Interview
The Revenue Visibility Gap: What One Engineering Firm Reveals About Martech Maturity
marketing 27 Feb 2026
By Marki Landerud, Vice President of Marketing at Marketri
Revenue visibility challenges are often treated as industry-specific.
In reality, they are martech maturity problems hiding inside operational silos.
One national engineering firm recently uncovered a significant blind spot inside its growth engine. Operationally, the organization was disciplined. Systems were stress tested. Assumptions were validated. Performance was instrumented and monitored over time.
Revenue generation was not.
The issue was not weak expertise or lack of effort. It was the absence of instrumentation across the CRM and revenue operations infrastructure.
For marketing and RevOps leaders, the lesson is clear: without structured lifecycle governance and clean data architecture, even sophisticated organizations operate with incomplete visibility.
The Illusion of Revenue Stability
Many services-based organizations, including engineering firms, have grown on the strength of reputation and relationships. Long-standing clients provide repeat work. Project managers maintain trusted networks. Conferences are attended. Proposals are written.
From the outside, activity looks healthy.
But when leadership asks fundamental revenue questions, the answers are often unclear:
- Which channels are producing high-value opportunities?
- How long does an opportunity remain in each lifecycle stage?
- Where are deals stalling?
- Which disciplines are driving new demand?
- How exposed are we to client concentration risk?
These are not just business development questions. They are martech maturity questions.
Without standardized CRM infrastructure, defined lifecycle stages, and integrated reporting, revenue can appear stable while underlying risk accumulates.
- A single large client reducing volume.
- A market segment cooling.
- A specialty discipline operating below target utilization.
These shifts often feel sudden. They are rarely unpredictable. They simply were not measured.
The Case Study: Instrumentation Before Intelligence
No engineering firm would operate critical infrastructure without monitoring performance data. Yet this firm was operating its growth engine without centralized CRM governance, consistent follow-up processes, or reliable attribution tracking.
Marketing activity existed. Business development activity existed. But the systems connecting them were fragmented.
Leadership could not clearly connect marketing investment to project type, utilization by discipline, or revenue contribution. Forecasting leaned more on intuition than on data.
Once a centralized CRM infrastructure was implemented, pipeline stages were defined, and follow-up was standardized, patterns became visible.
Leadership could see:
- Which service lines generated demand
- Which inquiries aligned with priority project types
- How long opportunities remained in evaluation
- Where additional visibility was needed
Close rates improved from approximately 30 percent to 43 percent after structured follow-up and deal tracking were introduced. Web-generated opportunities closed at 35 percent, outperforming common benchmarks. Conversions from paid search improved by 30 percent once campaigns aligned with priority disciplines and lifecycle data.
These were not just marketing wins. They were instrumentation corrections.
Behavior Changes Before Revenue Does
In this firm, revenue growth did not appear first. Operational discipline did.
When lifecycle stages were clearly defined and enforced, pipeline timing became measurable. Time in early opportunity stages and assessment phases could be tracked and improved.
Email outreach that once relied on individual effort saw engagement increase by 140 percent after structured workflows were implemented. Technical content authored by engineers generated more than 56,000 pageviews, with 73 percent of traffic coming from organic search. With proper attribution tracking, that visibility translated into qualified pipeline rather than isolated traffic metrics.
The measurable improvement in close rates and engagement preceded financial impact.
Within the first full year after implementing a structured growth system, revenue contribution exceeded total commercial investment by a factor of two.
The improvement did not come from increased activity.
It came from replacing anecdote with data.
Revenue Intelligence Requires a Clean Foundation
Many organizations are layering AI-driven revenue intelligence onto inconsistent CRM inputs and undefined lifecycle stages.
That approach rarely produces clarity.
AI does not compensate for poor data hygiene. It amplifies it.
In this case, meaningful performance improvement occurred only after the data foundation was stabilized. Lifecycle stages were standardized. Follow-up discipline was enforced. Attribution became visible.
Only then could forecasting become reliable.
Client Concentration and Enterprise Risk
This firm also faced revenue concentration exposure. A meaningful percentage of annual revenue was tied to a small number of long-standing clients. While stable on the surface, this structure introduced vulnerability.
Revenue concentration above 30 percent in a single client or sector is a structural risk few organizations would accept elsewhere in operations. Yet many tolerate it within their portfolios because CRM segmentation and reporting lack clarity.
With improved visibility into demand flow, leadership could intentionally diversify. They could identify which capabilities resonated in adjacent markets and reduce reliance on reactive selling.
Predictability improved.
And predictability strengthens enterprise value.
Applying Engineering Rigor to Martech Governance
Engineers solve complex problems through a clear process:
- Establish a baseline.
- Identify root causes.
- Implement measurable solutions.
- Monitor performance and refine over time.
Revenue operations and martech governance benefit from the same discipline.
A structured growth system connects marketing, business development, and sales into a single visible pipeline. It standardizes lifecycle definitions. It enforces data hygiene. It provides attribution clarity. It allows leadership to forecast with greater confidence.
This is not about increasing promotional activity.
It is about reducing uncertainty.
Closing the Visibility Gap
The revenue blind spot uncovered in this engineering firm is not unique to engineering.
It is a martech maturity issue.
When leaders cannot clearly trace how opportunities originate, how they progress, and how investment translates into predictable revenue contribution, they are operating without full visibility.
The organizations that close this gap do not necessarily work harder. They measure better.
How Collage is Reshaping Asset Management for Growing Brands
marketing 26 Feb 2026
You’re stepping into the CEO role at Collage as the company enters its next phase of growth. What’s your vision for Collage moving forward?
Collage was built on a simple belief: managing important brand content shouldn’t feel heavy, complicated, or out of reach for growing teams.
