Two recent pieces perfectly capture this tension. Tom Fishburne’s Marketoonist cartoon post “Misalignment at Speed” nails the organisational reality, while Scott Brinker’s deep-dive “Build vs. Buy Is the Wrong Question for Martech AI” reframes the technology strategy debate. Together, they offer a timely wake-up call for marketing leaders.

The Marketoonist Warning: AI Speed + Silos = More Noise, Not More Results

Tom Fishburne recently shared a William Blair report describing enterprise AI adoption as “a mile wide and an inch deep.” Nearly every organisation is dabbling, but few have orchestrated it strategically.

He cites a 2025 MIT report showing that 95% of enterprise Generative AI pilots failed to deliver meaningful ROI or scale beyond experimentation. The culprit? Not the tools themselves, but strategic misalignment.

Fishburne quotes Hugh Derrick: when you layer “AI-driven speed” onto misaligned systems, you don’t magically become more effective — you simply get faster at being inconsistent. And in big, complex organisations where 30% of senior leaders already blame silos for productivity stagnation and two-thirds describe their companies as overly complex, that speed just creates more noise.

It’s the classic silo syndrome on steroids. Marketing runs its own AI experiments for content. Sales builds agents for lead qualification. Ops automates reporting. Everyone moves faster, but the left hand has no idea what the right hand is prompting. The result? Fragmented customer experiences, duplicated effort, and zero competitive edge.

Fishburne highlights a truism that AI has not changed: technology doesn’t fix broken structures — it amplifies them.

Scott Brinker’s Insight: Build vs. Buy Is No Longer the Right Question

At the same time, Scott Brinker (chiefmartec.com) dropped a must-read preview of the State of Martech 2026 based on a survey of 208 marketing and martech leaders across ~70 specific AI use cases.

His core argument? “Build vs. buy” is the wrong question. AI has collapsed the cost of custom development so dramatically that the old binary no longer applies. The real pattern emerging is hybrid by design.

Key takeaways from Brinker’s research:

• Multiple solutions in parallel are the norm. Teams use embedded AI in existing SaaS platforms (e.g., your MAP or CRM), adopt specialist AI-native tools for creation tasks (copy, visuals, competitive intel), and build custom agents for proprietary data and processes.
• The stack is stratifying, not consolidating. AI-native tools dominate “creation” layers (prompt-driven ideation). Incumbent platforms own orchestration and execution. Agentic CDP-style platforms are emerging as an alternative layer that reasons directly over your first-party data.
• B2B vs B2C behaviour differs. B2B teams go broad (adopting AI across more use cases because they’re often under-resourced). B2C teams go deep — buying off-the-shelf for 80% of needs but building custom solutions for the 20% that matters most to brand voice, guardrails, and differentiation.

Brinker’s conclusion is liberating: stop obsessing over one-size-fits-all vendor lock-in or heroic in-house builds. Focus on orchestrating a portfolio of capabilities that fits your specific context.

The Real Challenge: Silos Make the Hybrid Strategy Almost Impossible

Here’s where the two pieces collide — and why they matter so much for marketing teams right now.

Marketoonist Fishburne shows us that conventional siloed organisations amplify AI’s downsides. You can’t effectively “orchestrate a portfolio” when departments don’t talk, data is trapped, and strategy is misaligned at the top. Brinker shows us the opportunity: a flexible, layered martech stack that blends buy, build, and embed. But that opportunity only materialises if you first fix the organisational operating system.

In other words, AI capabilities don’t break silos — they make silo problems painfully visible (and expensive).

What Smart Marketing Leaders Should Do Differently

If you’re leading marketing or martech in 2026, here are the practical shifts I’m recommending to my clients:

1. Start with problems, people, and principles (not “AI-first”). Borrow from the Marketoonist’s referenced HBR thinking: be problem-centric, people-first, and principle-driven.
2. Build cross-functional AI governance early. Create a small tiger team (marketing, sales, ops, data) that owns the portfolio orchestration Brinker describes. Don’t let every department run their own experiments unchecked.
3. Adopt a stratified stack mindset. Buy infrastructure and orchestration layers you trust. Use AI-native tools for rapid creation. Build custom agents only where proprietary advantage or brand control demands it.
4. Measure alignment before you measure ROI. Track whether AI initiatives are reducing (or increasing) cross-team friction. If pilots are failing at 95%, the first fix isn’t better prompts — it’s better organisational alignment.
5. Reimagine teamwork, not just tasks. As Ethan Mollick’s P&G study (cited in related Marketoonist posts) shows, AI works best as a teammate. That requires new management structures, not just new tools.

