Interview

Scaling Content Smarter: How Floyi Leverages Topical Maps and AI Briefs

marketing 31 Mar 2026

1. What challenges have you encountered in scaling content operations while maintaining alignment with brand voice, audience relevance, and search visibility?

Back in our agency days and while working on our own content sites, we ran into the same issues over and over. Strategy looked good on paper, but the content that went live often missed the mark. We struggled to align voice, audience intent, and SEO in a way that scaled.

That’s when we started building internal processes to manage everything - from persona clarity to keyword targeting to content structure. But the real shift happened when we introduced topical maps into the mix. Looking back, they should have been the first step. Topical maps now sit at the core of everything we do. They give us visibility, direction, and control before a single word gets written.

2. How do you currently leverage SERP analysis and competitor benchmarking to inform your digital content roadmap?

We rely on live SERPs and AIRS (AI ResultS) from platforms like ChatGPT, Google AI Overviews, and Perplexity. We analyze what’s ranking in the traditional search engine, what’s being cited in AI search engines, and how those overlap or diverge. That includes structure, tone, content type, and gaps in coverage. We look beyond who’s ranking and study how they’re ranking.

This dual-layer insight shapes every roadmap, brief, and internal link strategy we use. These workflows began as internal processes, and we’ve since built them into Floyi.

3. How important is reducing time-to-publish in your content strategy, and what tools or processes have you implemented to accelerate ideation and brief creation?

Reducing time-to-publish is a key priority for us. Especially when managing multiple campaigns or clients, the delays between research and execution used to kill momentum. So we started building tools to automate the bottlenecks. We pulled SERP data, analyzed competitors, integrated brand and persona inputs, and generated structured briefs.

That system cut our research time dramatically and eliminated back-and-forth cycles. We later realized other teams needed it too. That’s how Floyi was born. We turned the internal tools we built to speed up our own workflows into something others could use too.

4. What systems do you have in place to ensure that your content briefs are consistent, actionable, and aligned with both SEO goals and user intent?

Every brief we create includes specific data points: search intent, buyer stage, content type, point of view, tone, internal link prompts, and a list of keywords and entities. These are drawn from real-time SERPs and AIRS, so they’re grounded in both what search engines rank and what AI models surface.

What started as a set of Google Sheets, multiple browser tabs, and repeatable checklists is now a structured system we use every day. It keeps strategy and execution tightly aligned, whether we’re working on a single blog post or a large-scale content hub.

5. What mechanisms are in place to balance data-driven content creation with maintaining creative and editorial integrity?

We give writers structure, not constraints. The data guides what to say and who to say it to, but how it gets said is still up to the creative. Our briefs offer clarity on the audience, tone, key talking points, and gaps to address. But they leave space for originality and voice.

We built these processes to remove friction and second-guessing. The best writing still comes from people. Our system just makes it easier for them to hit the mark.

6. How do you see the role of automation evolving in content marketing, and what governance models are you considering to manage quality and accountability?

Automation is expanding, but we see it as a partner to the strategist and writer, not a replacement. We’ve already built governance into our workflow. Briefs are tied to real search queries and buyer personas. Content is mapped to search intent.

Automation handles the tedious parts, but human review ensures every piece meets our standards. That structure has helped us scale without losing control. As automation gets smarter, our quality controls get even sharper.

The Future of Influencer Marketing: David Abbey on AI-Driven Scale”

marketing 31 Mar 2026

1. How is your marketing team managing manual processes in terms of influencer relationships, and how are you addressing scalability challenges?

A lot of brands still rely on spreadsheets, manual outreach, and disconnected tools to manage influencer programs which makes it nearly impossible to scale efficiently. Once a brand grows from 10 to 50+ influencer partnerships, the wheels start to fall off. Teams get bogged down in manual follow-ups, managing approvals, handling gifting logistics, and compiling performance reports. It ends up consuming their entire bandwidth. That’s where automation changes everything. Influencer marketing platforms, like Endlss, that combine outreach, gifting, commission tracking, and communication in one place have become essential to keeping programs scalable. With the right tools, marketing teams can manage 3x the creator volume without needing to grow their headcount, freeing up time for the work that actually drives results.

2. How is your organization evolving its influencer marketing strategy to shift from brand awareness to measurable revenue generation?

