B2B Marketing Trends 2025: Insights from ROI·DNA's Surj Gish

artificial intelligence 4 Aug 2025

1. What are the biggest differences in B2B marketing expectations or behaviors across EMEA, APAC, and North America and how does your team adapt accordingly?

There are a couple of primary differences between regions: cultural nuances and technical variations.

We have experts on the ground in key regions like Japan, Singapore, Australia, and throughout Europe, which allows us to adapt our approaches based on lived experience and a true understanding of the people in these areas.

Technical differences are often manifestations of cultural differences. For example, privacy laws in the European Union reflect a higher level of concern for data privacy, which affects the effectiveness of account-based strategies. We adapt to these concerns by rethinking targeting with an eye for context and focusing on networks like LinkedIn, where there is a combination of high-quality targeting data and clear consent.

2. How do you support ABM and ABX strategies, and what kind of client impact have you seen since its launch?

We’ve been doing account-based B2B marketing since before it became popular. The ability to focus on specific accounts or groups of accounts based on fit and value has been game-changing. However, it’s not a cure-all—factors like business maturity, sales support, and internal alignment all play a role in ABM success. As a result, we still run targeted demand generation campaigns where they make sense.

3. How are you evolving your demand generation and paid media strategies to match the pace of change in the B2B tech buyer’s journey?

The pace of change continues to accelerate, and we’re here for it. The easy answer is AI and automation, and we’re certainly investing in these areas to deliver faster, work smarter, and scale more effectively. But we’re also listening more closely than ever to our clients. One of the most important things we heard was the need for customized solutions instead of off-the-shelf tools.

This feedback led to the creation of our AI Lab and a suite of proprietary tools: Hotwire Spark and Hotwire Ignite. Hotwire Spark helps clients understand how AI tools like ChatGPT, Google Gemini, or Perplexity are answering questions from potential customers and influencing purchase decisions. Hotwire Ignite enables faster, smarter prioritization of target accounts.

4. What are enterprise tech companies looking for most in an agency partner today and how do you deliver differently?

Obviously, serious expertise is table stakes at this point. Over the past year, we've seen clients place much greater emphasis on the partnership and people aspects of their agency relationships. They want to work with engaged, committed teams—not at arm’s length. They want to actually enjoy the experience.

This has always been our approach. We’ve been focused from the start on being a true partner, delivering strong results and being genuinely enjoyable to work with.

5. What’s the next frontier for global B2B growth marketing, and how are you preparing for it?

This is a tricky one. A lot of people would point to using AI to scale, and that’s a solid answer. Like many, I see huge potential in scaling pseudo-1:1 campaigns with AI tools.

But honestly, one of the biggest challenges we still see is the weak link between sales data—actual contracts—and campaign performance. That data is often outdated or inaccurate, which makes smart optimization tough.

Just as we’ve been building our own AI tools, we’re also tackling these data issues head-on by creating custom solutions for each client. Every engagement starts with a data onboarding and integration process that leads to tailored performance dashboards.

6. What key trends should CMOs and B2B marketing leaders be watching over the next 12–18 months?

A big shift we’re already seeing is in the search landscape. SEM is typically a major part of program spend, but search isn’t limited to traditional search engines anymore. We now think of holistic search as a combination of organic and paid results, plus AI-generated responses—from ChatGPT answers to in-SERP AI results. More and more, people are seeking answers outside of standard search engines.

It’s a massive undertaking, but there’s a huge opportunity for brands that can invest in and effectively manage efforts across all three areas: organic, paid, and AI results. With our decades of deep expertise and proprietary tools like Hotwire Spark, we’re well-positioned to help our clients win.

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Omri Shtayer on AI Agents and Data-Driven Growth

artificial intelligence 1 Aug 2025

1. From a strategic perspective, how crucial is the depth and recency of data in the effectiveness of AI-powered insights for your organization's growth initiatives?

No organization should ignore the potential of AI to improve marketing effectiveness and productivity. That being said, marketing leaders shouldn't expect these tools to deliver the results if they’re being starved for data – that’s when you run into the unfortunate syndrome of chatbots “making stuff up” in an attempt to answer a question. The data being fed to the AI tool needs to be as complete and recent as possible, so that the AI tool stack is well informed and knowledgeable about your company and your market.