As we enter our next phase, our vision is to become the modern infrastructure layer supporting how brands organize, distribute, and ultimately get more value from their content. We’re focused on doing the core work of Digital Asset Management exceptionally well — search, organization, and sharing — while removing the enterprise complexity that has defined the category for a decade.
We’ve grown consistently by serving teams who felt priced out or overwhelmed by legacy systems. Moving forward, we’ll double down on that momentum: improving migration experiences, strengthening our distribution capabilities, and investing in performance and simplicity.
How do you define Digital Asset Management today, and where do legacy platforms fall short?
At its core, Digital Asset Management should be the system of record for brand content — the place where assets are structured, searchable, and controlled, ultimately with the purpose of optimizing the value created by that content.
Historically, though, DAM platforms were designed more like vaults — places to store and protect files. Distribution was treated as a secondary feature. And over time, that inward focus created layers of complexity and rising costs.
Today, content flows outward constantly — to agencies, retail partners, sales teams, distributors, media outlets, technology platforms. If distribution isn’t central to the DAM, teams default to workarounds: email attachments, shared drives, Slack threads, ad-hoc file transfers. That fragmentation is where control and efficiency break down.
A modern DAM needs to function as an engine, not just a vault. It should activate content — making it easy to find, permission, package, and distribute without adding operational overhead.
Where legacy platforms often fall short is assuming every team needs enterprise-level complexity. Many growing organizations don’t. They need speed, clarity, and workflows that match how content actually moves.
That’s the shift we believe the category needs to embrace. By shifting focus to what we believe matters most for these teams, we can fundamentally make the platform more affordable without sacrificing impact for the vast majority of brands out there.
Collage describes itself as “distribution-first.” What does that mean in practice?
Distribution-first means designing DAM around how content is actually used — not just how it’s stored. At Collage, we focus on two core audiences:
- The teams who create and manage assets
- The audiences who consume and amplify them
By stripping away unnecessary complexity and streamlining distribution, we make modern DAM fundamentally more cost-effective, scalable and user-friendly. Practically, that shows up in capabilities like branded distribution portals, dynamic share links, asset embeds, flexible permissions, and modern search. Instead of responding to constant requests, teams can proactively enable access — reducing friction, eliminating bottlenecks, and accelerating brand amplification.
What problem does Collage solve that other DAM platforms struggle with?
We solve the mismatch between modern marketing teams and legacy software expectations.
Many growing brands are managing thousands of assets, collaborating with agencies, dealers, distributors, or partners — but they don’t have enterprise budgets or supporting IT departments.
Legacy DAM platforms often assume both. Collage removes the friction around:
- High contract costs
- Complex platforms that are difficult to adopt
- Fragmented systems for managing content
- Inability to activate content even if centralized and organized
We provide the essential capabilities teams actually use — search, tagging, portals, custom metadata, structured organization — without forcing them into a heavyweight system.
That balance of power and simplicity is where we see the most traction.
Who is Collage built for, and how does it fit into the broader DAM market?
Collage operates squarely in the Digital Asset Management category, but our philosophy is different. While many legacy vendors prioritize enterprise breadth and highly specialized functionality, we emphasize:
● Simplicity over complexity
● Access and distribution
● Practical value over feature accumulation
Rather than building another feature-heavy system, we designed Collage from the ground up to focus on essential functionality with real impact. That focus allows us to deliver a powerful DAM experience without the cost or complexity that has deterred so many teams. That makes Collage especially well-suited for growing brands that have historically been underserved or priced out of DAM altogether — as well as modern teams that want speed, clarity, and control without enterprise overhead.
How do you think DAM needs to evolve to keep pace with modern marketing workflows?
Digital Asset Management needs to become more connected, more intelligent, and more flexible.
The future of DAM is less about managing files and more about orchestrating access — ensuring the right people can find and use the right assets at the right time.
First, platforms must integrate seamlessly with the tools marketers already rely on — design software, CMS platforms, automation tools, and perhaps most importantly, AI systems. DAM can’t be an isolated repository; it should facilitate a broader content ecosystem.
Second, search and metadata management must continue to evolve. As asset libraries grow, discovery becomes mission-critical. Teams need fast, precise ways to surface what matters. This is also essential for fully leveraging the emerging AI capabilities.
And finally, AI will reshape expectations entirely. Most of what we see as AI in the category will become commoditized. However, the platforms that evolve distribution into AI-ready infrastructure will be the ones that deliver lasting value.
The companies that embrace this shift — from vault to engine, from storage to orchestration — will define the next generation of DAM.
What does success look like for Collage over the next few years?
Success means making DAM feel approachable, effective, and indispensable for modern teams. If Collage can help brands reduce friction, save time, and get more value from their content - while reducing complexity - we're doing our job.
Ultimately, our goal is to redefine expectations for DAM: simpler, more affordable, and built for how content actually moves in today’s organizations now and in the future.
How CMOs are rethinking metrics moving beyond vanity KPIs toward lifecycle value and ROI, while balancing CFO pressure to automate with the need to preserve brand voice and integrity.
marketing 26 Feb 2026
How is NRF redefining the role of events by becoming a platform for ecosystem-building and community?
Events as a whole are an excellent way to create a larger sense of community. However, NRF specifically creates continuity across various retail partners, technology providers, and decision-makers, allowing ideas and relationships to develop past the show floor. This show in particular excels at purposeful networking and going beyond an annual meeting, operating as an ecosystem that builds connections and offers a platform to connect everyone involved.
For example, they host education sessions for practitioners to share their best practices. They also offer year-round engagement like virtual sessions, meetups, and gatherings on a global scale, underscoring the foundation’s core role in driving innovation to shape the industry.