The organisations that win won’t be the ones with the most AI tools. They’ll be the ones that use AI to finally break down the silos that have held them back for decades.

At Mackey Enterprises, we’re helping marketing teams do exactly that — turning AI from a source of noise into a genuine competitive advantage. If your organisation is feeling the pain of misalignment at speed, let’s talk.

What’s your biggest AI challenge right now — the technology, the silos, or the strategy? Drop a comment or reach out. I’d love to hear how you’re navigating the build-vs-buy (or rather, orchestrate-the-portfolio) reality in 2026.

Mitchell Mackey Marketing & Business Optimisation Consultant Hobart, Tasmania | mitchellmackey.com.au Driving B2B success in the AI era.

The content boom: budgets up, volume surging, AI leading the charge

Content production is accelerating at a pace that’s outstripping expectations, fueled by expanding channels and AI innovations:

• Rising budgets: Over 80% of organizations plan to increase content and creative budgets in the near future, up from 70% in 2024. Notably, 29% expect significant increases, a jump from 19% in 2023.
• Volume explosion: 82% of teams reported higher content output last year, with 27% seeing significant growth. AI is the primary driver, with 75% of professionals saying it has boosted volume—30% significantly.
• Evolving ROI metrics: Success is measured through a mix of engagement (40%), brand awareness (38%), conversions/lead gen (38%), and sales revenue (38%). ROI is improving, with 28% reporting significant gains (up from 19% last year).

Teams with mature workflows are reaping the rewards, being 3x more likely to see substantial ROI. This underscores the need for investments in AI, automation, and centralized systems to handle the surge without sacrificing quality.

Content operations: maturity matters, fragmentation hurts

Operational challenges persist, but top performers are addressing them head-on. Canto’s report highlights persistent pain points like keeping up with content demands (40%) and managing volume (36%), but also shows progress in tech adoption.

• Workflow maturity: Only 43% have advanced, standardized workflows, but these teams report 48% significant ROI increases compared to 0% for those with ad-hoc processes.
• Improvement tactics: 51% are using AI for creation, tagging, and organization; 41% are enhancing cross-team collaboration; and 36% are introducing automation to cut manual work.
• AI’s role in efficiency: Widespread AI users find maintaining brand consistency extremely easy (49% vs. 6-9% for non-users).

High-ROI teams prioritize automation (47%), templates (45%), and asset distribution (33%), proving that streamlining operations directly boosts returns.

Digital Asset Management: centralization is key to avoiding chaos

Fragmented asset storage, a persistent problem, remains a major drag, with most teams using multiple systems like cloud storage (62%) and DAM solutions (47%). The consequences are clear:

Only 35% are very confident employees use up-to-date brand assets, meaning most perceive that colleagues in sales, product management, customer service and the broader marketing team are using dated, often inaccurate materials which exposes their brands to numerous risks.

In contrast, DAM users report smoother operations, and those unifying assets with product content see 4x higher ROI gains.

For product-focused teams, accuracy is critical: 88% struggle with consistency across channels, but centralized approaches reduce duplication and improve access.

Tech stack optimization: closing gaps for better scale

Content teams lose time on manual tasks, with 22% prioritizing AI automation and 21% better search/retrieval. Core capabilities like analytics (43%) and standardized workflows (41%) are established in many organizations, but only 24% have fully optimized stacks.

Teams with strong ROI excel in AI tools (59%), analytics (50%), and automated tagging (42%). As stacks mature, expect more focus on integration to eliminate silos.