That’s particularly critical as we move from chatbot interactions to deploying AI Agents that we expect to do work on our behalf, more autonomously over time. We need them to execute tasks based on hard data and well-defined plans.

Think about data being like fuel for an aircraft. Skimp on fuel, and you won’t get high performance. You may not make it to your destination.

2.In what ways do you believe specialized AI tools can provide a competitive advantage in areas like SEO, sales, and market trend analysis?

Specialization in the software and the data is how we make AI more productive and impactful for marketing and sales. In addition to providing AI tools with high quality and up to date data, we want to focus it on the problem we aim to solve.

Similarweb’s mission is to provide marketers with more and better data, but the beauty of adding AI agents into the mix is that we don’t have to worry as much about overwhelming the users. Similarweb’s AI agents can cover a lot of ground very quickly, meaning, they can accomplish in minutes what otherwise would be time consuming research tasks.

With a conventional user interface for a data system, we work on developing individual screens and charts and dashboards to help users understand and navigate through the data. But given the limitations of our poor human brains, as consumers of that data it still takes time to look at each screen, understand what it means, and maybe download data into spreadsheets for additional analysis.

Instead of looking at one screen or one spreadsheet export at a time, they can look at all the data and make sense of it for us. We get the answers we’re looking for, all together, not in bits and pieces.

That doesn’t mean you let them do all the thinking for you. You and your colleagues still have to review the analysis the agent has produced and determine whether it is sound and makes sense for the organization. You might want to do some spot checking of the underlying data, just as you would for a new employee whose work you don’t entirely trust. However, the odds that the data will be correct and the output will be useful are a lot higher if you work with an AI agent that is specifically trained on marketing and the data that is important to marketers.

3.The "AI Meeting Prep Agent" is cited for reducing research time. How critical is the efficiency gained from such tools in optimizing the productivity of sales and business development teams within your enterprise?

The AI Meeting Prep Agent is a good example of focusing the technology on a common business task or challenge. A good salesperson will go into a meeting prepared to achieve the best outcome, and it’s something they do many times every week, or even every day. The AI agent functions as a capable assistant who finds out your goals for the meeting and produces a thorough briefing on the people you will be meeting with and the opportunities and challenges of their business, based on news reports and company data as well as digital signals.

The reception from Similarweb Sales Intelligence customers has been very enthusiastic, with customers sharing feedback about saving hours of work and going into meetings better prepared. We have a similar story with our AI Content Strategist, which crunches data on SEO and competing companies, identifies content gaps, and makes specific recommendations.

In addition to the agents announced at the end of May, we’re working on a lead generation agent, a competitive intelligence agent, a stock research agent, and an ad creative agent — while scanning the horizon for other opportunities to build or buy additional products. We also have several projects under way to create agents for our own internal use.

4.How can rapid AI-driven insights into emerging market shifts directly inform and accelerate your strategic decision-making processes?

Everyone who works in digital marketing knows how fast things change. Even before the current panic over changes in Google search (search clicks decreasing as summary AI Overviews appear in the results more often), digital marketers were continually forced to adapt to changes in the algorithm. The same concept applies for marketing on Facebook, LinkedIn, or X. All of this has to be considered before marketers begin to evaluate changes in the competitive landscape. For example, look at how fast Temu went from nothing to a major ecommerce player – becoming the #2 most visited ecommerce website in the US by August 2024 – and how fast it retreated from the US market in the face of new tariff policies in the past few months, throttling back paid advertising so that it dropped to #11 in May.

Change is constant in marketing, and often it feels like a full-time job to stay on top of. Even with access to good data, marketers could use help sorting through it. This is where AI agents can assist. They take us beyond providing dashboards of data to providing analysis and making recommendations.

5.Looking ahead, what specific opportunities do you foresee in adopting and scaling AI Agent technologies across diverse departments within a large enterprise, and how do you plan to address them?

I love that question because the answer is: we’re just getting started imagining all the possibilities. The whole MarTech industry is going to shift to competing over who can deliver the best agents. And by best, I don’t mean the ones with the flashiest demos, I mean the ones that deliver practical results.

Our customers are just getting started sifting through all the AI-related pitches that are coming their way to determine what’s real and what’s smoke and mirrors. As they get more comfortable with this agent-driven generation of AI technology, they will be better equipped to ask for what will be useful to their organizations.