NRF integrates data and AI to offer personalized attendee journeys and engagement experiences at every touch point, before, during and after the event. Doing this builds richer and more relevant connections, both to the content and other professionals.
In your view, what will differentiate high-impact events from “check-the-box” events over the next few years?
Traditionally, marketers have relied on surface-level metrics to determine the success of their events for years. However, in today’s complex landscape and with the increased use of digital tools, only counting vanity metrics, like number of attendees, falls short in showcasing the full value and business impact of an event.
With more AI and tech solutions at marketers’ disposal in the next few years, the impact of events will be measured by how effectively marketers can convert interactions into long-term value. AI tools can speed up this process by helping teams to look beyond the isolated actions taken at an event, such as registration data, engagement metrics, or survey feedback, so they can better understand how the attendees’ experiences impact tomorrow’s pipeline, revenue, and relationships.
The distinction between a “check-the-box” and high-impact event will ultimately come down to whether marketing teams can prove an event contributed this type of sustained momentum, or only captures activity in isolation.
How has the shift toward lifecycle value and long-term ROI changed the way marketing teams plan and evaluate campaigns?
The traditional linear funnel of sales no longer applies in the world of B2B. 44% of marketers believe they’re effective at running in-person events, but because buying decisions are becoming more complex with multiple stakeholders, teams must rethink how they can accelerate business goals and capture the best ROI from events.
This led to a more connected approach to planning and evaluating activations, where teams leverage AI to better analyze attendee behavior and engagement metrics to predict customer lifetime value, and estimate long-term revenue and ROI potential. Evaluation now centers on influence across the full journey, not just performance at a single point.
What challenges do CMOs face when trying to align metrics with executive expectations especially at the board and CFO level?
Attribution is one of the biggest challenges when it comes to marketing and aligning metrics with executive expectations. Opportunities aren’t driven by a single tactic, but rather influenced by a mix of campaigns, channels, content, timing, and follow-ups. Revenue is the result of multiple touchpoints working together, often over a long period of time. But that complexity doesn’t always translate easily in boardroom conversations. What boards want is clear ROI with data to back it up.
Time horizons and differences in priorities are another challenge. Boards and executives work on quarterly and monthly time horizons, while many marketing investments are set at longer times for impact. Focus areas are also intrinsically linked but different – boards care more about data like revenue growth, margins, and cash flow, and marketers focus more on share of voice, engagement, pipeline metrics, velocity, and customer lifetime value.
Overall, CMOs aren’t struggling to prove ROI from a lack of data, but rather how they connect that data to metrics discussed in the boardroom. In fact, the global martech market is expected to increase from $131B in 2023 to $215B by 2027. Despite the enormous industry growth, many companies still can’t tie their current marketing investments directly to business outcomes. Fragmented systems across marketing, sales, and events, only amplify these challenges, making it difficult to tell a cohesive story.
To demonstrate value and close this gap, more marketers will partner hand-in-hand with CFOs to establish shared KPIs and align on what real impact really means, while beginning to measure data beyond clicks and impressions, and managing martech like a strategic enterprise asset.
CFOs are pushing for automation and efficiency across marketing. Where do you see automation delivering value and where does it do more harm than good?
Pushing for automation where it drives operational clarity and scale can help improve overall operations and remove friction, reduce cost, and provide resource allocation. However, automation delivers the most value when it removes the burden of manual analysis and allows teams to act faster on metrics that actually influence business outcomes. For example, AI can synthesize attendee behavior across events, digital interactions, and post-event engagement to surface patterns that would otherwise take weeks or months to uncover. Examples of this include which buying groups are showing intent, which topics correlate with pipeline creation, and how long it typically takes for this momentum to turn into revenue. This helps marketing and sales teams prioritize follow-up, tailor messaging, and focus their time on the highest-value opportunities to boost rather than chasing every interaction equally.
On the other hand, it can cause issues when it replaces intentional experience or human connection. It’s important to keep authenticity in mind when it comes to automation. AI can be a powerful engine for fueling meaningful connections, but leaning on generic, AI-generated marketing content only adds to growing digital fatigue, and risks customer backlash. Automation should power precision and insight, not replace empathy and strategy.
What role does cross-functional alignment between marketing, finance, and technology play in redefining success metrics?
Cross-functional alignment is essential for moving from reactive reporting to proactive growth planning. Marketing, sales, finance, and technology all contribute unique perspectives and pieces of the data needed to understand true event impact. However, when these teams operate in silos, success looks different depending on the system or report being referenced, whereas alignment enables a shared view of performance that leadership can trust, build upon, and act on for tangible results.
Using customer data integration (CDI) centralizes and aligns customer information across all touchpoints, such as sales, marketing, and service channels. For example, by integrating real-time behaviors with holistic customer journey records, businesses can create detailed, up-to-date customer profiles. Based on these customer profiles, companies can tailor communications and touchpoints to individual preferences, ensuring a more relevant and personalized experience.
CDI also facilitates an omnichannel approach that supports seamless, two-way communication and continuous feedback loops, streamlining workflows by breaking down silos between departments and enabling teams to work collaboratively.
How do data and storytelling coexist in a metrics-driven marketing organization?
Data provides the evidence of how buyers behave, how long it takes for impact to occur, and which touchpoints influence outcomes. Storytelling connects those insights into a narrative that explains what changed, why it matters, and what should happen next. Together, they allow organizations to move beyond dashboards and into strategic decision-making. Without storytelling, even sophisticated data fails to drive alignment or action.
What capabilities, technological or organizational will be most critical for CMOs to thrive in this evolving landscape?