Influencer marketing used to be all about reach and impressions, but the most forward-thinking brands today are treating it like a true performance channel. Instead of chasing vanity metrics, they’re focused on driving measurable, attributable growth. To meet that demand, more teams are adopting attribution tools that link creator content to conversions—whether through custom landing pages, affiliate links, or dynamic tracking infrastructure. On our end, we’ve built SmartLinks into the core workflow, so each creator’s impact is measured in real-time, and with partners of ours like Creator Commerce, together we provide co-branded shopping sites to elevate the consumer experience with a trusted shopping experience that increases conversions. Weekly performance reports make it easy to see which partnerships are generating returns and which need to be re-evaluated. That kind of visibility helps transform influencer marketing from a brand play into a predictable revenue stream.

3. How are you approaching influencer selection and outreach to ensure alignment with your brand values and audience segments at scale?

Alignment is everything in influencer marketing and not just in terms of values. The right creator should reflect the brand’s tone, speak to the right audience segment, and have a track record of driving action. Brands are getting more precise with how they vet creators, looking at engagement quality, audience breakdowns, content style, and past performance before making a move. With AI-powered messaging, every brand can personalize outreach in their own tone of voice—tailored to each creator’s audience, style, and past content.

But finding the right fit at scale is a different challenge. That’s where AI and smart filters are transforming outreach. With AI-powered messaging, every brand can personalize outreach in their own tone of voice—tailored to each creator’s audience, style, and past content. Combined with branded application forms and full creator analytics, brands are scaling high-quality outreach without losing that human touch. Inviting existing customers to apply is low hanging fruit when you want to scale effectively, and authentically—people who already know and love the brand often make the best partners.

4. What limitations have you encountered with traditional tracking methods (e.g., promo codes, UTM links), and how are you planning to evolve your attribution strategy?

Traditional tracking methods come with real friction. Promo codes can get leaked or shared in unintended ways, making attribution muddy. UTM links often break in-app or get stripped entirely, especially on mobile. This creates a gap between creator activity and the sales data that marketers rely on to optimize spend. To move past these limitations, we’re focusing on more robust attribution tools that work reliably across platforms and devices. SmartLinks, for example, generates unique tracking for each creator and integrates directly into conversion and payout workflows. Clean attribution is foundational to scaling today’s influencer programs responsibly. Whether it's to manage budgets or reward high performers, teams need to trust the data.

5. How are you evaluating new MarTech platforms to determine their potential impact on operational agility and cross-functional collaboration?

When evaluating MarTech tools today, agility is at the top of the list. Marketing teams need tools that are fast to implement, intuitive to use, and flexible enough to support cross-functional workflows. If a platform takes weeks to implement or requires engineering support to operate, it’s already a blocker. The best tools today integrate seamlessly with existing systems, whether that’s ecommerce platforms like Shopify, payment processors like Stripe, or internal communication tools. Endlss replaces four different tools in one, so brand, finance, and CX teams can all work from a single system. At the end of the day, the best platforms don’t just do more; they reduce friction across every team.

6. What competitive advantages do you see in adopting lean, AI-powered influencer marketing platforms compared to legacy tools with heavier infrastructure and higher costs?

Legacy influencer marketing platforms were often built with large enterprises in mind. They’re powerful, but also complex, expensive, and heavy to manage. For fast-moving teams, that’s become a real disadvantage—especially when speed and efficiency are critical. Lean, AI-powered platforms are flipping the script. By automating outreach, tracking, and gifting workflows, brands can move from idea to execution in 20 minutes, not weeks. And because these tools are often modular and self-serve, they’re far more cost-effective. What we’ve seen is most brands using Endlss are cutting their software spend by 50% or more while getting campaigns live that day, not weeks. That kind of agility has become a major competitive edge, especially for brands trying to maximize output with lean teams.

The 4 Voices Strategy: Luke Williams on Elevating Customer Experience”

marketing 31 Mar 2026

1. How does the ‘4 Voices’ strategy you created influence the way you approach enterprise CX and research programs?

The ‘4 Voices’ strategy Customer, Partner, Employee, and Market reflects my belief that there is no single pathway to truth in CX or research. Each voice offers a distinct perspective, and only by listening holistically can we uncover insights that are both grounding and surprising. This multi-perspective approach deepens our understanding, surfaces systemic patterns, and ensures that strategy and action are based on a more complete view of reality. In enterprise environments, this triangulation is essential it aligns teams, clarifies priorities, and converts fragmented feedback to focused, cross-functional execution.

