build by what we do outside of our screens.
The Bottom Line: Spend the Brand Dollars
It’s time to stop apologizing for spending on brand.
If your entire strategy comes down to the number of "clicks," you don’t have a brand; you have a digital coupon book. Eventually, that well will dry up, because you haven't bothered to expose yourself to new people.
OOH is the most effective way to introduce yourself to your target audience. It forces you to be brief, witty, and compelling. It forces you to become a marketer again, instead of someone who spends their day tweaking settings in an ad manager.
So, here is my advice: Stop trying to find a sneakier way to track people around the internet. They hate it. Instead, let’s meet them outside. Give them something worth looking at and being intrigued by. Let them "touch grass," and while they are, make sure your brand is the most interesting thing they see.
"The secret sauce is not in the code. It’s on the street."
Chief Technology Officer Driving CX Assurance and AI Innovation
marketing 17 Mar 2026
From the survey results, the number one reason customers ghost brands is not being able to reach a human agent. Why do you think it still matters so much even as AI gets more advanced?
Customers want confidence that their problem will actually get solved. A human agent represents that safety net. The data shows that 71% of consumers still prefer to begin customer services with a live person, and the number one dealbreaker is being unable to reach one at all.
Even as agentic AI improves, customers know there are complex situations where nuance, judgement, or empathy matter. Billing deputes, travel disruptions, or anything tied to money or personal data often carries emotional weight. People want the option to escalate to someone who can take ownership of the issue.
What kind of hybrid models between AI and human service do you think work best, and why?
The most effective hybrid models treat AI as the first layer of assistance rather than the final authority. Agentic AI is excellent at handling high volume tasks like account lookups, status checks, and other structured requests. When automation handles them well, human agents gain more time to focus on complex cases that require judgement and empathy.
Where hybrid models fail is when the escalation paths are unclear. The survey shows that many customers want to escalate immediately or after a single failed bot interaction.
A strong hybrid model depends on CX assurance. Organizations must validate that the journey works from the customer’s perspective and that the handoff from AI to human support is smooth and reliable.
The survey suggests younger generations are more open to AI handling issues if it’s seamless. Do you see this shaping long-term AI strategy?
Yes, but mainly in how companies design their customer experiences. Younger customers tend to prioritize speed and convenience. More than half (56%) of Gen Z say they would choose an AI over a human interaction if it resolved their issue seamlessly.
However, generational expectations vary widely. Older customers often prefer speaking with a human, especially when the situation involves sensitive information or financial decisions.
The long term strategy should focus on flexibility. Brands should provide multiple ways to resolve an issue and allow customers to choose the path that works for them.
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Nearly half of consumers quit a brand after just a couple of bad experiences. How do you measure and improve AI’s effectiveness to avoid those moments?
Many organizations measure technical success rather than customer success. If the bot responds and the system records a completed interaction, the dashboard may show everything working correctly. Meanwhile the customer might have repeated themselves, been routed incorrectly, or received an answer that did not solve the problem.
Improving AI effectiveness requires measuring the full journey. Teams need visibility into whether the system understood the request, provided the correct information, and resolved the issue without unnecessary friction.
CX assurance plays a role here. Continuous validation across channels allows organizations to identify where journeys break and correct those issues before customers experience them.
There is a perception gap around AI capabilities versus reality, like consumers thinking humans resolve issues faster. How can tech teams help close that perception gap?
The perception gap comes from experience. Many customers have interacted with automation that failed to understand their question or trapped them in a loop. Those early experiences shape expectations long after the technology improves.
The only way to change perception is through reliability. When AI consistently resolves issues quickly and accurately, customers start to trust the channel. By continuously validating customer journeys, teams can detect and correct breakdowns before they damage customer confidence.
What’s your approach to building trust in AI-driven experiences rather than just rolling them out quickly?
Trust comes from discipline in how systems are deployed and monitored. Many organizations feel pressure to launch AI quickly. They introduce automation into customer journeys without fully validating how those systems behave under real conditions.
A better approach starts with governance and testing before launch, followed by continuous monitoring once the system is live. AI systems evolve as data changes and models learn, so reliability must be validated continuously.
CX assurance provides that layer of oversight. It helps organizations confirm that AI interactions remain accurate, compliant, and aligned with the intended customer experience.
A major takeaway is that poorly validated AI can harm reputation and loyalty. How do you ensure your AI systems are tested and governed before they go live?
Testing AI requires going beyond a limited set of scripted scenarios. Traditional quality assurance might validate a small number of expected interactions. Real customers behave very differently. They interrupt conversations, change topics, and ask questions in unexpected ways.
Effective CX assurance intentionally pushes AI systems through these use cases. Teams test unusual phrasing, multi-step conversations, and cross channel interactions to see how the system responds.