AI is disrupting and transforming the way that we work, as more marketing organizations are already learning to vibe code to deliver more value quicker. The most successful teams will be the ones who embrace AI, but also inspire their teams to use it for better efficiency when it comes to automation, workflows, and automating tasks. This allows teams to focus on the strategic areas and accomplish more in advancing their company's objectives.
CMOs will also need the ability to see and understand the full, non-linear customer journey across digital, hybrid, and in-person experiences, rather than relying on isolated metrics from individual campaigns or channels. The CMOs who thrive will be those who can combine human judgment with predictive insight to turn interactions into sustained momentum, loyalty, and revenue over time.
As part of this, authentic thought leadership and messaging in the midst of AI and digital fatigue will be paramount. With more brands pivoting to AI to support their content engines, there is risk for undifferentiated, vanilla perspectives, meaning brands need to elevate their efforts to break through the noise.
How event technology is becoming a core go-to-market lever, enabling organizations to translate engagement signals into smarter pipeline and revenue strategies.
marketing 26 Feb 2026
What does “meaningful engagement” mean in today’s environment especially for audiences that are digitally saturated?
Meaningful engagement today is defined by intent and relevance, not volume. Audiences are inundated with content and notifications, meaning attention is no longer freely available, and has to be earned. Engagement becomes meaningful when the interaction is timely, context-aware, and clearly valuable to the individual on the other side of the device. Instead of asking the audience to passively consume more content, the focus should shift to creating moments where they can participate, signal interest, and move a relationship forward.
Increasingly, meaningful engagement has become a two-way exchange between the audience and the brand. This enables the audience to derive personalized value by actively participating in the conversation, experience, and community. It can be as simple as emoting on a livestream, or as complex as a tailored meeting with hand-picked experts, peers, and activities for that specific audience. Because most aspects of customers’ day-to-day lives have become digitally saturated, this type of valuable engagement also delivers purpose.
Event technology has evolved far beyond registration and badge scanning. What capabilities are now table stakes for delivering connected event experiences?
Today’s event technology must support the holistic attendee experience, going beyond a single event or program. To do this, a connected data foundation is essential to modern event experiences. Table stakes now include unified attendee profiles, real-time behavioral data capture, and bi-directional integration with marketing systems and CRM. These capabilities allow teams to understand more than simply who attended, but how and when they engaged, what they found valuable, and where that activity fits within the broader customer journey. Without this connective tissue, events remain disconnected moments rather than strategic touchpoints.
What role does real-time data play in adapting experiences while an event is still happening?
Real-time data allows teams to move beyond static execution models by providing immediate visibility into how attendees are engaging across sessions, networking, and event content. It can take an event from a fixed program to an adaptive environment. When teams have immediate visibility into session engagement, content interaction, and attendee movement, they can respond in the moment and adjust staffing, promote different content, or facilitate more meaningful connections. This shift away from static agendas and toward experiences that evolve based on actual behavior make the event more responsible and valuable for attendees.
Furthermore, events provide a wealth of first-party intent signals that can offer value beyond logistical management. The real-time discovery of an expansion opportunity, high-value meeting with sales, qualified lead for a partner, or connection with an influencer, are just the beginning of key customer milestones achieved through an event experience. These behaviors can drive the right next best action to accelerate the customer journey, whether it be another onsite experience, marketing campaign, sales engagement, or recommendation.
Many organizations now view event technology as a core part of their GTM stack. What’s driving this shift?
Events generate some of the strongest first-party engagement signals available to marketers. As traditional digital attribution becomes less reliable, organizations are prioritizing channels that provide clear indicators of intent. Event interactions, such as what session an attendee joins, who they meet with, what they participate in, offer high-confidence insights into buyer interest. When that data is connected directly into GTM systems, events move from beginning standalone moments to measurable accelerants to pipeline and revenue.
What challenges do organizations face when translating event engagement data into actionable pipeline insights?
Fragmentation is the biggest challenge. Event data is often captured across disconnected tools without a shared data model or consistent definitions of engagement. This makes it difficult to unify insights, act quickly, or deliver clear context to sales teams, which can slow down analysis and follow-up. When engagement data isn’t standardized or integrated, its value can decay rapidly after the event, limiting its impact on follow-up and pipeline acceleration.
How are sales using event intelligence to have more relevant, timely conversations with prospects?
Event intelligence gives sales teams context before and after contact. As opposed to starting conversations cold, reps can see what a prospect engaged with in the past, topics of interest, and where interest was concentrated. This allows outreach to be relevant and rooted in the attendee’s experience, not just within a generic sales narrative.
As part of this, event insights can also greatly inform post-engagement follow ups. When data is delivered quickly and shows which attendees had the highest levels of engagement or interest, follow up conversations are not only timely, but better aligned with buyer intent.
What organizational shifts are required to treat events as a revenue driver rather than a brand-only channel?
It starts with intentional planning and shared ownership. Events have to be designed with GTM outcomes in mind from the beginning, supported by shared KPIs across events, marketing, and sales. Organizations need to move away from managing disconnected tools and toward orchestrating outcomes by relying on technology to handle complexity while teams focus on strategy, alignment, and execution.
Revenue teams should also be an active participant in pre-, during, and post-event planning, to drive audience acquisition, craft personalized experiences, and have better oversight in engagement. These elements should be readily available and a part of co-planning efforts in order to support nomination goals, activities like timely follow ups, and management oversight.
What innovations in event technology are you most excited about from a GTM and revenue perspective?
The most meaningful innovation is the shift from task automation to decision support. Emerging, agent-based approaches can help teams identify buying signals, guide attendees through relevant experiences, and recommend the next best action in real time. Rather than replacing human judgement, these systems augment it, helping teams recognize key moments as they happen, and act with greater precision across the event lifecycle to ultimately connect the full spectrum of events to the customer journey.