Don’t get me wrong, we’re already seeing strong demand and a pipeline of desirable AI agents that will keep us busy for a long time to come. I think the “scaling” limit is less about scaling technology than it is about learning to work with it productively. Enterprises will need to scale their leadership in AI adoption, educating employees on embracing their new AI team members and putting them to work productively.

At the same time, skepticism is entirely warranted. Every MarTech product is going to claim to be an AI product as part of the same old tech hype cycle. Tech buyers will need to sort out what’s real. It’s my job to ensure Similarweb comes down on the right side of that.

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David Abbey on Scaling Influencer Marketing with AI

artificial intelligence 30 Jul 2025

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

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

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

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

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

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

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

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

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

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

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

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

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

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Paul Stephens on Scalable, Secure Innovation: How Netwrix Is Redefining Identity-First Data Security

artificial intelligence 18 Jul 2025

1. What governance or leadership frameworks are in place to ensure cohesive decision-making across technology, product, IT, and security domains?

We’ve implemented several strategies that work in conjunction to ensure transparent and agile decision-making across Netwrix. At the strategic level, our Executive Steering Committee drives alignment across departments by setting direction, synchronizing roadmaps, managing budgets and facilitating timely decisions.

To reinforce focus and alignment, we’ve adopted the popular Objectives and Key Results (OKRs) framework across the organization. OKRs help ensure that all teams are aligned around shared business outcomes while allowing flexibility to address domain-specific priorities.

Security governance is embedded through our application security team, which is part of the CISO organization. This team partners closely with engineering and other stakeholders to assess and prioritize risks, as well as manage incident communication both internally and externally.

2. How do you plan to balance innovation with the rigorous demands of enterprise security and compliance?

To balance innovation with the demands of security and compliance, our engineering teams are adopting a proactive and integrated approach that involves key stakeholders from the very beginning of the software development lifecycle. Building capabilities and products on a secure foundation ensures long-term resilience and reduces the need for costly and complex retrofits later. It's always more efficient to construct a secure system from the ground up than to retrofit security into an existing one. Achieving this level of alignment requires fostering a culture of shared responsibility for all teams.

3. What is your approach to maintain platform agility and scalability as enterprise customer requirements continue to evolve?

My approach always begins with the customer. A clear understanding of their requirements is essential — if you’re not building something that directly addresses their needs and solves their problems, you don’t have a viable product.

Once the customer's needs are well understood, the product must be adaptable and flexible enough to evolve and support new capabilities. A well-designed architecture, built using an API-first approach, is a major enabler of this flexibility. When the system is modularized with clear contract boundaries, it becomes significantly easier and faster to extend functionality and maintain the solution over time.

The other piece of the puzzle is the delivery mechanism: Fast, reliable CI/CD pipelines with high levels of automation are critical. They empower teams to deliver quickly and with confidence, ensuring that innovation doesn’t come at the cost of stability.

4. How is AI being leveraged internally to enhance infrastructure performance, resilience, and service delivery across the business?

At Netwrix, we have integrated AI chatbot capabilities into our data security platform, Netwrix 1Secure, as well as into products like Netwrix Auditor. These enhancements help our customers strengthen their security posture and streamline workflows, enabling faster time to resolution.

We also recently launched a free, open-source MCP server that integrates with Netwrix Access Analyzer. It acts as a bridge between Netwrix products and external systems, facilitating seamless data exchange and analysis across platforms. By eliminating the complexity of system-to-system integration, it empowers both our customers and our internal teams to rapidly gain deep insights into data security and quickly identify and remediate risks.

Additionally, we are beginning to leverage AI within our infrastructure for tasks such as capacity planning. This is enabling us to better anticipate customer needs and detect patterns indicative of network issues.

5. How are technology leaders collaborating to accelerate time-to-market for new features while maintaining platform stability and security?

Our technology leaders are accelerating time-to-market by fostering close collaboration between engineering, security and operations teams, with a shared focus on building secure, stable solutions. A security-first mindset is essential, which means embedding practices like automated scanning and threat modeling early in the development lifecycle. To maintain velocity, we are investing in robust CI/CD pipelines that automate build, test and deployment processes, which reduces manual errors, increases release frequency, and ensures consistency. Through automation, cross-functional alignment and early risk mitigation, we are able to rapidly deliver new features without compromising platform stability or security.