It’s important to identify weaknesses before customers encounter them. This approach reduces risk and ensures the experience behaves safely under real world conditions.
Looking ahead, what do you think needs to change in AI-powered CX to delight customers rather than frustrate them?
The next stage of AI powered CX will depend on reliability across the entire customer journey. Many organizations focus primarily on the intelligence of the agentic AI model. In practice, the most common failures occur in the surrounding workflow such as knowledge accuracy and/or escalation paths.
Customer journeys also move across multiple systems and channels. Each transition introduces potential friction if context is lost or the experience resets.
To deliver experiences that truly delight customers, companies need to design those journeys end to end and validate them continuously. CX assurance helps ensure that every step works as intended, even as systems evolve and customer behavior changes.
The Marketing Stack Just Got Simpler: Prompt-Driven Campaigns With getpixel.ai
marketing 17 Mar 2026
Marketing teams are being asked to drive pipeline with smaller budgets and leaner teams. Where does getpixel.ai.ai create the most immediate impact for operators who are stretched thin?
getpixel.ai delivers immediate impact by removing the manual, fragmented work that slows small marketing teams. Instead of spending hours building assets, coordinating channels, and tracking performance, marketers simply describe their product, audience, and campaign goals in natural language. getpixel.ai then generates brand-aligned creative and launches campaigns across LinkedIn, Google, Meta, Reddit, Bing, and X from a single interface. Lean teams can achieve enterprise-level output without adding headcount or hours.
Getpixel is for SMBs who look for a prompt ->campaign execution without much interference with AI - they are not marketing experts but rather business owners who want to run campaigns in minutes, not days, and pay a fraction of the cost.
A recent customer said this - 'I launched my campaign on Pixel and within 12 hrs already had 2 meetings booked.' David Vainer, Managing Partner & CEO, Alliance Risk
MetadataONE gives the power of LLM but within enterprise requirements, including security, compliance, budget control, brand guidelines, and other necessary requirements for b2b enterprises.
getpixel.ai.ai turns a single natural-language prompt into a fully live, multi-channel campaign. What’s happening behind the scenes to ensure that level of automation actually drives qualified pipeline and revenue not just traffic and vanity metrics?
getpixel.ai reads your website to understand your brand, tone, and positioning, then autonomously creates copy, visuals, and channel-native campaigns. AI/ML models continuously optimize in real time, shifting budgets toward combinations that drive qualified leads and revenue, not just clicks or impressions. It’s a full-funnel system where the input is intent and the output is measurable growth and brand presence.
For in-house performance marketers and demand gen leaders, what changes day-to-day when they adopt getpixel.ai? What parts of their workflow disappear and what becomes more strategic?
The repetitive, low-leverage work disappears: building assets manually, resizing visuals, duplicating campaigns, monitoring budgets, and stitching reports. What becomes strategic is guiding the system: defining the vibe, positioning, messaging, offers, and audience intent. Marketers spend their energy shaping strategy and analyzing results, while getpixel.ai handles production, execution, and continuous optimization.
As automation takes on more of campaign execution and optimization, how does getpixel.ai ensure marketers maintain strategic control, brand integrity, and clear visibility into performance across channels?
Control starts with the human prompt: marketers define goals, audience, and desired brand vibe in natural language, and getpixel.ai translates that into campaigns and creative. Every decision is visible in a unified interface, with performance tied to real outcomes. You can adjust the prompt anytime to shift strategy or tone. Automation accelerates execution and optimization, but humans remain the architects, ensuring brand, strategy, and creative vision are always intact.
Inside the AiVANTA–SlangIT Collaboration: Delivering Hyper-Personalized CX for Arabic-Speaking Audiences
marketing 16 Mar 2026
1. How will the combined solution transform customer engagement for businesses in Arabic-speaking markets?
The AiVANTA–SlangIT partnership creates an end-to-end engagement ecosystem that blends intelligent conversations with hyper-personalized communication — both tailored for Arabic-speaking audiences. SlangIT brings voice, chat, and IVR capabilities enriched with dialect-specific NLP, while AiVANTA layers personalized, localized video messaging triggered by user actions or behavior.
This combined platform transforms CX by allowing businesses to move from generic, one-size-fits-all interactions to contextual, emotionally resonant experiences — in the customer’s native dialect, at the right time, and through the right channel. Whether it's onboarding, upselling, or service resolution, enterprises can now automate the entire interaction flow — conversation to communication and back — in a way that feels natural, local, and human.
2. What does ‘hyper-personalized communication’ mean in the context of video messaging for multilingual and multicultural audiences?
In this context of video messaging, hyper-personalized communication means delivering video content that is not only tailored to an individual’s data such as their name, preferences, transaction history, or plan details, but also culturally and linguistically adapted to their specific context.