Perfecting Personalized Promotions, The secret to achieving advanced personalization, and what’s holding retailers back
marketing 25 Feb 2026
Jeff Baskin, Chief Revenue Officer
Promotions are a key performance area for all retailers, and effectively implementing truly personalized offers at scale has been a goal for enterprise retailers for decades. Eagle Eye’s CRO Jeff Baskin shares his thoughts on how technology is making this goal more attainable, the legacy approaches that are holding retailers back, and what impacts they can expect from genuine one-to-one promotional engagement.
1. What are the biggest inefficiencies you see in traditional promotional models today and why are so many retailers still relying on broad, mass-discount approaches?
The biggest flaw with traditional mass discounting is that it often incentivizes customers who would have purchased anyway, while failing to influence behavior where it matters. This inefficiency is largely driven by legacy systems and the inertia of “what’s always worked.” Most retail infrastructure was built to support blanket offers or, at best, broadly segmented campaigns, not individualized promotions at scale. For years, that was effective, but as competition intensifies and technology improves, more retailers are embracing approaches that enable one-to-one engagement. After all, dynamically creating an offer for the exact brand of organic snacks the individual customer is most likely to respond to at the exact discount level most likely to prompt them to action is inherently more effective – and efficient – than placing a generic discount on similar items in the weekly circular.
2. Why has true one-to-one promotional personalization been so difficult to achieve?
Manual processes, unstructured data, and legacy platforms are the main roadblocks to true one-to-one personalization and are what keep retailers relying on broad-based approaches. There are also two issues of scale: first, the volume of data (customer data, SKUs, multiple sales channel data) retailers must manage has increased exponentially; and second, the ability to deploy personalized offers at enterprise level across millions of transactions remains out of reach. Few incumbent promotional systems support the real-time decisioning or on-the-fly offer creation necessary to deliver unique promotions to individual customers across a multi-store network, let alone a portfolio of banners.
3. What has changed (technologically or operationally) that is now making individualized promotions possible at enterprise scale?
From a tech perspective, AI and machine learning models can now analyze behavior and generate custom offers in milliseconds. Cloud infrastructure handles the computational demands of real-time adjudication across millions of shoppers or loyalty members. Operationally, retailers now have the customer data and digital touchpoints necessary to identify individual shoppers and deliver offers at checkout, online and in-app. These two components are equally important; even the most advanced AI will deliver irrelevant promotions without data-based insights into what individual customers care about, and all the customer data in the world is useless without the technology make it actionable.
4. Retailers often struggle to know whether an offer is actually influencing behavior or simply rewarding shoppers who would have purchased anyway. How can retailers start measuring true incremental impact?
Attribution at the individual shopper level is essential. You need systems that track each customer's baseline purchasing patterns, then measure how behavior changes when specific offers are delivered. Closed-loop reporting that connects offer allocation, redemption, and actual sales lift reveals which promotions are working. Of course, this requires technology that follows the complete customer journey from offer to purchase, across platforms and channels, and incorporating both marketing-exclusive systems (like retail media networks) and traditionally analog interaction points (like physical stores).
5. Boston Consulting Group has estimated that shifting even a portion of mass promotion spend into personalized offers can dramatically improve ROI. What does that tell us about how much promotional budget is currently being misallocated?
BCG estimates enterprise retailers can generate over $100 million in topline impact from scaling personalized offer execution. That suggests that retailers’ current promotional spending is underperforming, delivering little incremental value for the budget. It tells us that when retailers offer undifferentiated incentives to customers with existing purchase intent, or offer deeper discounts than necessary to change behavior, they’re essentially paying for sales they already had.
7. As shoppers’ expectations for immediate value increase, how are promotions emerging as a new competitive battleground for retailers?
Customers now expect offers that reflect their actual shopping behavior; generic discounts feel irrelevant. In this way, promotions have become a de facto indicator of whether retailers truly understand their customers. Those who do can deliver timely, meaningful incentives, build stronger engagement and capture more share of wallet. Those who don't risk spending promotional dollars with little measurable return. In a marketplace with more choice than ever, relevance is a clear competitive advantage.
8. When promotions are personalized at the individual level, how does that change the way shoppers engage with offers and deliver value at the right moment?
Personalization ensures that customers receive offers that feel relevant and appropriate rather than random or excessive. When offers align with consumers’ actual preferences, purchase patterns and contextual cues, engagement naturally increases. Delivered through digital channels or at checkout in the moment of decision, personalized incentives create a higher-value experience that encourages repeat behavior and strengthens ongoing loyalty. They also drive results for retailers; Eagle Eye’s AI-powered Personalized Challenges, which creates personalized, incremental goals for each shopper based on their purchase history, has generated 7:1 ROI for high-profile retailers that have implemented the solution
AI’s Double-Edged Sword: Countering AI-Enabled Cyberattacks by Deploying Defensive AI Strategies
artificial intelligence 24 Feb 2026
By Dr. David Utzke, CEO and CTO at MyKey Technologies
Organizations are at an inflection point where AI is accelerating cybercrime at scale, as experts warn that it broadens the attack surface, creates new vulnerabilities, and introduces complex governance and compliance challenges.
Like all AI systems, those deployed in cyberattacks continuously learn and evolve, enabling them to adapt, evade detection, and develop attack patterns that traditional security tools may fail to recognize.
Furthermore, AI agents capable of operating autonomously are significantly increasing the scalability and sophistication of cyberattacks and fraud operations.
(Q) What makes AI-powered cyberattacks fundamentally different from traditional automated cyber threats?