AI throughout the lifecycle: from analysis to personalization

AI is embedded across operations, with top uses in performance analysis (41%), SEO optimization (40%), and tagging (39%). High-ROI teams apply it more broadly, especially in personalization (41%) and discovery (43%).

Looking ahead, teams want AI for streamlining reviews (38%), smart search (36%), and reuse recommendations (35%). Adoption is at 77%, with 81% confident in AI’s accuracy for tagging—higher among heavy users.

What this means for your marketing strategy

The report paints a clear picture: success in 2026 hinges on operational maturity. Fragmented systems fuel burnout, waste and magnify risks, while AI-driven, centralized approaches unlock speed, consistency, and ROI. If your team is stuck in manual workflows or siloed assets, now’s the time to audit and optimize.

I help businesses in Tasmania and beyond implement these strategies— from AI integration to DAM setups. If you’re ready to scale your content operations, let’s talk. Download the full Canto report here to dive deeper. Let’s turn these insights into action for your business.

Key takeaways from the piece include:

• Democratization of Building: With AI making creation and automation “free” or low-cost, the real challenge shifts to distribution. As Bodnar puts it, “When building is free, distribution becomes everything — and that means more money, attention and energy will flow here than ever.” This signals a surge in investment toward marketing strategies that cut through noise and capture audience attention.
• Pivoting to Complexity: Initially focused on simplifying paid ads for smaller users, Bodnar’s team adapted by developing sophisticated AI workflows for large ad agencies managing hundreds of accounts. This highlights AI’s role in scaling operations but underscores the enduring need for human oversight—especially in high-stakes areas like budget allocation on platforms such as Google Ads or Meta, where errors can be costly.
• Human-AI Synergy: While AI empowers non-experts to handle end-to-end marketing workflows, Bodnar and commenters emphasize the risks. Non-specialists might miss nuances or errors in AI suggestions, reinforcing the value of expert-guided AI systems for trust and performance.

This evolution in martech points to a future where AI agents handle routine tasks, freeing marketers to focus on strategy, storytelling, taste, and building genuine consumer trust. However, it also warns of potential disruptions for traditional agencies and tools that don’t adapt quickly.

Bodnar’s experience is a timely reminder for marketing professionals: Embrace AI as an amplifier, not a replacement, and prioritize distribution in an era of abundant creation. For those in the sector, it’s worth checking out the full article on X for deeper insights into building resilient AI-driven strategies.

(Originally shared by @irabukht on X: https://x.com/irabukht/status/2025846968245948795)

SaaS spending hits highs amid AI volatility

One of the standout revelations is the continued growth in SaaS expenditures. Large organizations are now averaging $55.7 million in annual SaaS spend, an 8% increase year-over-year. This uptick isn’t just from adding more tools; it’s largely fueled by AI integrations and evolving pricing models. AI-native applications have seen a massive 108% surge in spend overall, skyrocketing to 393% for big enterprises. Meanwhile, the broader AI category has exploded with 181% growth in usage, making it the fastest-expanding segment in the dataset.

However, this growth comes with challenges. 78% of IT leaders reported unexpected charges from AI features or consumption-based pricing, while 61% had to cut projects due to these unplanned costs. These figures underscore the need for robust governance to manage volatility, as rising costs can erode budgets and weaken negotiation positions during renewals.

Stabilizing portfolios with a focus on AI embedding efforts.

Gone are the days of unchecked SaaS sprawl. Rather than expanding through new acquisitions, growth is happening within existing tools via AI embeddings, tier upgrades, and usage-based pricing. This shift signals a maturation in SaaS management, where “SaaS velocity”—the rapid entry and exit of applications—demands stricter intake processes and clear ownership to mitigate risks.

New tools, such as the Digital Sales Room (DSR) Trumpet, are being introduced, but at the expense of legacy tech. In 2025, organizations added 34% new tools to their tech stacks. Yet overall stack size grew only 11% — 305 total apps on average, up from 275.