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How Puntt AI Is Redefining Compliance: Ronnie Kwesi Coleman on Speed, Scale & AI

artificial intelligence 17 Jul 2025

1. In what ways has the integration of AI-powered compliance agents impacted the speed and cost-efficiency of your campaign approval and product launch processes?

The impact has been direct and measurable. AI-powered compliance agents have allowed us to reduce the average review time from days to minutes. That speed translates into faster launches, less rework, and ultimately lower operating costs.

More importantly for executives: this isn’t just about saving time, it’s about improving margins. By automating what was once a highly manual and cross-functional process, we’ve reduced headcount needs while increasing output. Faster execution leads to faster revenue realization, and we’ve turned a major bottleneck into a growth lever.

2. Given the dynamic nature of global regulations, how do you see Puntt AI evolving as a strategic tool for proactive risk mitigation and legal oversight within your organization?

Puntt AI is already a strategic safeguard. Regulations shift fast, especially across markets, and manual teams can’t keep up. Our platform continuously integrates global rule changes and brand-specific standards, giving executives visibility and control before issues arise.

We think of it as a proactive governance layer that scales with the business. It’s not just reducing risk; it’s enabling responsible growth into new markets by ensuring compliance is baked in, not bolted on.

3. How critical is Puntt AI’s ability to continuously learn from internal approvals and external regulatory shifts to maintaining real-time compliance and legal consistency?

It’s essential. The value of the system is in its ability to learn and adapt. We built Puntt to evolve with both the market and our organization.

This learning loop creates a compliance knowledge engine, so the system doesn’t just follow rules, it gets smarter with every review. That consistency builds confidence across marketing, legal, and executive teams, and eliminates surprises at launch.

4. In your view, how have you enabled your organization to enter new markets, knowing that creative and packaging meet local compliance requirements?

Global expansion used to require heavy investment in local compliance support. With Puntt, we’ve shifted that burden onto the platform itself.

We train the system to understand market-specific requirements, from legal language to cultural risk factors, so assets are reviewed and approved against local standards automatically. This gives us speed and certainty when entering new regions, without needing to rebuild our compliance process for each one.

It’s how we scale growth while protecting brand and regulatory integrity.

5. What metrics or KPIs are you using to evaluate the ROI of deploying AI compliance infrastructure, and how are those insights informing future investments in enterprise AI?

We focus on four core metrics:
• Time saved per asset review
• Reduction in rework and escalations
• Campaign turnaround time
• Global consistency and adoption across teams

But ultimately, we use one benchmark to tie it all together: cost to launch.

By tracking how AI reduces hours, delays, and downstream legal involvement, we see exactly how it improves our margins and speeds up revenue capture. That’s the clearest ROI signal for any executive, and it’s guiding where we invest next.

6. How do you envision the role of autonomous compliance systems evolving in the next 3–5 years, and what role will your organization play in shaping that future?

In 3 to 5 years, autonomous compliance will be as essential to marketing ops as automation is to finance or CRM to sales. Companies will need real-time systems to ensure content meets legal and brand standards, especially with the volume and speed AI introduces.

At Puntt, we’re not just preparing for that shift, we’re building the infrastructure now. Our belief is that speed to market and compliance shouldn’t be at odds. Executives shouldn’t have to choose between moving fast and staying protected.

That’s the future we’re shaping: operational speed without regulatory risk.

Get in touch with our MarTech Experts.

Ian Baer on Emotional AI: Redefining Predictive Marketing with Sooth’s 93% Advantage

artificial intelligence 16 Jul 2025

1. Given that traditional demographic and transactional data explain only a small fraction of buying behavior, how are you reassessing your current data frameworks to account for deeper emotional and situational drivers?

It’s really all of data-driven marketing that needs a reset right now. At the dawn of modern advertising, it wasn’t unusual for brands to see conversion rates of 20% or more — and repurchase rates near 50%. Today, when a brand achieves a 1–2% conversion rate, it’s cause for celebration. Retention is harder, too — meaning brands are often forced to re-win the same customers over and over again.

The reason is simple: nearly every brand is competing to optimize the same 7% of buyer data — demographics and past transactions. These signals have been the easiest to track for the last 100 years. They were stored in shoeboxes before computers existed, and despite all the advances in technology and data modeling, most strategic decisions are still based on those same commoditized signals.

But people leave clear behavioral trails that reveal their emotional priorities — and the practical and situational filters that shape real brand choices. That’s why we’ve shifted focus to analyze the remaining 93% — the part that predicts 13 out of every 14 decisions consumers actually make.

