DEV Community: Mark Petays

ABM vs. Traditional Lead Generation: Which Strategy Is Better?

Mark Petays — Tue, 15 Sep 2026 05:14:23 +0000

In B2B marketing, generating qualified leads is essential for building a strong sales pipeline. Traditionally, businesses have relied on broad lead-generation strategies to attract as many potential customers as possible. However, Account-Based Marketing (ABM) has emerged as a more targeted approach, focusing marketing and sales efforts on specific high-value accounts.

Both strategies can deliver results, but they work differently. Understanding the differences between ABM and traditional lead generation can help businesses choose the right approach for their growth objectives.

What Is Traditional Lead Generation?
Traditional lead generation focuses on attracting a large pool of potential prospects and then identifying which leads are most likely to become customers. Marketers typically use channels such as search engine optimization, paid advertising, content marketing, webinars, email campaigns, social media, and gated content.

The process generally follows a funnel model:

Attract → Capture → Nurture → Qualify → Convert

The primary objective is to generate a consistent flow of leads. Sales teams then evaluate these prospects based on factors such as engagement, company information, budget, and buying intent.

This approach can work particularly well for businesses with broad target markets and products that appeal to many types of customers.

What Is Account-Based Marketing?
ABM takes a different approach. Instead of trying to attract a large number of prospects, businesses identify specific companies that closely match their ideal customer profile (ICP).

Marketing and sales teams then collaborate to create personalized campaigns for those target accounts. These campaigns may include customized content, account-specific advertising, personalized email outreach, executive engagement, and targeted events.

The ABM process is more focused:

Identify Accounts → Research → Engage → Personalize → Measure → Expand

Rather than measuring success primarily through lead volume, ABM often emphasizes account engagement, pipeline contribution, conversion rates, deal size, and customer revenue.

Key Differences Between ABM and Traditional Lead Generation
The biggest difference is focus. Traditional lead generation aims to attract many potential customers, while ABM concentrates resources on accounts with the highest potential value.

Traditional lead generation generally prioritizes lead volume, whereas ABM prioritizes account quality and relevance.

Personalization is another major difference. Traditional campaigns may use audience segments to deliver relevant messaging, while ABM enables marketers to develop highly specific messaging based on an individual account's business needs, industry, challenges, and buying stage.

The sales and marketing relationship also differs. Traditional lead generation can involve marketing generating leads and sales following up with qualified prospects. ABM requires closer alignment because both teams work together to identify target accounts, develop engagement strategies, and influence buying groups.

Which Strategy Produces Better Results?
There is no universal winner. The better strategy depends on the company's goals, sales cycle, market, and available resources.

Traditional lead generation may be the better choice when a company needs to build awareness, reach a broad audience, or generate a large volume of prospects. It can also provide an effective foundation for businesses with scalable products and shorter sales cycles.

ABM can be more effective when a business sells high-value products or services to a defined group of companies. It is particularly useful for organizations with long sales cycles, multiple decision-makers, and complex B2B purchasing processes.

For example, a technology provider selling an enterprise platform to a small number of large organizations may benefit more from ABM than from generating thousands of general leads.

Can Businesses Use Both Strategies?
Absolutely. Many successful B2B organizations combine the two approaches.

Traditional lead generation can be used to attract and educate a broad audience, while ABM can be used to identify and engage the highest-value accounts within that audience.

This creates a balanced strategy: traditional lead generation builds reach, while ABM creates deeper engagement with priority accounts.

Final Thoughts
The debate between ABM vs. traditional lead generation should not simply focus on which strategy is better. Instead, businesses should consider which approach best matches their target market and revenue objectives.

Traditional lead generation is valuable for scale and audience expansion, while ABM provides precision, personalization, and stronger alignment between marketing and sales. For many B2B companies, combining both strategies can create a more effective and sustainable revenue-generation engine.

AI-Powered Medical Imaging: What's Next?

Mark Petays — Tue, 15 Sep 2026 05:01:06 +0000

Artificial intelligence is rapidly transforming medical imaging, changing how healthcare professionals detect, analyze, and monitor diseases. From radiology and cardiology to oncology and pathology, AI-powered imaging tools are helping clinicians process large volumes of medical images and identify patterns that may be difficult to detect manually. As these technologies continue to mature, the next phase of medical imaging is expected to focus on greater accuracy, faster diagnosis, personalized care, and deeper integration into clinical workflows.