This is a great question and one that I am frequently asked. I find it helpful to begin by defining a cyberattack. In cybersecurity, a cyberattack is an intentional, malicious attempt by an individual or organization to breach a computer network or system. These attacks aim to compromise the CIA triad: the Confidentiality, Integrity, or Availability of digital assets and information. The NIST (National Institute of Standards and Technology) CSRC (Computer Security Resource Center) Glossary officially defines it as an attempt to gain unauthorized access to system services or resources, or to compromise system integrity and availability.
So, working from this common definition of cyberattacks, AI-powered cyberattacks differ from traditional, non-AI attacks primarily through increased speed that increases the capability in the number of attacks, considerable automation lowering the barrier to entry, and intelligent, real-time adaptation to avoid detection. While traditional attacks rely on manual, static methods, AI technologies enable autonomous scanning, evasive polymorphic (i.e., occurring in several different forms) malware, and highly personalized social engineering at scale, transforming the threat landscape from weeks of planning to near-instantaneous execution.
Some of the core advancements in AI technology-facilitated cyberattacks include:
o Hyper-personalized social engineering
o Synthetic media (deepfakes)
o Autonomous vulnerability discovery
o LLMjacking
o Prompt Injection
o AI Model data poisoning
(Q) How can autonomous AI agents amplify the speed, sophistication, and scale of modern cybercrime?
It is important to articulate that the term “autonomous AI agents technology” is considered partially accurate but often hyped, representing an emerging capability rather than a fully realized, foolproof technology as of the time of this interview. I have to laugh every time I see the ServiceNow ad on streaming. In the dialogue, when AI agents are brought up, it is clarified that they are not just “secret agents,” but rather “autonomous minions that you control” to handle routine, repetitive tasks. How can the minion (def.: underling of a powerful person) at the same time be autonomous? Get it? The hype!
So, here is another opportunity to define another frequently misunderstood term from the perspective of AI architecture. An “AI agent” is most commonly an LLM (Large Language Model) that can take actions to achieve specific, high-level goals with minimal human oversight – a step up from an AI bot. Unlike an AI bot, AI agents can break down complex tasks, use tools, and learn from experience.
An AI agent is a coded system that can, to a limited extent, set its own sub-goals, plan, and take actions to achieve a high-level objective with little to no human intervention. However, most, if not all, current “autonomous” agents require human-in-the-loop for oversight (aka Human Agent), especially for high-stakes decisions, making them more “agentic” than fully autonomous.
The term “autonomous AI agents” is often used as a marketing buzzword that obscures the actual technology behind it. To highlight AI technologies involved in cyberattacks, include:
o ML (Machine Learning) and DL (Deep Learning)
o GPTs (General Pre-trained Transformers) and LLMs (e.g., WormGPT and FraudGPT)
o GANs (Generative Adversarial Networks) and NNs (Neural Networks)
o NLP (Natural Language Processing): Voice-to-Text and Text-to-Voice
Given the advancements in ML, specifically DL, AI models can understand complex, nuanced language patterns. NLP is the driving force underpinning LLMs, enabling more accurate, context-aware, and human-like interactions to enact more sophisticated cyberattacks against cybersecurity frameworks, even if an organization deploys AI-enhanced cybersecurity systems.
It is for this reason that it is crucial for cybersecurity professionals to understand AI model architecture rather than treating AI as an impenetrable “singularity” or a magical black box. As AI models become deeply integrated into IT infrastructure, understanding the specific mechanisms, data pipelines, potential failure points of these systems, and how to audit AI models for vulnerabilities is essential for effective, proactive defense. Viewing AI as a “singularity,” or as a mysterious, all-knowing entity, leaves organizations vulnerable to unique, AI-based cyberattack threats.
(Q) How can organizations detect AI-generated attacks that are specifically designed to evade conventional security tools?
When I teach grad students and CPE sessions on the topic of cybersecurity, I emphasize that the first necessary step for an organization is to have a well-established AI model and data governance framework. Implementing technology governance frameworks is no longer just a compliance task; it is a foundational strategic requirement for any organization. Having AI model and data governance frameworks is critical for organizations to ensure AI initiatives are reliable, ethical, secure, and compliant with emerging regulations. Without a governance framework, organizations face significant risks beyond cyberattacks that include biased models, inaccurate or harmful outputs, as well as suffering from potential reputational damage and legal penalties.
With the above noted, cybersecurity professionals can audit and detect AI-based cyberattacks, which often evade traditional defense mechanisms. But it requires moving from point-in-time, snapshot, random, or set periodic audits to a continuous monitoring approach. Continuous monitoring is crucial because it replaces snapshot, point-in-time, or random audits with real-time, “always-on” visibility, allowing organizations to detect and remediate risks instantly rather than months later. It reduces security vulnerabilities and ensures continuous regulatory compliance (e.g., DORA, PCI DSS 4.0).
(Q) In what ways can companies move from reactive incident response to predictive, AI-driven threat prevention?
To protect against AI-based cyberattacks, organizations need to adopt a ZTA (zero-trust architecture) and a defense-in-depth strategy that combines AI-driven security tools, robust AI governance, and enhanced human training. Key measures include deploying anomaly detection, behavioral biometrics, and automated AI-based security tools to counter rapid, automated attacks, while enforcing strict data validation to prevent data poisoning.
· Defense-in-depth is a comprehensive cybersecurity strategy that layers multiple, heterogeneous security controls—covering people, technology, and operations—to protect assets, ensuring that if one defense fails, others contain the threat. Inspired by military, castle-style tactics (i.e., reinforced architecture), it aims to increase attacker complexity and prevent single points of failure.
