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 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.

Key Highlights from Forrester’s 2026 Predictions

Forrester outlines five pivotal trends that will shape B2B operations this year, all centered on “The Race to Trust and Value.” Here’s a what they mean for marketing, sales, and product teams:

• Governed AI for Smarter Outcomes: While ungoverned generative AI could lead to significant costs, proactive governance opens doors to efficiency. With 75% of sales reps already using AI tools, empowering your team with AI literacy can prevent pitfalls and value through better decision-making.
• Human Expertise as a Competitive Edge: Buyers are craving deeper validation beyond AI—30% viewed genAI as meaningful in 2025, but human interactions with product experts top the list for trust. This shift highlights the power of upskilling your teams to deliver personalized insights, fostering stronger buyer relationships from early stages to post-sale success.
• Influencer Relations as Growth Levers: A huge 75% of B2B enterprises plan to boost budgets here. As buying networks expand, influencers like analysts and experts become key to building trust. Integrating them into your go-to-market (GTM) strategy—via social amplification and endorsements—can elevate proof-of-success over brand reputation, driving measurable impact.
• AI Agents Revolutionizing Payments: Expect AI agents in one-third of B2B payments, streamlining complex workflows like invoicing and trade credit. Vendors like Basware and Coupa are leading the charge, promising higher automation and efficiency—perfect for Australian firms dealing with diverse supply chains.
• Agent-Led Negotiations for Dynamic Deals: 20% of sellers will engage AI buyer agents in quotes, turning negotiations into data-driven, compliant opportunities. This evolution from static pricing to agile interfaces means suppliers with strong AI partners can protect margins while adapting to buyer demands.

These predictions underscore a market where clarity, accountability, and human-AI synergy win big. In volatile times, they’re your roadmap to turning rapid change into sustainable advantage.

Activation guidance

Ready to put Forrester’s insights into action? Here’s practical advice for B2B leaders in Tasmania and beyond:

1. Build AI Governance from the Ground Up: Democratize AI education—train teams to spot bad outputs and integrate governance into commercial apps. Start small: Audit your current tools and invest in “AI intelligence quotient” workshops to boost confidence and reduce risks.
2. Elevate Human Touchpoints: Prioritize product experts and customer success teams early in the buyer journey. Upskill them to handle complex queries, ensuring your offerings meet real needs. This not only validates AI insights but builds lasting trust.
3. Strategize Influencer Engagement: Shift from tactical PR to integrated GTM. Expand your signals to include influencer activity—track commentary and endorsements. Align budgets with performance metrics to see ROI in buyer discovery and conversions.
4. Embrace AI Agents in Workflows: For payments and negotiations, partner with agentic AI vendors. Assess your invoicing and procurement processes for automation potential, starting with pilot programs to scale efficiencies without disrupting operations.
5. Prepare for Buyer Evolution: Equip sales teams with AI agents for counteroffers, focusing on margin protection and compliance. Collaborate with in-house engineers or trusted partners to stay agile in a market where 61% of buyers use genAI for purchasing.

By prioritizing trust and value—as Forrester advises—you’ll position your business for growth in a buyer-empowered world.

For more on how digital marketing can amplify these strategies, from AI-optimized content to influencer campaigns, let’s chat. Connect via mitchellmackey.com.au/contact. Here’s to a prosperous, high-impact 2026!

Mitchell Mackey | Digital Marketing Strategist | Hobart, Tasmania Driving B2B Success in the AI Era

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.

The product manager—a bright, young, and refreshingly candid voice in the martech world—shared an observation, gained from many formal and informal discussions with senior executives across the world, that was not surprising and yet still shocking. Despite decades of so-called “lessons learned,” and the explosion of sophisticated sales and marketing technologies, the fundamental rift between sales and marketing teams remains stubbornly in place at all too many big and small companies.

The arguments are eerily familiar to anyone who’s been in B2B long enough:

• Marketing complains that sales doesn’t convert the leads they’ve worked so hard to generate.
• Sales counters that marketing doesn’t deliver enough high-quality leads worth pursuing.

These debates were raging decades ago. They’re still raging today.

The truth—then and now—is that neither side is entirely right or wrong. The real issue is that too many organisations treat sales and marketing as separate, even competing, functions rather than two halves of the same revenue engine.

As my new friend in martech product management highlighted, no ABM platform, no AI-powered targeting tool, no shiny piece of martech will magically bridge this divide. Technology can enable better alignment, but it can’t create it.

What’s required is genuine, consistent collaboration—not just between department heads, but all the way down to the front lines. That means:

• Talking regularly—not just at quarterly reviews, but in weekly syncs and shared planning sessions. Most do this, but few do it well.
• Agreeing on common goals—clear revenue targets, shared definitions of a qualified lead, and mutual accountability for results. Common goals are, of course, common sense, but this remains a scarce commodity.
• Embracing one unified scoreboard—so that both sales and marketing are working to the same numbers, in real time, without the selective reporting that fuels mistrust. Separate reporting and tracking processes and systems are almost guaranteed to generate friction.

Genuine collaboration is simple in concept, hard in practice, and absolutely essential. The most advanced ABM platform in the world—contact-level or otherwise—will fail in an environment where sales and marketing remain siloed. But when the two teams align on purpose, process, and performance measurement, technology becomes a force multiplier rather than a false promise.

If you’re investing in ABM, don’t just evaluate the tool. Evaluate your organisation’s willingness to collaborate—because without that fundamental commitment, you’re just buying another piece of tech for the shelf.

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.

Announced with characteristic bravado and ambition at the company’s Dreamforce jamboree in San Francisco in September, Genie generalises its CDP capabilities across all its Sales, Service, Marketing and Commerce clouds, as well as its Platform, Tableau, Slack and Mulesoft tools, to enable customer experience orchestration in real time.

Salesforce, which has overtaken SAP to seize enterprise software application leadership by revenue, says Genie connects with the CDP engine to establish a single customer or group of customers with AI and machine learning and connects that data to Salesforce’s Flow automation.

Examples include serving ads, content or offers; making sales recommendations based on browser behaviour, pulling up historical sales and service data or reacting to real-time maintenance alerts to quickly solve customer problems or deleting or restricting customer data when they opt out of marketing.

Users can see exactly how the entire end-to-end process is flowing or stalling, from marketing across to sales, service, compliance, and system-of-record transaction management.

The rebranding of the CDP to Genie was to “make it fun” and, critically, to help customers and stakeholders embrace and understand the value proposition.

In summary, Salesforce asserts that data orchestration blended with engagement capabilities and genuine AI, results in a so-called “real-time” CRM. Next level stuff.

Constellation Research founder Ray Wang was quoted as stating, “to take the CDP to the next level, you really have to do it in a very different way. You have to not just think about the data that’s there, you have to think about how everything’s connected”.

Salesforce claims large companies run an average of 976 separate applications in their businesses, resulting in data fragmentation and an inconsistent environments where value can not flow smoothly.

Salesforce’s CDP leader Rahul Auradkar was quoted as saying that the company’s own customers had said that their own downstream customers were expecting far more personalized experiences now and in the future.

“The power of real time is doing the right thing, at the right time,” said Auradkar.

Let’s see how that promise, oft touted and promised in the CRM world over the years, is delivered.

Nothing wrong with the ambition. Everyone agrees we need to create a hyper-personalised relationship with our customers, pooling and harmonizing data into what Forbes calls a “a reconciled, comprehensive customer portrait”.