The Evolution of AI in Medical Imaging
Traditional medical imaging depends heavily on specialists interpreting X-rays, CT scans, MRIs, ultrasounds, and other diagnostic images. While expert interpretation remains essential, the growing volume of imaging studies has created challenges for healthcare systems.

AI can support clinicians by analyzing images rapidly, highlighting potentially abnormal areas, and prioritizing cases that may require urgent attention. Machine learning and deep learning models can recognize patterns associated with conditions such as tumors, fractures, cardiovascular abnormalities, and neurological disorders.

The technology is increasingly moving beyond simple image detection. Modern AI systems can combine imaging data with patient history, laboratory results, and other clinical information to provide a more comprehensive view of a patient's condition.

What's Next for AI-Powered Medical Imaging?
The future of AI-powered medical imaging will likely be shaped by several major developments.

More Personalized Diagnosis AI could help physicians move toward individualized diagnosis and treatment. Instead of evaluating an image in isolation, future systems may analyze imaging characteristics alongside a patient's age, medical history, genetics, medications, and previous scans.

This could help clinicians understand how a disease is progressing and identify treatment approaches that are more appropriate for individual patients.

Earlier Disease Detection One of AI's most important opportunities is identifying diseases at earlier stages. Advanced algorithms may detect subtle changes that are difficult to recognize during routine examination.

Early identification could be particularly valuable in areas such as cancer, cardiovascular disease, and neurological conditions, where treatment outcomes can depend significantly on how early a condition is discovered.

AI-Assisted Radiology Workflows AI is also expected to become more deeply integrated into radiology departments. Rather than replacing radiologists, AI tools can act as clinical assistants by automating repetitive tasks, organizing workloads, identifying urgent examinations, and generating preliminary findings.

This could allow specialists to spend more time on complex cases and direct patient care while reducing administrative and repetitive workloads.

Multimodal Medical AI The next generation of systems will increasingly combine different types of healthcare information. Medical images could be analyzed together with electronic health records, pathology results, clinical notes, and laboratory data.

Multimodal AI could provide clinicians with a broader clinical context and potentially improve diagnostic decision-making.

Greater Transparency and Trust As AI becomes more influential in healthcare, transparency will become increasingly important. Clinicians need to understand why an AI system identifies a particular image as suspicious.

Explainable AI could help physicians review the evidence behind an algorithm's recommendation rather than treating its output as an unquestionable answer. Validation, regulatory oversight, data quality, and continuous monitoring will also remain essential.

Challenges Ahead
Despite its potential, AI-powered medical imaging faces important challenges. Algorithms can produce inaccurate results when trained on limited or unrepresentative datasets. Differences in imaging equipment, patient populations, and clinical environments can also affect performance.

Privacy and security are additional concerns because medical imaging contains sensitive patient information. Healthcare organizations will need strong data governance and cybersecurity practices as AI systems become more connected to clinical infrastructure.

Human oversight will remain critical. AI should support medical professionals rather than eliminate the need for clinical judgment.

Conclusion
AI-powered medical imaging is moving toward a future where intelligent systems work alongside healthcare professionals to make diagnosis faster, more precise, and increasingly personalized. The next generation of technology will likely combine advanced image analysis with broader clinical data, support earlier disease detection, and become embedded within everyday healthcare workflows.

The biggest opportunity is not simply to automate image interpretation, but to create intelligent clinical tools that help physicians make better-informed decisions. With responsible development, rigorous validation, strong privacy protections, and continued human oversight, AI could become an important part of the next era of medical imaging.

First-Party Data in the Age of AI

Mark Petays — Tue, 15 Sep 2026 04:50:43 +0000

As artificial intelligence (AI) reshapes digital marketing, first-party data has become one of the most valuable assets for businesses. With third-party cookies declining, privacy regulations expanding, and customers demanding greater transparency, companies are increasingly turning to data collected directly from their own audiences. When combined with AI, first-party data can help marketers create more relevant experiences, improve targeting, and make smarter business decisions.

What Is First-Party Data?
First-party data is information a company collects directly from its customers and prospects through interactions with its own digital properties and channels. This can include website behavior, purchase history, CRM records, email engagement, form submissions, product usage, webinar registrations, customer-service interactions, and account activity.

Unlike third-party data, first-party data comes directly from the relationship between a business and its audience. This makes it particularly valuable for organizations looking to build accurate customer profiles while maintaining greater control over how information is collected and used.