· ZTA is a cybersecurity framework based on “never trust, always verify,” treating all network traffic as hostile, regardless of origin. It removes implicit trust, focusing on strict IAM (Identity & Access Management) verification, least-privilege access, and microsegmentation (divides networks into small, isolated, and granular security zones) to contain breaches. Key components include continuous monitoring, MFA, and data encryption to secure distributed, modern, cloud-based environments.
(Q) How can resilient risk-based AI governance frameworks help organizations rebuild trust and accountability as AI-driven threats continue to escalate?
As AI-driven cyber threats, such as adversarial attacks, data poisoning, and model BS (imprecisely called hallucination), escalate, the need for governance frameworks to provide the necessary guardrails to ensure AI technologies are reliable, ethical, and secure becomes even more urgent.
Key ways that governance frameworks rebuild trust and accountability include:
· Establishing Proactive Risk Management
· Ensuring Transparency and Explainability
· Enforcing Clear Accountability
· Implementing Real-Time Monitoring and Control
· Aligning with Ethical Standards
In addition, well-devised governance frameworks counteract escalating threats as AI-driven threats grow, by offering a structured approach to resilience by incorporating Red-teaming and adversarial testing to uncover security gaps before deployment, Data Security Posture Management (DSPM) to protect sensitive data used in AI workloads, and continuous monitoring to identify vulnerabilities and potential threats in real-time.
Ultimately, these frameworks turn cyberattacks into a manageable risk and compliant processes, moving from a position of “control” to “confidence.”
As a final note, this interview is given with an eye on research of the near-term future of cybercrimes through cyberattacks as AI technologies that are currently being converged with quantum computing. MyKey Technologies is addressing the research involving the near-term future (2026–2030) of the integration of Artificial Intelligence (AI) technologies with emerging quantum computing capabilities, which is set to fundamentally reshape the threat landscape, turning cybercrime into a highly automated, “agentic” ecosystem. While fully functional quantum attacks on encryption are anticipated closer to the 2030s, the immediate threat lies in the combination of AI-powered reconnaissance with the “harvest now, decrypt later” (HNDL) strategy.
So, balancing the immediate, “here-and-now” threat responses with attention given to near-term strategic planning is a critical, yet challenging endeavor for organizations. However, failing to do so can lead to a “whack-a-mole” cycle of endless crisis management. Effective approaches involve integrating short-term actions into a strategic vision regarded as strategic agility.
Cancer Awareness Month Can Highlight Real Community Support
marketing 19 Feb 2026
Each February, the country turns pink. Landmarks glow, national campaigns launch, and stories of survival and resilience fill television screens and social feeds. The scale of support is both inspiring and necessary, reminding millions that they are not alone in the fight against cancer. Yet beyond the national spotlight, something quieter is happening.
In hospital waiting rooms, volunteers sit beside patients before chemotherapy begins. In community centers, local nonprofits coordinate rides so no one misses treatment. In church basements and neighborhood gathering spaces, families come together for support groups because healing is not only physical, but emotional. In kitchens across America, neighbors prepare meals for someone too exhausted to cook.
These moments rarely make headlines, but they form the backbone of the fight.
Cancer is deeply personal. It touches families street by street and house by house, and the organizations responding most immediately are often local and deeply rooted in the communities they serve. They know the names behind the diagnoses. They understand the practical barriers patients face. And they continue showing up long after awareness campaigns fade from view.
Cancer Awareness Month offers a powerful national platform. The opportunity before us is to extend that platform to the people doing this work closest to home.
Imagine if the storytelling strength that powers major national campaigns also illuminated the hospital down the road, the screening event at the high school gym, or the local survivor who turned personal hardship into community action. When people see their own community reflected back to them, something shifts. Engagement becomes personal. Support becomes immediate. Action feels tangible.
Today, we have the technology to elevate local organizations with the same creative quality and reach once reserved for large national causes. Through modern media channels, community based nonprofits can share their stories at scale, connecting households to resources and reminding viewers that help is not abstract. It is nearby.
National momentum and local action do not compete with one another. They reinforce each other. Broad awareness drives conversation, while local visibility drives participation. Together, they create a stronger and more responsive support system for patients and families.
Awareness is most powerful when it becomes tangible, when it connects a household to a place they recognize, a service they can access, or a story they understand. It is measured not only in dollars raised or campaigns launched, but in rides provided, meals delivered, appointments kept, and hands held during uncertain moments.
The fight against cancer lives in communities, carried forward by neighbors, volunteers, caregivers, and local leaders who work tirelessly, often without recognition. This Cancer Awareness Month, as we honor the national movement, let us also make space to elevate the people doing the work closest to home. Their impact is real, immediate, and deeply human. They deserve to be seen.
Why longevity and adaptability after deploying agentic AI will define enterprise success in 2026
artificial intelligence 19 Feb 2026
Spokesperson: Adam Beavis, Country Manager Australia and New Zealand, Databricks.
Q1: Agentic AI has moved quickly from experimentation to deployment. What will separate organisations that succeed from those that fall behind after rollout?
A: What separates organisations that win with agentic AI after rollout from those that stall is less about the tech — and more about the operating model and discipline. The key differentiators tend to be:
1. Data readiness: High performers invest heavily in clean, permissioned, continuously improving data and instrument agents with feedback loops. Without this, agent performance degrades quickly after initial rollout.
2. Strong guardrails and governance by design: Winning organisations bake in controls, auditability, escalation paths, and human-in-the-loop thresholds from day one. Those that fall behind treat governance as an afterthought—leading to trust issues, halted deployments, or regulatory friction.
3. Clear business ownership, not just tech ownership: Successful firms tie agents to specific business outcomes (cost, speed, risk reduction, revenue uplift) with accountable business unit owners.