AI’s transformative role in SaaS

AI isn’t just a buzzword; it’s fundamentally altering the SaaS ecosystem. According to the report, 64% of SaaS companies are embedding AI as a supporting feature, with 36% making it core to their offerings. An impressive 92% have either launched or plan to introduce AI capabilities. This proliferation is rewriting pricing structures, moving from traditional seat-based models to token, action, or usage-based charges that can introduce mid-contract surprises.

Looking ahead, the report predicts AI will become the most expensive “invisible worker,” automating tasks but creating unmanaged spend. Usage-based pricing will expose low-value AI implementations, accelerating consolidation, and a “value crisis” will emerge, where spend must be justified by measurable outcomes.

Security risks and the imperative for governance

With AI’s rapid integration comes heightened security and financial risks. Hidden AI features in familiar tools can amplify exposure before governance catches up. The report emphasizes that without mature practices—like full visibility, cross-functional alignment between IT asset management (ITAM) and FinOps, and disciplined renewals—organizations are vulnerable to greater volatility and lost control.

Benchmarks and actionable recommendations

Zylo’s benchmarks, grounded in hard data rather than surveys, offer a roadmap for success. Companies with strong SaaS management programs are better equipped to handle AI-driven changes, improving forecasting and renewal outcomes. Key recommendations include:

• Implementing programmatic management to adapt to dynamic pricing.
• Aligning ITAM and FinOps for better usage insights and cost predictions.
• Tying AI expenditures to business value during evaluations and renewals.
• Prioritizing intake discipline to manage the accelerating churn in SaaS portfolios.

As we head further into 2026, trends like AI volatility, faster application lifecycles, and higher-stakes renewals will make SaaS management a core competency across teams.

Optimizing Your SaaS Strategy

AI is clearly both an opportunity and a challenge in the SaaS world. For businesses looking to thrive, investing in governance and visibility isn’t optional—it’s essential. If you’re navigating these complexities in your marketing or operational strategies, let’s connect. .

The 2026 edition, marking its 10th anniversary, cuts through market noise to highlight converging innovation platforms that are accelerating step-function changes in productivity, capital allocation, and global growth. It’s not just a forecast; it’s a framework for understanding how these technologies are scaling from niche ventures to mainstream forces. As ARK puts it, “the future doesn’t arrive all at once. Those who recognize it early have the opportunity to Own What’s Next.”

Key Messages from Big Ideas 2026

Drawing from ARK’s research across public and private markets, the report explores 13 interconnected “Big Ideas” spanning AI, blockchain, space exploration, robotics, energy, and biology. At its core is “The Great Acceleration,” authored by the firm’s Chief Futurist Brett Winton, which positions AI as the central dynamo igniting five major innovation platforms: AI, Public Blockchains, Robotics, Energy Storage, and Multiomics. Here are the standout insights:

Technological convergence is driving exponential growth: Disruptive technologies are no longer siloed—they’re intermeshing at an unprecedented rate. The report notes that Convergence Network Strength, a metric tracking how these platforms catalyze each other, surged 35% in 2025. AI remains the key enabler, but robotics and energy storage are emerging as critical catalysts. For instance, reusable rockets could deploy AI chips in orbit to overcome earthly constraints on cloud computing, potentially boosting demand for space launches by 60x. Meanwhile, multiomics data shared via digital wallets could fuel neural networks to develop precision therapies for rare diseases. Multiomics isn’t just more data—it’s smarter, interconnected data that turns complex biology into actionable insights, much like how AI turns raw compute into intelligence.

• Unprecedented investment cycles ahead: The world is entering a massive technology investment wave, with capital expenditures as a percentage of GDP poised to rival historical booms like electrification or automobiles. ARK forecasts that innovations in AI data centers, robotaxis, and space-based compute could drive this surge, far exceeding consensus expectations. Historical parallels show how past tech shifts— from railroads to software—transformed economies, and today’s convergence could amplify that impact.
• Profound macroeconomic implications: Each platform has the potential to deliver structural boosts to global GDP. A compelling example: humanoid robots in the home. Currently, only about $2,600 of the average $68,000 in annual home upkeep contributes to GDP. A single household robot could add $62,000 per year by monetizing unpaid labor. If adopted in 80% of U.S. households over five years, this could accelerate GDP growth from 2-3% to 5-6% annually. Overall, ARK projects that capital investments in these platforms could add 1.9 percentage points to annualized real GDP growth this decade, with realized growth potentially exceeding forecasts by over 4 points as returns on invested capital soar.
• From experimentation to scale: The report delves into specific Big Ideas, such as AI Infrastructure (scaling neural networks), Bitcoin and Tokenized Assets (building a digital monetary ecosystem), Multiomics (advancing biology through data), Reusable Rockets (enabling space-based AI), and Autonomous Logistics (revolutionizing transportation). These aren’t isolated trends; their interdependence—e.g., stablecoins enabling AI agents to direct real-world resources—could redefine competitive advantages across industries.