Why First-Party Data Matters for AI
AI systems require reliable data to generate useful insights. The quality of an AI model's output depends heavily on the quality and relevance of the information it receives. First-party data can provide businesses with a detailed understanding of their actual customers rather than relying on broad assumptions based on external audience segments.

AI can analyze large volumes of first-party data to identify patterns that may be difficult for marketing teams to discover manually. For example, an AI system can detect which content prospects consume before requesting a sales conversation, identify common characteristics among high-value accounts, or predict when an existing customer may be ready for an upsell.

This combination can make marketing more data-driven and responsive.

AI-Powered Personalization
One of the biggest opportunities is personalization. Marketers can use AI to analyze customer behavior and deliver content, offers, and recommendations based on individual interests and engagement.

For B2B organizations, this can support account-based marketing (ABM) by helping teams understand account-level engagement. AI can identify which companies are showing increasing interest in specific topics, products, or solutions and help marketers prioritize those accounts.

Instead of delivering the same message to an entire audience, companies can create more relevant experiences based on real customer signals.

Improving Customer Segmentation
Traditional segmentation often depends on demographic information or predefined categories. AI can make segmentation more dynamic by analyzing behavioral signals across multiple customer touchpoints.

For example, AI could identify groups of customers who frequently engage with specific content, have similar purchasing behaviors, or demonstrate comparable levels of product adoption. These insights can help marketing teams develop more precise campaigns and improve audience targeting.

Privacy and Responsible Data Use
The growing importance of first-party data does not eliminate privacy concerns. Businesses still need to collect, store, and process customer information responsibly.

Clear consent practices, transparent privacy policies, strong data security, and appropriate governance are essential. Companies should also avoid collecting information simply because it is technically possible. Data strategies should focus on information that provides genuine value to customers and the business.

Responsible AI practices are equally important. Organizations need to understand how AI systems use customer data and ensure that automated decisions do not create unfair or misleading outcomes.

The Future of First-Party Data
The future of marketing will increasingly depend on the ability to connect trusted customer data with intelligent technology. Businesses that establish strong first-party data foundations can give AI systems better information for forecasting, personalization, segmentation, and customer engagement.

Rather than treating first-party data simply as a replacement for third-party cookies, marketers should view it as a strategic business asset. By combining high-quality customer information with AI-driven analytics, organizations can develop deeper customer relationships while creating more relevant and measurable marketing experiences.

Open Banking in the Age of AI

Mark Petays — Tue, 15 Sep 2026 04:42:05 +0000

Open banking is entering a new phase as artificial intelligence (AI) transforms how financial data is accessed, analyzed, and used. By enabling customers to securely share financial information with authorized third-party providers through application programming interfaces (APIs), open banking has already changed the relationship between banks, fintech companies, and consumers. The integration of AI is now creating new opportunities for smarter financial services, personalized experiences, and automated decision-making.

How AI Is Transforming Open Banking
Traditional open banking primarily focused on data connectivity. Customers could authorize financial institutions and fintech applications to access information such as account balances, transactions, and payment details. AI adds an intelligence layer to this connected ecosystem.

Machine learning models can analyze large volumes of financial data to identify patterns, understand spending behavior, and generate actionable insights. For example, AI-powered financial applications can categorize transactions automatically, identify unusual spending, forecast cash flow, and provide personalized recommendations.

For businesses, AI can analyze banking and transaction data to improve financial forecasting, automate reconciliation, assess customer behavior, and support more informed lending and credit decisions.

Personalized Financial Experiences
One of the biggest opportunities created by AI-powered open banking is personalization. Instead of providing customers with basic account information, financial platforms can use authorized data to understand individual financial circumstances.

AI can identify recurring expenses, income patterns, savings opportunities, and changes in spending behavior. Based on these insights, applications can recommend ways to manage budgets, reduce unnecessary expenses, or improve savings.

This could move financial services from reactive reporting toward proactive financial guidance. Customers may increasingly receive relevant recommendations before a financial problem develops rather than after it occurs.

Improving Fraud Detection and Security
The combination of open banking and AI can also strengthen financial security. Open banking creates multiple connections between financial institutions and third-party providers, making effective monitoring and authentication increasingly important.

AI systems can process transaction data in real time and identify anomalies that may indicate fraud. Unusual payment amounts, unfamiliar transaction patterns, or unexpected account activity can trigger additional verification.