For multilingual and multicultural audiences, especially in regions like the Middle East, this goes beyond language translation. It includes:
● Dialect-specific narration (e.g., Emirati vs. Egyptian Arabic),
● Culturally relevant references in visuals and tone,
● And personalized content logic based on behavioral triggers or segment attributes.
The result is video messaging that resonates on a personal, emotional, and cultural level — making users feel seen, understood, and valued — while enabling businesses to scale this experience across millions of customers, channels, and journeys.
3. Can you elaborate on how your platform will integrate with Slangit’s Knowledge Base as a Service and conversational tools?
The AiVANTA platform integrates seamlessly with Slangit’s Knowledge Base as a Service (KBaaS) and conversational AI tools to create fluid, end-to-end engagement journeys. This means a customer’s interaction doesn't stop at a chatbot or a video — instead, both experiences talk to each other and adapt dynamically based on user actions.
Here are a few illustrative workflows:
1. Communication → Conversation
A user receives a personalized video from AiVANTA — say, a product recommendation or a service reminder. Embedded within the video is a CTA that opens a SlangIT-powered chat interface. The chatbot continues the conversation, answers queries in local dialect, and even guides the user to take action (like policy upgrades or offer redemption).
2. Conversation → Communication
A customer initiates a conversation on a website or WhatsApp using SlangIT’s assistant. Once they express interest in a product or service, AiVANTA triggers a follow-up personalized video — explaining the selected plan, summarizing their choices, or confirming next steps — all in the user’s preferred dialect and tone.
3. Multi-Stage Loop (Telecom or Retail Use Case)
Customer receives a loyalty offer video → engages with chatbot to understand terms or redeem → receives a confirmation video post-action. Each touchpoint is contextual, localized, and automated end-to-end.
This integration ensures that every engagement — whether inbound or outbound — is intelligent, personalized, and complete, turning static communication into a living, evolving customer experience loop.
4. What measurable outcomes (e.g. Reduced support load, engagement uplift) have you observed or anticipate from early pilots or deployments?
From early pilots and live deployments, we’re seeing strong signals that the AiVANTA–SlangIT integration drives tangible impact across multiple KPIs:
1. Reduction in Support Load
By combining AiVANTA’s proactive, video-based education with SlangIT’s real-time conversational interfaces, enterprises report:
● Up to 30–40% reduction in repetitive support queries, particularly in onboarding, policy explanation, and benefit clarifications.
● Lower call center volumes, as many user actions shift to self-service chat and voice flows.
2. Engagement & Conversion Uplift
● Video open and completion rates as high as 60–70%, especially when the content is delivered in the user’s native dialect.
● In telecom and BFSI pilots, personalized video + chatbot flows have shown 20–25% uplift in campaign response rates compared to static SMS or email.
● Cross-sell/upsell conversions improved where conversational follow-ups were offered post-video.
3. Operational Efficiency
● Automation of entire interaction workflows (e.g., onboarding → confirmation → support) reduces dependency on manual teams, improving scalability and consistency.
These metrics reinforce the core value proposition: smarter conversations + emotionally resonant communication = better CX, lower costs, and higher lifetime value.
5. What upcoming innovations or features are planned in the roadmap for your AI video personalization technology?
We’re actively investing in three key innovation areas to expand the value of our personalization engine and make customer engagement truly seamless:
1. Journey-Oriented Video Automation
We’re moving beyond one-off personalized videos toward automated, multi-touch video journeys. This includes smart orchestration where videos adapt based on customer actions — e.g., onboarding → reminder → upsell — all triggered through CRM or SlangIT conversational flows.
2. Plug-and-Play Integrations
We’re building out-of-the-box connectors for platforms like WhatsApp, Salesforce, and SlangIT’s KBaaS layer to allow videos to be auto-triggered and embedded across any customer touchpoint — web, app, chat, or email — without manual configuration.
3. New Language & Dialect Expansion
To serve diverse markets, we’re deepening our multilingual stack — including expansion into new Arabic dialects, Urdu, and Farsi. We’re also training AI avatars and voice models that mirror regional tones, emotional delivery styles, and cultural references.
Together, these innovations will allow brands to not only personalize what they say, but how, when, and where they say it — delivering truly end-to-end, emotionally intelligent engagement at scale.
6. Are there plans to replicate this co-development model for other regional markets or languages?
While we currently have no immediate plans to replicate this model, we absolutely see the value in strategic co-development with regional specialists — especially where language, dialect, or cultural nuance plays a pivotal role in customer engagement.
The partnership with SlangIT is a strong proof point: when deep local intelligence is combined with scalable AI infrastructure, the result is far more impactful than a generic solution.
We’ll continue to explore similar partnerships in other linguistically complex or under-served markets — where combining our personalization engine with local conversational AI or content intelligence can unlock meaningful, region-specific experiences.
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