ARK emphasizes that while these opportunities are immense, they come with uncertainties. Investors should approach with a cross-sector lens, understanding regulatory, political, and market risks. The report serves as an educational tool, not investment advice, reminding us that past performance doesn’t guarantee future results.

In a world where innovation is accelerating faster than ever, ARK’s Big Ideas 2026 offers a clear-eyed view of what’s on the horizon. For those in Tasmania and beyond looking to stay ahead, it’s essential reading. You can download the full report from ARK Invest’s website to dive deeper. What are your thoughts on how these trends might shape our economic future?

What is Martec’s Law?

At its core, Martec’s Law observes that technological progress follows an exponential curve—think Moore’s Law, where computing power doubles roughly every two years, or Metcalfe’s Law, which shows how network value grows exponentially with connections. Social media’s rapid evolution is another prime example: what started as simple platforms has exploded into complex ecosystems that reshape how we communicate and market.

In contrast, organizations evolve much more slowly, on a logarithmic scale. This isn’t just about size or bureaucracy; it’s rooted in human factors like established processes, cultural inertia, incentives, and the sheer effort required to shift behaviors. Larger teams amplify this resistance, making change feel like pushing a boulder uphill. Without strong leadership, stagnation becomes the norm.

Brinker calls this dynamic “the great management dilemma of the 21st century”.

Implications for Marketing and Beyond

For marketers, this law is especially relevant in the martech space, where tools and platforms multiply at a dizzying rate. It’s not just about adopting the latest AI-driven analytics or automation software—it’s about integrating them into your team’s workflow without causing disruption. The risk? Overwhelmed employees, half-baked implementations, and missed opportunities as competitors who bridge the gap pull ahead.

But it’s not all doom and gloom. Brinker emphasizes that even the best-led organizations can’t match technology’s pace outright. Instead, the key is strategic selection: consciously choosing which changes to embrace and which to ignore, aligning them with your broader business goals. As he draws from A.G. Lafley’s Playing to Win, strategy is about making tough choices—what’s “in” (the red area) and what’s “out” (the blue).

How to Bridge the Gap

To make Martec’s Law work for you, focus on absorption as much as adoption. This means:

• Strategic Prioritization: Evaluate tech changes against your organization’s mission. Not every shiny new tool deserves a spot in your stack.
• Cultural Integration: Go beyond installation—invest in coaching, training, and inspiring your team. Educate on why the change matters, nurture buy-in, and elevate those who champion it. A experienced consultant once recommended to myself that for every $1 committed to software licensing/subscribing, $3 should be allocated to training and change management.
• Proactive Leadership: Anticipate the gap and plan for it. In marketing, this could involve piloting martech tools in small teams before scaling, ensuring they enhance rather than hinder creativity.

I’ve seen businesses thrive by applying these principles. For instance, a mid-sized firm might skip the hype around every new AI platform and instead double down on data privacy tech that aligns with their customer trust strategy.

If you’re grappling with martech overload, Martec’s Law offers a roadmap. It’s a reminder that thoughtful adaptation beats frantic chasing. Check out Brinker’s original article for a deeper dive, and let’s discuss how this applies to your organization—drop a comment below or reach out via the contact form.

Originally inspired by Scott Brinker’s article on chiefmartec.com.