However, AI also introduces new risks. Financial institutions must ensure that models are reliable, transparent, and resistant to manipulation. Strong authentication, encryption, API security, consent management, and continuous monitoring remain essential.

AI-Powered Lending and Financial Decision-Making
Open banking data can provide lenders with a broader view of a customer's financial behavior. When combined with AI, this information can support more sophisticated credit assessments.

Instead of relying solely on traditional credit histories, lenders may analyze cash flow, income consistency, payment behavior, and other permitted financial indicators. This can potentially help financial institutions make faster and more informed decisions.

At the same time, responsible use of AI is critical. Financial institutions need safeguards against biased algorithms, inappropriate data use, and decisions that customers cannot understand or challenge.

The Importance of Data Privacy and Trust
The success of AI-powered open banking will ultimately depend on consumer trust. Customers need to understand what information is being shared, why it is being used, and which organizations can access it.

Clear consent mechanisms and transparent data policies will become increasingly important. Financial institutions and fintech companies must also comply with applicable privacy, security, and financial regulations while ensuring that customers retain meaningful control over their data.

The Future of Open Banking
AI is likely to make open banking more intelligent, automated, and personalized. As financial institutions and fintech companies develop new AI-powered services, open banking could evolve from a data-sharing framework into an intelligent financial ecosystem.

The organizations that combine secure data connectivity with responsible AI, strong privacy protections, and useful customer experiences will be better positioned to compete in the next generation of financial services.

Ultimately, the future of open banking will not depend on data access alone. The real opportunity lies in turning connected financial data into secure, intelligent, and meaningful financial experiences.

Onetag Appoints Peter Wallace as CRO to Accelerate Global Growth

Mark Petays — Tue, 15 Sep 2026 04:27:44 +0000

Onetag, the next-generation global exchange, curation platform, and creative intelligence company, announced the appointment of Peter Wallace as Chief Revenue Officer (CRO).

Wallace will lead Onetag’s global commercial strategy as the company enters its next phase of international expansion.

Peter Wallace Brings Global Commercial Experience
Wallace brings more than 15 years of leadership experience in commercial strategy, enterprise sales, and global business expansion.

His work has particularly focused on:

Contextual advertising
Attention
Data
Technology-led approaches to digital media
Wallace joins Onetag from GumGum, where he served as General Manager for EMEA and JAPAC and played a key role in driving the company’s growth beyond the United States through organic expansion and acquisitions.

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His previous experience also includes leading digital specialists at Total Media, a behavioral planning agency, and at audience data platform Eyeota, where he drove a fourfold increase in revenue.

Leading Onetag’s Global Commercial Strategy
At Onetag, Wallace will oversee commercial development across demand, partnerships, and supply.

His responsibilities will focus on:

Driving revenue growth
Deepening relationships with publishers
Expanding relationships with agencies and advertisers
Strengthening partnerships with technology platforms
Expanding Onetag’s global footprint
“Peter’s proven track record of scaling businesses internationally makes him a fantastic fit for Onetag,” said Filippo Gramigna, co-CEO of Onetag. “As we step up our commercial ambition, his leadership and understanding of shifting, modern ad tech will be instrumental in deepening our partner relationships as we continue to innovate and deliver value across the digital advertising ecosystem.”

Palantir and Fujitsu Deepen Partnership to Advance Enterprise AI Transformation

Mark Petays — Tue, 15 Sep 2026 04:07:14 +0000

Palantir Technologies Inc. and Fujitsu Limited announced the renewal of their strategic partnership, with a deeper investment in the capabilities needed to help enterprises in Japan and around the world.

In connection with the partnership, Fujitsu has signed a new agreement with Palantir Technologies Japan KK for Palantir AIP and Palantir Foundry and will serve as a Global FDE Partner.

As demand for sovereignty grows, enterprises need greater control over their data, models, infrastructure and operations. Palantir platforms connect AI models to governed data, Ontology-powered workflows, access controls, auditing and customer-controlled deployment environments, helping Palantir and Fujitsu deliver trusted AI applications.

Fujitsu Deploys Supply Chain Resilience Solution
Fujitsu implemented a supply chain resilience solution for a leading Japanese manufacturer using the Palantir platform.

The solution integrated data across:

More than 3,000 suppliers
18 factories
Previously siloed enterprise systems
The implementation was completed without disrupting operations.