2. How can organizations move beyond simply collecting feedback to activating it across business units?

Collecting high-quality feedback at scale is challenging but without connecting it to business decisions, it becomes a pleasant commodity rather than a catalyst for change. Moving beyond collection means designing feedback programs with business outcomes in mind. Every metric should have a clear owner and a defined action if performance declines. This creates accountability and ensures that signals resonate with both customer needs and operational priorities. Metrics must be meaningful, not abstract translating sentiment into tactical insight. Ultimately, activation happens when data is embedded in workflows, and teams see the clear link between feedback, action, and impact.

3. What methodologies do you recommend for aligning research insights with measurable business outcomes?

To align research insights with measurable outcomes, I advocate for a mixed-method approach grounded in business impact. But outcomes don’t exist in a vacuum customer sentiment, behavior, and intent are always relative: to the market, to competitors, and to past experiences. That’s why I recommend using relative metrics alongside standard KPI; they better reflect the customer’s context and decision-making lens. It’s equally important to model barriers to those outcomes understanding not just what customers want, but what’s preventing them from getting there. When research accounts for both drivers and friction, it becomes a far more powerful tool for driving focused, ROI-positive action.

4. What role does action-first thinking play in closing the gap between customer feedback and business performance?

Action-first thinking fundamentally reshapes how we approach feedback it shifts the mindset from passive analysis to proactive readiness. Instead of waiting to interpret what feedback might mean, we design systems with predefined responses so that signals trigger action, not debate. This posture assumes that teams are ready to respond, and the data simply tells them when. Often, we don’t need four-decimal precision to intervene; where there’s smoke, there’s usually fire. The goal is to empower teams to act autonomously and swiftly to investigate, triage, and improve without waiting for perfect clarity. This is what closes the gap between listening and performance.

5. In your view, what differentiates companies that sustain long-term CX excellence from those that fall behind?

Many companies aspire to be customer-centric but often settle for being merely customer-focused—responding to feedback without truly redefining their strategy around customer value. The key difference is that customer-centric organizations identify what truly creates value for their target personas and actively engineer strategies to deliver on those needs, even when it requires bold pivots. They don’t just improve the current experience—they reimagine it. These companies are also more discerning about whom they serve best and more deliberate in designing for those use cases. Crucially, they build innovation and adaptability into their core developing the muscle memory to evolve as customer expectations shift. The ones who master 10x innovation are often better at 10% improvements, too, sustaining CX excellence over the long term.

6. How will your capabilities evolve to meet emerging demands around real-time CX, personalization, and predictive analytics?

We’re actively investing in capabilities across real-time CX, personalization, and predictive analytics—but just as critically, we’re focusing on preparing customers to embed these capabilities into their everyday routines. Measurement has come a long way—today we can detect a bad experience in real time, even before the customer leaves the parking lot. But that speed is meaningless without companion systems that empower employees to respond with equal agility. Personalization, often misunderstood, isn’t about treating every customer as entirely unique; it’s about recognizing archetypes and delivering mass-personalization that aligns with those distinct cohorts. On the analytics front, we’re extending from predictive to prescriptive using models and knowledge bases to recommend the most probable high-impact actions. While humans will always make the final call, these tools de-risk decisions and help build the muscle for consistent, everyday experience-making at scale.

Get in touch with our MarTech Experts.

Why Creative Commerce Is Powering Marketing’s Unified Growth Engine

marketing 30 Mar 2026

1. Komerz has made two acquisitions in under 60 days with Pathformance in February and Glassbox in March. What's the strategic logic behind moving that fast?

Today’s industry is moving faster than the traditional agency model can adapt, and brands are telling us they can't wait another three years for holding companies to figure out integration. Our acquisition with Pathformance gave us the measurement backbone and the ability to connect advertising investment directly to sales outcomes at the transaction level. Glassbox gives us the upstream brand strategy and creative capability to feed that system with the right content and positioning from the start. Together, they complete a platform we've been deliberately architecting to round out the services we know brands are looking for, giving them the tools they need, all in one place.

2. You're using the term "creative commerce" to describe the category you’re building. For marketing professionals who haven't heard that term before, how would you define it in plain language?

Creative commerce is what happens when brand building and selling are no longer separate activities. For decades, agencies have built brands and retailers sold products and there was never an intersection between the two. Creative commerce means contextual content, data-led activation, and distribution functioning as one accountable growth engine. A brand doesn't just tell a story and hope it converts somewhere downstream. Creative is now built to perform and every distribution decision is informed by brand strategy which can be directly tied to measured outcomes at the transaction level.