2. What role do you see predictive emotional AI playing in optimizing your media mix and reducing waste in campaign spending across multiple channels?

Marketing mix modeling and attribution analysis are just the latest attempts at a data-driven holy grail for brands. Ninety-seven percent of the Fortune 500 invest heavily each year in data intelligence to establish a unified set of truths from which all functions, including marketing, can operate. Simultaneously, due to poor targeting, ad fraud, and various inefficiencies, brands are wasting between 20% and 50% of their media budgets, putting the credibility of marketing itself at risk. That’s why Meta talks about making brand marketing obsolete with its AI ad model, and people are leaning in.

This situation has turned marketing into a competition focused on doing things cheaper and faster, because most have given up on improving effectiveness. Without better data governance and visibility, CMOs find themselves in a precarious position with their leadership and shareholders, which is a significant reason why one-third of Fortune 500 companies no longer employ a chief marketing officer. For business-to-business brands, it’s more than half. Marketing has become so enamored with vanity metrics that are divorced from business outcomes – such as page views and brand awareness – that it no longer speaks the same language as those with true spending power in most organizations. I recently heard from one of our clients in a business strategy role, “Marketing gets all this money but I truly have no idea where it goes.” As long as marketing budgets are seen as a black hole rather than a predictable investment, marketing teams will continue to lose their seats at the big table. When we clarify the 93% of buyer signals that truly drive behavior, we not only reduce waste — we re-establish marketing as a source of measurable profit.

3. How does scoring creative and messaging against emotional drivers in real-time, change your approach to creative development and campaign testing?

One consistent truth I’ve seen throughout my career is that when budgets tighten, brands often cut or eliminate their investment in research and measurement. Without clarity on what will work or why, brands tend to produce more assets and invest more in media. That reaction is understandable, especially when research, testing, and programmatic media are often seen as more costly than effective.

To meet this need, we developed a creative scoring engine that can ingest creative content in any format and deliver predictive intelligence across various audiences within one or two days. With predictive intelligence based on data-verified needs analysis, we can improve performance through emotional connection. We are enabling understanding as a central part of every creative and investment decision, without losing time or momentum.

4. How do you plan to evolve your understanding of high-value customer segments using behavioral audience analysis rather than legacy demographic assumptions?

One of the most overlooked metrics in modern marketing is share of requirement — the portion of a customer’s total category spend that a brand earns. While brands do a very good job of measuring and optimizing the business a customer already does with them, they have almost no visibility into the share of category spend each customer allocates to competing brands. What looks like a low-value customer to one bank may, in fact, be someone whose primary financial relationship is simply elsewhere, but they have no category visibility. Combine that with outdated demographic clustering and internal silos, and many brands end up flying blind — relying on the law of averages instead of verified emotional insights. The significant evolution in our model is category-wide visibility, so we can track shifts in real-time and recommend strategies to address the opportunities as well as the threats to their customer base. We have enhanced or replaced traditional research-based brand tracking with a much more dynamic and opportunistic model designed to steadily increase and protect each customer’s mind and wallet share.

5. How do you balance the demand for real-time decision-making with the need for accuracy and contextual relevance in your messaging and customer engagement?

A core part of our model has always been to keep insights both accessible and instantly actionable. Our model does not rely on demographic assumptions. Still, we output precise target profiles for media buying that incorporate both behavioral and demographic signals, fully compatible with existing targeting, delivery, and dynamic optimization platforms. This enables creative to be tailored in real time, around the emotional and situational drivers we know lead to profitable outcomes. It’s just one way our use of AI in insights and strategy dovetails seamlessly with emerging AI-driven media targeting and delivery. It’s truly hand-in-glove.

6. What challenges do you foresee in adopting AI-driven emotional prediction models at scale within your organization whether cultural, operational, or technological?

We knew we weren’t choosing the easy path when we told the marketing world it’s been focusing on the wrong inputs for decades. Another headwind we face is that when it comes to AI in marketing, the term has become shorthand for generative tools and workflow automation—solutions designed to make marketing faster, not necessarily better. Failing cheaper is still failing, and when a customer leaves because another brand met them more effectively in the moment, almost none return. This has put marketing on the clock across industries worldwide. That’s why we spend so much time with marketing leaders—not just sharing tools, but reshaping how they think about AI in marketing through a completely different lens—one that focuses on shifting 13 out of every 14 buying decisions, rather than the one that marketing has been obsessed with for a century. Where our approach flies in the face of traditional marketing and its outdated concept of “best practices,” our message is finally taking root. While the hardest work is ahead of us, I couldn’t be more excited about the challenges and opportunities that lie ahead.