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By combining Palantir’s platform capabilities with Fujitsu’s domain expertise and AI technologies, the customer achieved:

More than $10 million in cost savings within one year.
Doubled operational productivity.
Significantly accelerated disruption response and decision-making.
Fujitsu Expands Forward Deployed Engineering Capabilities
Fujitsu is making a significant investment in building Forward Deployed Engineering (FDE) capabilities with Palantir for customers in Japan and around the world.

Fujitsu will bring together its own AI technologies, including the Takane large language model, extensive industry knowledge, Uvance offerings and experienced FDE Palantir professionals.

These capabilities will support customers adopting Foundry and AIP and help them develop custom AI applications within sovereign, production-grade architectures.

“This partnership is about bringing Palantir’s sovereign AI architecture to Fujitsu’s customers across Japan. Fujitsu is investing in the deployed engineering capabilities needed to help enterprises build custom AI applications and models on AIP and Foundry, grounded in their own data, workflows, and governed within their own operational environments.”

— Kevin Kawasaki, Global Head of Business Development at Palantir Technologies

How Intent Data Helps Identify Ready-to-Buy B2B Prospects

Mark Petays — Fri, 11 Sep 2026 05:25:20 +0000

In B2B marketing, reaching the right prospects at the right time can make a significant difference in conversion rates. Many potential customers may fit a company's ideal customer profile (ICP), but that does not necessarily mean they are ready to purchase. Intent data helps marketing and sales teams identify organizations that are actively researching a product, service, or solution, allowing businesses to prioritize prospects that show stronger buying signals.

What Is B2B Intent Data?
B2B intent data refers to information that indicates a company or individual is showing interest in a particular topic, product category, or business solution. These signals can come from activities such as website visits, content consumption, keyword searches, product research, webinar participation, and engagement with industry-related content.

Instead of relying only on demographic or firmographic information, marketers can use intent data to understand what prospects are interested in and how actively they are researching it.

Identifying Prospects Showing Buying Signals
One of the biggest advantages of intent data is its ability to reveal potential buyers before they directly contact a company. A prospect may repeatedly search for information about a specific technology, visit relevant product pages, download comparison guides, or engage with educational content.

When several of these activities occur within a short period, they can indicate increased purchase intent. Marketing teams can use these signals to identify accounts that deserve greater attention.

For example, a company researching "B2B account-based marketing platforms," comparing vendors, and downloading ABM resources may be further along in the buying journey than an organization simply reading a general marketing article.

Improving Account Prioritization
Intent data can help sales and marketing teams prioritize accounts instead of treating every lead equally. Businesses can combine intent signals with their ICP criteria, including company size, industry, location, revenue, and technology environment.

An account that matches the ICP and demonstrates strong intent can receive a higher priority than an ICP-fit account with little or no recent engagement.

This approach helps teams focus resources on prospects that have both business fit and demonstrated interest.

Enabling More Relevant Engagement
Intent data also supports more personalized B2B marketing. When marketers understand the topics an account is researching, they can deliver content and messaging that aligns with those interests.

For example, if an account is showing increased interest in sales automation, a company could provide relevant case studies, product information, expert insights, or educational content related to sales automation rather than sending generic promotional messages.

More relevant engagement can help businesses build relationships while prospects are still researching potential solutions.

Connecting Marketing and Sales
Intent data can create stronger alignment between marketing and sales teams. Marketing can identify accounts displaying meaningful buying signals and share those insights with sales representatives.

Sales teams can then approach prospects with greater context about their potential interests and research activity. This can make outreach more timely and relevant while reducing the need for completely cold prospecting.

Using Intent Data Effectively
Intent data should not be treated as proof that a prospect is ready to buy. A company may research a topic for many reasons, including general education, market research, or competitive analysis.

The most effective strategy is to combine intent signals with other data, such as first-party website engagement, CRM information, firmographic data, and direct interactions.

Businesses should also establish clear intent thresholds so sales teams know when an account has demonstrated enough activity to warrant outreach.

Conclusion
Intent data gives B2B organizations greater visibility into prospect behavior and emerging buying interest. By identifying accounts that are actively researching relevant solutions, companies can improve account prioritization, personalize engagement, and create better coordination between marketing and sales.

When combined with ICP data and other buyer signals, intent data can help businesses move beyond simply finding potential prospects toward identifying which prospects are more likely to be ready for a meaningful sales conversation.