3. The marketing industry has been debating brand building vs. performance for years. Is "creative commerce" essentially your answer to that debate, and why hasn't the industry solved it until now?

The industry has always revolved around agencies benefiting from campaigns and retailers benefiting from transactions. There hasn’t been a clear incentive to own the entire chain and connect the pieces together. Now, consumer brands are under immense pressure to demonstrate commercial impact, not just brand metrics. That shift has changed the dynamic and elevated creative commerce from a supporting function to the connective tissue that ties together media, messaging, customer experience, and conversion into a single, accountable system.

4. Pathformance brings measurement and analytics, and Glassbox brings brand strategy and creative. How do those two capabilities actually work together in practice for a client?

I like to think of it as closing the loop that most marketers never get to close. Traditionally, brand strategy informs creative, creative goes into market, and then you wait for quarterly brand tracking data to tell you whether it worked, by which point it's too late to do anything about it. With Pathformance's transaction-level measurement integrated into the platform, we can see in near real time how brand investment is influencing purchase behavior across digital, retail, and marketplace channels. That feeds back into Glassbox's brand strategy work, which refines the creative and messaging. It's a continuous loop rather than a linear campaign process and for brands operating across multiple markets and channels simultaneously, that's a significant operational advantage.

5. Your client roster includes some of the most sophisticated marketers in the world. What are they asking for that the traditional agency model isn't delivering?

We partner with companies that have spent decades building some of the world's most valuable and iconic brands, and they're under pressure to demonstrate that brand investment drives commercial outcomes rather than just brand awareness scores or share of voice. They are looking for a partner that can connect the brand equity work to the revenue line and it’s even better if each of the services they’re looking for can be found under one roof and one seamless team working on their account. In addition, these companies are also asking for speed. The current pace of digital commerce means the old model of annual brand planning cycles simply doesn't work anymore.

6. AI-driven distribution is a core part of the Komerz platform. Can you walk us through what that actually means?

At scale, the volume of decisions involved in distributing content and product across digital, retail, and marketplace channels is beyond what human teams can manually manage. Our AI infrastructure, and what we call the Commercial Growth Operating System, sits across a brand's existing tech stack and connects marketing, distribution, and analytics into a single data layer. That means AI-powered inventory planning, demand forecasting, multi-market operations, and last-mile delivery coordination are all running from the same system, informed by the same brand and performance data. We're not ripping and replacing what brands already have but are rather making those systems talk to each other intelligently for the first time. Plus, because we operate on a performance-aligned model where our incentives are tied to the brand's commercial outcomes rather than upfront fees, the AI is always optimizing for actual growth not just activity.

7. Glassbox was founded in Mumbai and has deep roots in the Indian market. With India's digital commerce market projected to hit $345 billion by 2030, how central is India to Komerz's growth strategy?

India is central to our growth strategy - not just as a market but as a model. The pace of digital commerce adoption in India, the sophistication of the consumer, and the complexity of operating across digital, retail, and traditional trade channels simultaneously makes it one of the most demanding environments for brand growth in the world. If you can build a system that works in India, it works everywhere. Geetanjali and Anil's experience building Glassbox in that environment brings something genuinely valuable to the platform: a ground-level understanding of how brand building and commerce actually intersect in one of the world's fastest-growing economies.

8. How do you build market awareness around this new category of creative commerce and what does winning look like for Komerz in three to five years?

We are building the category by demonstrating results that the existing categories can't explain. When a brand grows equity and revenue simultaneously, in the same system, measured at the transaction level, the outcome tells the story better than any positioning document can. In three to five years, winning looks like creative commerce being the default expectation for how sophisticated brands go to market globally, and Komerz being the company that defined what that looks like in practice. We're not trying to be the biggest agency network in the world. We're trying to make the agency network model irrelevant.

9. For a CMO reading this who is interested in shaking up their current agency relationship, what's one thing you'd want them to take away from what you’re building?

The fragmentation you're likely experiencing between brand and performance, between creative and commerce, between strategy and measurement, is a structural problem, not a talent problem. The model itself needs to change. What we're building is a single system where those things were never separated in the first place. Given our core capabilities and the ecosystem we have created, we can offer full funnel attribution at SKU level; something the world has never seen before. If that sounds like what you've been looking for, I’d love to talk.

The Great SaaS Reset in the Age of AI

marketing 27 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.

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