Get in touch with our MarTech Experts.

Ankur Edkie on Scaling Ethical, Impactful Voice AI | Murf AI Interview with MarTech Edge

artificial intelligence 9 Jul 2025

Q.1) What frameworks do you follow to ensure your AI initiatives scale across global teams while remaining aligned with your core business strategy?

Murf AI helps global enterprises create high-quality voiceovers and dubs effortlessly. At Murf AI, we follow three key principles to ensure our AI initiatives scale globally across diverse industries.

1. Solving content challenges at scale

At Murf, we solve repeatable, high-impact content related challenges. Whether it’s large-scale content creation, enabling fast updates to voice content or simplifying localization, these are common problems faced by global teams across industries and our AI solutions across Studio, Dubbing and API are designed to solve these problems.

2. Centralized Platform for Decentralized Teams

Murf is built as a centralized platform that empowers globally distributed teams to collaborate with ease. Teams across regions can work together in real time; reviewing voiceovers, sharing feedback, and managing projects from a single interface. With access to a rich library of multilingual voices, users can create content that sounds local while staying on brand globally. This balance of centralized control and local flexibility enables consistent, scalable voice content across markets.

3. Enterprise-Grade Trust Framework

From consent-based voice creation and royalty-sharing with artists to compliance with global data regulations, our governance model is again built for scale. This allows enterprises to deploy Murf across regions confidently and responsibly.

Q.2) How are you incorporating AI-powered voice solutions to improve training, internal communications, or customer engagement?

AI voiceovers are not just a tool, but a transformative solution reshaping how organizations communicate internally and externally.

Learning & Development (L&D) teams are harnessing the power of Murf's platform to create more interactive training modules and boosting learner retention with professional voice narration in multiple languages.

Murf also makes it easy to update existing content. What once required a full re-recording can now be done seamlessly, keeping content current, compliant, and aligned with the regulatory demands of industries like pharma.

With fewer production bottlenecks, marketing teams can now create as much content as they want; not just as much as they can afford. From product explainers to radio spots, Murf voiceovers make it easy to produce multiple versions of a creative, enabling true hyper-personalization.

Q.3) What lessons can be drawn from cross-sector AI implementations that could inform your digital transformation roadmap?

At Murf AI, we’ve found that the most impactful AI implementations across industries share three core principles - they augment human capability, drive efficiency, and scale seamlessly.

1. Augment Human Capability

Our platform democratizes content creation, putting creators and builders firmly in control. With an intuitive interface, real-time feedback, and collaborative workflows, teams can craft exactly the voice output they need, without relying on technical or studio-heavy processes.

2. Drive Efficiency

By eliminating traditional, time-intensive workflows, Murf delivers up to 70% cost savings in voice production and enables content creation that’s up to 10x faster. This efficiency unlocks greater creativity and agility for marketing, L&D, and product teams alike.

3. Build for Scale

With a rich library of voices, support for multiple languages, and enterprise-grade compliance and security, Murf makes it easy for global organizations to scale voice content confidently and consistently across markets, teams, and platforms.

Q.4) What challenges have you faced when deploying voice AI solutions across different geographies, and how have you overcome them?

Voice AI needs to be linguistically and culturally aware. It's not just about translating language; it's about tone, pronunciation and intonation. We've invested heavily in creating diverse voice datasets, developing region-specific trained models, and engaging local talent in the development and QA process to address this. To further enhance pronunciation and accent accuracy, we’ve built a deep linguistic modeling layer that enables our speech model to reproduce the subtle nuances of each accent in multiple languages, achieving a pronunciation accuracy of 99.38%.

Q.5) What ethical considerations guide your decision-making when deploying AI technologies, especially those that generate human-like content?

At Murf, ethics is the foundation of everything we build.

We follow a clear, three-step approach:

Consent-first creation - Every Murf voice begins with the active involvement and agreement of the artist.
Full control - Artists have the freedom to withdraw consent at any time.
Royalties that grow - Artists earn royalties each time their voice avatar is used, and earnings grow with the avatar’s popularity.