Healthcare AI Governance: Balancing Innovation and Patient Safety

Mark Petays — Fri, 11 Sep 2026 05:15:15 +0000

Artificial intelligence (AI) is rapidly transforming healthcare, from assisting clinicians with diagnosis to improving drug discovery, patient monitoring, medical imaging, and administrative operations. However, as healthcare organizations increasingly integrate AI into critical workflows, the need for strong healthcare AI governance has become essential. Effective governance can help organizations capture the benefits of AI while protecting patient safety, privacy, and trust.

The Growing Role of AI in Healthcare
AI technologies are being deployed across healthcare systems to analyze large volumes of medical data and support faster, more informed decisions. Machine learning models can help identify patterns in medical images, predict potential health risks, personalize treatment recommendations, and automate repetitive administrative tasks.

Generative AI is also creating new opportunities. Healthcare professionals can use AI tools to summarize clinical information, support documentation, answer questions, and improve communication with patients. These applications can reduce workloads and potentially allow clinicians to spend more time on direct patient care.

However, healthcare differs from many other industries because AI-generated errors can have serious consequences. An inaccurate recommendation, biased prediction, or incorrect interpretation of patient information could directly affect clinical decisions.

Why AI Governance Matters
Healthcare AI governance refers to the policies, processes, controls, and responsibilities organizations establish to ensure that AI systems are developed and used safely and responsibly.

A comprehensive governance framework should address the entire AI lifecycle, from selecting and developing a model to deploying, monitoring, updating, and eventually retiring it. Organizations need clear accountability for determining when AI can be used independently and when human oversight is required.

Governance is particularly important for evaluating AI models for accuracy, reliability, fairness, security, and transparency. Healthcare providers should understand how an AI system performs across different patient populations and clinical situations rather than relying solely on overall performance statistics.

Balancing Innovation With Patient Safety
One of the biggest challenges for healthcare leaders is finding the right balance between innovation and risk management. Excessive restrictions can slow the adoption of potentially valuable technologies, while insufficient oversight can expose patients and healthcare organizations to significant risks.

Human oversight should remain a central component of high-impact healthcare AI applications. AI should generally support clinical professionals rather than replace appropriate medical judgment. Clinicians need mechanisms to review AI-generated recommendations, identify potential errors, and override automated outputs when necessary.

Continuous monitoring is equally important. An AI model that performs effectively during initial testing may become less accurate when patient populations, clinical practices, or underlying data change. Healthcare organizations should therefore establish processes for monitoring model performance, detecting unexpected outcomes, and responding quickly to identified problems.

Protecting Privacy and Addressing Bias
Patient data is one of the most valuable resources for developing healthcare AI, but it also introduces substantial privacy responsibilities. Governance programs should include strong controls for data access, security, consent, and appropriate data use.

Bias is another critical concern. If training data does not adequately represent different demographic and clinical populations, an AI system may produce less reliable results for certain groups. Regular testing and evaluation can help identify disparities and improve model performance.

Building Trust in Healthcare AI
Successful AI adoption ultimately depends on trust. Patients and healthcare professionals need confidence that AI systems are being used responsibly and that safety remains the priority.

Healthcare organizations can strengthen trust by establishing transparent AI policies, documenting how systems are evaluated, educating staff, and communicating appropriately with patients about AI-assisted care. Governance should not be viewed simply as a compliance requirement. It should become an ongoing organizational process that supports responsible innovation.

Conclusion
Healthcare AI has the potential to improve clinical decision-making, operational efficiency, and patient outcomes, but innovation must be accompanied by strong safeguards. Effective healthcare AI governance provides a framework for managing risks related to accuracy, privacy, bias, security, and accountability.

As AI adoption accelerates, organizations that combine technological innovation with continuous monitoring, human oversight, transparent policies, and patient-centered safeguards will be better positioned to realize AI's benefits without compromising patient safety. The future of healthcare AI will depend not only on what these technologies can do, but also on how responsibly they are governed.

Generative Engine Optimization: How Brands Can Become Visible in AI Search

Mark Petays — Fri, 11 Sep 2026 05:06:40 +0000

Search is changing rapidly. Consumers and business decision-makers are increasingly using AI-powered search tools to discover information, compare solutions, research vendors, and answer complex questions. Platforms powered by generative AI can provide direct, summarized answers instead of simply displaying a list of traditional search results.

This shift has created a new opportunity for marketers: Generative Engine Optimization (GEO). GEO focuses on improving a brand's visibility and likelihood of being mentioned, cited, or recommended within AI-generated search responses.