Leading marketing technology consultant Scott Brinker, of Martec, says we marketers are all facing variations of the following challenges:

Subscription Fatigue: Companies are overwhelmed by the sheer number of SaaS subscriptions. Managing dozens of tools—each with its own pricing tier—creates complexity and cost creep. In Martech, this is acute, as teams juggle platforms for email, social, SEO, and more.

Diminishing Returns: Many SaaS tools promise innovation but deliver incremental improvements. Marketers often find overlapping features across platforms, leading to redundancy and inefficiency.

Integration Challenges: The dream of seamless SaaS ecosystems often falls short. Disparate tools require complex integrations, leading to data silos and workflow bottlenecks.

Market Saturation: The martech landscape is overcrowded. New entrants struggle to differentiate, and consolidation (e.g., Zuora’s $1.7B acquisition by Silver Lake and GIC in 2024) signals a maturing market.

Economic Pressures: Complex economic macros and tighter budgets in 2025 are pushing companies to scrutinize SaaS spending. CFOs are questioning the ROI of tools that don’t deliver measurable impact.

The Rise of SaaS

SaaS emerged as a game-changer in the early 2000s, driven by pioneers like Salesforce. By delivering software over the internet, SaaS eliminated the need for costly on-premise installations, enabling businesses to scale quickly and pay only for what they used. In Martech, this fueled an explosion of tools, covering everything from CRM to analytics and automation. The low barrier to entry empowered startups and enterprises alike, democratizing access to cutting-edge technology.

The appeal was clear:

• Cost Efficiency: Subscription models spread costs over time.
• Scalability: Tools could grow with a company’s needs.
• Accessibility: Cloud-based platforms allowed global, real-time collaboration.

For marketers, SaaS meant agility. Teams could deploy tools like HubSpot or Marketo to orchestrate campaigns, analyze data, and personalize customer experiences without heavy IT involvement.

The Fall of SaaS?

However the SaaS trajectory is shifting. Brinker argues that we’re entering a “post-SaaS” era, where the model evolves or gives way to new paradigms. Key trends include:

• Consolidation and Platforms: Mega-platforms like Salesforce and Adobe are absorbing smaller players, creating all-in-one suites. This reduces the need for multiple subscriptions but risks vendor lock-in.
• AI-Driven Automation: AI is reshaping martech, with tools embedding generative capabilities to automate content creation, analytics, and personalization. This could reduce reliance on specialized SaaS tools.
• Open-Source and Hybrid Models: Businesses are exploring open-source alternatives or hybrid solutions (e.g., self-hosted SaaS) to cut costs and gain control.
• Usage-Based Pricing: Some SaaS providers are shifting to pay-per-use models, aligning costs with actual value delivered.

What This Means for Marketers

For Australian businesses, particularly those leveraging martech, the evolving SaaS landscape demands a strategic rethink:

• Audit Your Stack: Review your SaaS subscriptions to eliminate redundancy. Tools like our consulting services can help optimize your martech stack.
• Prioritize Integration: Invest in platforms that integrate seamlessly or leverage APIs to connect your ecosystem.
• Embrace AI: Adopt AI-powered tools to streamline workflows and reduce dependency on multiple SaaS products.
• Negotiate Contracts: With market consolidation, negotiate better terms with SaaS providers to maximize value.

Looking Ahead

SaaS transformed martech, but its challenges—cost, complexity, and saturation—are undeniable. As Brinker notes, the future lies in smarter, more integrated solutions that prioritize outcomes over subscriptions. At MitchellMackey.com.au, we’re committed to helping Australian businesses navigate this shift, ensuring your martech investments deliver real results.

This post is adapted from Scott Brinker’s insights in the State of Martech 2025 report, co-authored with Frans Riemersma. For the full report and more Martech insights, visit chiefmartec.com or download the report at content.martechday.com/state-of-martech-2025.pdf.

Brinker’s key theme is that marketing has become a technology-powered discipline, and therefore marketing organizations must infuse technical capabilities into their DNA. I agree.

Unfortunately, despite commitments to artificial intelligence (AI) and automation to upgrade customer service capabilities, a CCW (Customer Contact Week) study has confirmed what we all sense to be true, reporting a 55% reporting a decline in quality over the past year. The issue isn’t AI itself but how companies use it – prioritizing efficiency over empathy.