We maintain strict voice cloning protocols, no voice is ever cloned without explicit permission. Our philosophy is simple: AI should augment, not replicate, and we seek to foster creativity while maintaining authenticity and trust.

Q.6) In your view, how can organizations better communicate the value of their AI initiatives to internal stakeholders, customers, and industry peers?

The key to communicating the value of AI initiatives is through storytelling with quantifiable impact. Over technical jargon or theoretical possibilities, organizations need to emphasize real use cases and how AI improved efficiency, lowered costs, or boosted customer experience. Internally, that means correlating AI initiatives to business KPIs and involving cross-functional champions early. At Murf, we frequently release client success stories and data-driven results to bridge the gap between AI innovation and business value.

Get in touch with our MarTech Experts.

Oliver Walker on Future-Ready Website Optimization | Hookflash Interview with MarTech Edge

artificial intelligence 8 Jul 2025

1. How can businesses balance user experience (UX), and conversion rate optimization (CRO) for maximum impact?

User experience and conversion rate optimisation (CRO) go hand-in-hand. One is the discipline user research, understanding user requirements and best practices around things like accessibility and design. The other is the mechanism, or process, to allow you to test different experiences against another. By utilising both together you should be able to drive better experiences on the website, with confidence in only deploying new features, components or functionality if you know they are going to positively impact your website. At the very least, you should expect the changes won’t harm them!

2. What are the most common website optimization mistakes, and how can businesses avoid them?

The most common mistake is not testing changes to a website. We’ve seen multiple pieces of research that has converged around a similar figure – the likes of Optimizely (80%) and Google (70%) have both found that the majority of changes don’t do a thing to improve engagement and conversion rates. And some of those will even make those metrics worse. It’s really important that you’re using data to understand challenges, but then also using data to validate that the changes you’ve made to address those challenges is a positive one.

3. How has the role of website optimization evolved with the rise of mobile-first and omnichannel experiences?

We will always be guided by data and as you would expect the majority of B2C sites now have more traffic on mobile sites. As a result, there’s a much heavier weighting towards website optimisation on mobile. For some B2B brands, we still see the majority of traffic on desktop, and as such, our research and efforts would pivot that way too. More broadly, we see an increase in, and advocate for, connecting experiences together from media or CRM to touchpoint to website. This “symmetrical messaging” has generated incredible results when we’ve deployed it and it’s as simple as targeting experiences based on the presence of a certain value in the landing page URL. For one client, we saw a 46% increase in conversion by tying the PPC ad to landing page experience more closely together.

4. What emerging technologies are set to redefine website optimization in the coming years?

AI is disrupting all areas of marketing and business, and website optimisation is no different. Most of our technology partners are embedding agents within their platforms, so you can either ask them to support with insight generation, suggestions for new web page layouts, or even to build out those experiences so that they can be tested. Likewise, those platforms are leaning into the analysis and categorisation of users into different buckets depending on “digital body language” to allow you to personalise experiences. For example, some users might be researching or just more conscientious in wanting to review more information. You should give those users a different experience to those you can infer are in buying mode or are more impulsive and want to simply get through the purchase process as quickly as possible.

5. How can businesses prepare for the future of privacy-first digital marketing while optimizing their web presence?

This is a two-fold answer. The first is ensuring that you maximise the data you’re able to collect whilst respecting user privacy and the relevant legislation in your country. The second is ensuring you’re using your collected data to fill in the gaps on those users and sessions where you couldn’t collect their data. On the first point, there’s a multitude of steps you can take to ensure you’re best able to measure and optimise your marketing and website. From looking at server-side set-ups to very specific solutions like Google Tag Gateway, they help to mitigate some of the solutions that block tracking as a by-product of blocking ads. Likewise, collecting first-party data and sharing that back to the media platforms to allow for ad to conversion matching (amongst other things) helps increase the amount of data these platforms have to use in their algorithm. On the second point, modelling is a critical component in helping to optimise in a privacy-first way. Whether you’re using Google’s own Advanced Consent Mode – which tracks users who reject cookies in a cookieless way and utilises modelling off the users who accepted cookies to fill in the gaps – or you’re doing your own modelling, it’s a natural step to take to ensure we’re working with as much as we can.

Get in touch with our MarTech Experts.

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