4. Cultural and behavioural change: The biggest gap is human, not technical. Leaders who succeed redesign roles around human–agent collaboration, and retrain employees to integrate AI into their daily work and oversee autonomous systems.
Q2: Many organisations feel they have done AI once it is deployed. What often goes wrong for organisations beyond that point?
A: The biggest misconception is treating deployment as a box-ticking exercise. Models that are trained on historical data can drift as inputs change, and without consistent and continuous evaluation, problems often surface too late.
The solution is shifting from one-off checks to continuous evaluation in production. Just like humans need performance reviews, so do AI systems. Enterprises need systems that continuously measure performance against real tasks, retrain or adjust agents and balance quality against cost. Many early deployments struggle, because they were not designed with long-term operation in mind.
Q3: Why is the transition from single agents to multi-agent orchestration important for enterprises?
A: Enterprise work rarely happens in a single step. A realistic workflow often includes retrieving from multiple data sources and validating data against business rules, compliance checks, and a final decision with explainability requirements. Expecting a single agent to handle all these tasks reliably and efficiently is unrealistic.
In 2026 we will see broader adoption of multi-agent orchestration, where specialised agents handle distinct tasks and a supervising agent coordinates sequences, mirroring how human teams operate. While this gives the benefit of better performance, it also allows for improved governance. Each agent can be monitored and evaluated on its specific responsibility, modifications can be isolated, and the overall system remains transparent and auditable, and easier to troubleshoot.
Q4: Many enterprises struggle to get AI agents and applications into production. What is causing the bottleneck and how is Databricks addressing it?
A: The bottleneck is not building a demo, it is making agents reliable in the real enterprise. In productions, agents must consistently reason over complex, proprietary data, operate with guardrails and integrate with operational systems.
General knowledge of AI is becoming a commodity, but it’s still elusive to get AI that truly understands the proprietary data inside an enterprise. Many AI agents fail in enterprise environments because they prioritise ease of use over accuracy, leading to inconsistent results or behaviour organisations cannot trust.
Databricks is addressing this by building through a number of growth areas:
- The rise of AI-powered coding is changing how software is built. As developers create apps via natural language processing, those apps automatically need databases and agent backends. Databricks is seeing this first-hand, with over 80% of databases launched on Databricks now being created by AI agents, rather than humans. We are enabling developers to rapidly build applications that run on Lakebase and are powered by agents, all within a unified and governed platform..
- Enterprises need a modern transactional layer for AI-native apps. Traditional transactional databases have changed little for decades, so we launched Lakebase, which simplifies operational data workflows and is optimised for AI agents operating at machine speed.
- Agent Bricks then helps organisations build and deploy agents that can securely work within their own data, where most of the business value sits. It helps organisations build domain-specific agents that reason over their data, track quality with task-specific benchmarks and balance performance with cost over time. The aim is to make agent quality measurable and improvable in production, not assumed at deployment.
Together, this trifecta removes the common production blockers by combining an operational database layer, an agent-building platform that works with enterprise data and an application layer that helps ship faster, with reliability and governance built in.
Q5: In Australia and New Zealand, how are organisations specifically adopting AI applications and how does that differ from earlier phases?
A: Across Australia and New Zealand, we’re seeing a pivot from general-purpose experimentation to domain-specific AI applications that are grounded in trusted enterprise data, with stronger attention to governance and sovereignty. As organisations shift from pilots into production to deliver real business outcomes, organisations are now embedding AI into real workflows, from customer support and supply chain to finance and operations.
For example, Suncorp needed to scale AI across the organisations to improve claims accuracy, reduce operational risk and support more automated digital customer experiences. Manual processes were creating additional loads for staff, and employees often lacked instant access to complex policy and claims information when decision making was required. By building, deploying and scaling domain-specific AI directly into claims workflows with Databricks, the company has achieved 99% accuracy and saved more than 15,000 hours of manual workload.
Additionally, Atlassian uses Databricks AI/BI Genie to power on demand insights in plain English through Atlassian Rovo, its AI assistant. This allows teams across the business to ask complex questions of their data and receive trusted, contextual answers directly within their existing workflows.
These examples reflect a broader trend that we’re seeing across the region in 2026, where value increasingly comes from AI applications designed around the business, grounded in governed data, and operationalised end to end.
Q6: How are AI applications, including AI agents, playing out across key verticals? What advice would you give to enterprise leaders in these sectors?
A: Whether you lead in finance, pharma, media, CPG, or tech, the questions are converging: how do we use AI to improve business productivity? How do we balance industry regulation with AI innovation? How do we control costs without slowing adoption? The leaders who solve these challenges today will build faster, more resilient operations and gain a competitive edge.
In the public sector, AI is connecting data across agencies to reduce administrative burden and support decisions from benefits assessment to emergency response. Success depends on strong governance, clear lineage and transparency so outputs can be trusted and audited.
In marketing, AI applications are moving beyond content generation to orchestrating campaigns, analysing performance data and adapting system strategies in near real time. Data Intelligence for Marketing allows organisations to centralise customer and campaign data, apply AI to drive more accurate decisions, and automate tasks that scale human resources using AI agents.
In cybersecurity, multi-agent systems are proving effective to validate threats and accelerate response times while keeping humans in the loop. Databricks’ Data Intelligence for Cybersecurity powers scalable SecOps at scale with Agent Bricks by automating triage, enrichment, response and investigation, reducing alert fatigue and costs while boosting analyst productivity.
My advice for leaders is simple:
● Invest in your data and AI foundations with high-quality data and governance
● Have clear business ownership and outcomes you want AI to accomplish
● Scale what is already working.
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