The Problem: Misaligned AI Strategies

Businesses too often deploy AI to cut costs, forcing customers into rigid, impersonal journeys. Common pain points include:

• Basic automation failing complex queries.
• Channel-switching (e.g., WhatsApp to phone).
• Lack of personalization, requiring customers to repeat themselves. A persistent source of friction.
• High expectations set by CX leaders like Amazon, unmet by others.

The Solution: AI + Human Empathy

AI excels in transactional tasks like password resets but falls short in emotionally charged moments like service outages. For now, the immediate future lies in AI complementing human agents, not replacing them. Companies must:

• Use AI for high-volume, low-emotion tasks.
• Reserve human interaction for complex, empathetic needs.
• Train AI with quality data tailored to customer use cases.

Looking Ahead to 2026

To win at CX, businesses must prioritize customer-first strategies. This means building or partnering for AI models that ensure compliance, accuracy, and scalability..

The future of CX is about blending AI’s efficiency with human empathy to create genuine connections that keep customers coming back.

Scott and his Martech Tribe colleague Frans Riemersma now have created a dedicated platform to assist marketers filter and navigate through their subcategories and options.

They have also produced a comprehensive assessment of AI’s impact on the Martech universe. Despite (and maybe also because of) its 111-page length, Martech for 2025 is well worth reviewing.

As marketers navigate this dynamic era, the report highlights five key segments driving growth and innovation, alongside practical strategies for leveraging AI effectively.

AI-Powered Martech Growth

The surge in Martech tools, which have jumped from 11,038 in 2023 to 15,385 in 2025, shows no sign of slowing down. The growth is largely fueled by AI-powered solutions. These are categorized into five segments:

1. Indie Tools: Specialized, AI-native startups focusing on niche marketing tasks like content creation and sales automation.
2. Incumbent Platforms: Established players (e.g., Adobe, Salesforce) embedding AI to enhance existing products.
3. Challenger Platforms: Emerging platforms leveraging AI to disrupt traditional categories, such as digital experience platforms (DXPs), formerly CMS tools or website Content Management Systems.
4. Instant Software: Custom, AI-generated software created on-the-fly to address specific business needs. Salesforce, recognising an emerging commercial threat, is proactively warning against this approach, labelling it DIY AI software.
5. Service-as-a-Software: AI-driven solutions that perform tasks autonomously, like customer service or campaign orchestration, often with outcome-based pricing.

Foundations for an AI Strategy

The report reinforces the essential reality that a robust AI strategy hinges on a solid data foundation. Scott and Fran emphasize the importance of a universal data layer. 71% of those who responded to a survey for the report claimed to be using cloud data warehouses (e.g., Snowflake, Google BigQuery) to integrate data across their stacks. Additionally, a universal content layer is emerging to manage personalized content at scale, blending customer data with brand identity for hyper-personalized experiences. API composability is also critical, enabling AI agents to execute actions via large action models (LAMs), which complement large language models (LLMs).

This data challenge is the biggest AI-adoption hurdle for traditional legacy businesses, who, with few exceptions, are yet to genuinely master their myriad data management issues.

Generative AI Use Cases

Generative AI adoption is widespread, with content ideation (69%) and content copy production (62%) leading as the most popular use cases given their obvious low-hanging fruit status. Other notable applications include transcription, summarization, and competitive research. While content and data-related tasks dominate, adoption in advertising and social media is lower, partly due to outsourcing or reliance on platforms like Google’s Performance Max. The report notes that 60% of marketers use both embedded AI in existing tools and new AI-native tools, with frequent use in content and analytics tasks.

Governance and Practical Insights

Only 52% of organizations have a generative AI policy, impacting adoption rates. Those with policies are more likely to cite cost as a reason for discontinuing AI tools, indicating greater maturity in evaluating ROI. The report also highlights the need for governance to address data privacy, security, and compliance concerns.

Key Takeaways

• Experiment Strategically: Start small with AI to test high-impact use cases without overhauling existing systems.
• Prioritize Data: A strong data strategy is essential for unlocking AI’s potential. There is little merit in feeding sophisticated AI engines with poor-quality data fuel.
• Embrace Composability: Build flexible, API-driven stacks to integrate AI seamlessly.
• Focus on Outcomes: Shift from flashy demos to measurable ROI, leveraging AI for efficiency and personalization.

As AI continues to evolve, marketers must balance innovation with governance to harness its transformative power. For a deeper dive, explore the full “Martech for 2025” report.

For marketers, Agentforce offers significant capability enhancements beyond what can be achieved with separate Large Language Models such as ChatGTP and Grok.

Even if you don’t run Salesforce, all marketers should be understanding and embracing autonomous AI agents, digital workers, to streamline processes, enhance personalization, and drive efficiency.

AI agents, from Salesforce and other Martech companies, are now delivering real business benefits to marketers.

Campaign automation and optimization: Agentforce automates the full campaign lifecycle, from generating briefs and audience segments to creating content and end-to-end customer journeys. The promised payoff is faster campaign execution and higher ROI.

Genuine (finally) personalized engagement: by integrating with the Salesforce Customer 360 and Data Cloud products (for those who have their data act somewhat together, if not altogether), Agentforce agents access real-time data to deliver focused communications across email, SMS, web, and mobile channels. If executed effectively, this moves marketers closer to our long-sought after goal of genuinely personalised content that boosts engagement, conversion rates and loyalty.

24/7 customer interaction: Agentforce enables two-way, conversational marketing by embedding agents in channels like email, SMS, and web, transforming “do-not-reply” messages into dynamic interactions. Again, if the execution is superior, these agents operate around the clock, enhancing satisfaction and reducing churn.

Data insights: leveraging the Salesforce tools Data Cloud and Retrieval Augmented Generation (RAG), Agentforce agents pull insights from structured and unstructured data (e.g., PDFs, videos, CRM data) to inform marketing strategies. They analyze historical and real-time data to identify trends, predict customer behavior, and suggest actionable recommendations, such as targeting at-risk customers to increase loyalty. These capabilities have long been promised by the industry’s Martech vendors and now we are closer than ever before to realising them.

Cross functional collaboration: Agentforce integrates with Salesforce’s Sales Cloud, Service Cloud, and Slack, aligning marketing with sales and service teams. For instance, marketing agents can share campaign insights with sales teams to support upselling or cross-selling, ensuring a cohesive customer journey and maximizing revenue opportunities.

Time and cost savings: by automating repetitive tasks like content creation, segmentation, and performance monitoring, Agentforce frees marketers to focus on strategic initiatives. Salesforce claims Agentforce’s efficiency reduces operational costs while scaling marketing efforts.

Scalability and customization: Salesforce says its low-code Agent Builder allows marketers to customize prebuilt agents or create new ones tailored to specific goals, industries, or use cases using tools like Flows and Prompt Builder. This flexibility ensures Agentforce scales with business needs, from small teams to large companies

Enhanced security and compliance: Salesforce’s Einstein Trust Layer ensures data privacy with features like zero data retention, toxicity detection, and audit trails, allowing marketers to deploy AI confidently while adhering to security and regulatory policies.

Improved content performance: Agentforce agents can A/B test content to recommend the most effective versions and optimize marketing materials for better search rankings and engagement.

Event and appointment management: Agents automate event logistics, such as sending details, booking sessions, and conducting surveys, or schedule appointments based on customer history, streamlining personalized marketing efforts and improving customer experiences.

Real-world impact: Salesforce cites reference customers who assert that Agentforce is reducing customer inquiry response times by nearly 50% during peak seasons, demonstrating the ability to handle high-volume marketing tasks efficiently.

In summary, Agentforce has the potential to transform marketing by automating workflows, personalizing customer interactions, and providing actionable insights, all while ensuring scalability, security, and alignment with our broader business goals.

The challenge, as always with sophisticated, potentially complex marketing tech, is execution and for that we need not only smart AI but also smart, tech-literate marketers.