What Is Generative Engine Optimization?
Generative Engine Optimization is the process of creating and optimizing content so that AI-powered search engines and answer engines can better understand, trust, and reference a brand's information.

Unlike traditional SEO, which primarily focuses on ranking webpages for specific keywords, GEO focuses on brand visibility within AI-generated answers. AI systems evaluate information from multiple sources to construct responses, making factors such as relevance, authority, factual accuracy, context, and content structure increasingly important.

For brands, this means optimizing not only for search rankings but also for how their expertise and information can be interpreted by AI systems.

Why GEO Matters for Brands
AI search is changing how users discover businesses. Instead of searching for "best ABM platforms" and reviewing multiple websites, a buyer may ask an AI tool to recommend the best ABM platforms for an enterprise organization.

The resulting answer may mention only a handful of companies.

This creates a new visibility challenge. A brand can rank well in traditional search while receiving limited visibility in AI-generated responses. GEO helps businesses address this emerging search environment by building content that clearly demonstrates expertise and provides useful, reliable information.

How Brands Can Improve AI Search Visibility

Create High-Quality, Answer-Focused Content Brands should develop content that directly answers the questions their target audiences ask. Detailed guides, industry analysis, research reports, FAQs, comparisons, and expert commentary can provide valuable information for both users and AI systems.

Content should be clear, factual, well-organized, and supported by credible information.

Demonstrate Industry Expertise AI systems need signals that indicate whether information is trustworthy. Brands can strengthen their authority by publishing original research, expert interviews, case studies, statistics, whitepapers, and thought leadership.

Consistent coverage of a specific subject can also help establish topical authority.

Strengthen Brand Mentions Across the Web AI search visibility does not depend entirely on a brand's own website. Mentions from reputable publications, industry websites, research organizations, podcasts, interviews, and other authoritative sources can contribute to a brand's broader digital presence.

Building genuine digital authority through relevant PR, partnerships, expert contributions, and high-quality backlinks can therefore support GEO efforts.

Structure Content for Easy Understanding Well-structured content is important in an AI-driven search environment. Marketers should use descriptive headings, concise paragraphs, lists, tables where appropriate, FAQs, and clearly defined concepts.

The goal is to make information easy for both people and machines to understand.

Optimize for Questions and Conversational Search Users increasingly ask complete questions rather than entering short keywords. Brands should create content around conversational queries such as "How does account-based marketing improve B2B pipeline?" or "What are the benefits of AI-powered demand generation?"

Answering these questions comprehensively can increase opportunities for visibility across AI-driven search experiences.

The Future of Search Is Multichannel
Generative Engine Optimization does not replace traditional SEO. Instead, it expands the search optimization strategy.

Brands should continue investing in technical SEO, keyword research, internal linking, quality backlinks, structured data, and useful content while also considering how their information appears across AI-powered search platforms.

The brands most likely to succeed will be those that consistently provide accurate, authoritative, original, and genuinely useful information.

As AI search continues to evolve, GEO will become an increasingly important part of digital marketing. Brands that begin building authority and creating answer-focused content today can position themselves for greater visibility in the next generation of search.

Embedded Finance: The Next Phase of Financial Services

Mark Petays — Fri, 11 Sep 2026 04:52:18 +0000

The financial services industry is entering a new phase of digital transformation as embedded finance becomes increasingly integrated into everyday products, platforms, and customer experiences. Rather than requiring consumers and businesses to visit a traditional financial institution or standalone banking application, embedded finance brings financial services directly into the platforms they already use.

From digital wallets and payment options to lending, insurance, and banking services, embedded finance is changing how financial products are delivered, accessed, and consumed.

What Is Embedded Finance?
Embedded finance refers to the integration of financial products and services into non-financial platforms, applications, and digital ecosystems. A retail marketplace, for example, can offer customers instant payments, financing, insurance, or a digital wallet without requiring them to leave the platform.

This model is enabled by technologies such as APIs, cloud infrastructure, open banking, artificial intelligence, and banking-as-a-service platforms. These technologies allow businesses to connect with financial infrastructure and provide targeted financial services as part of their existing customer journeys.

Why Embedded Finance Is Growing
One of the biggest advantages of embedded finance is convenience. Customers increasingly expect fast and seamless digital experiences. When payment, lending, insurance, or other financial services are integrated directly into a platform, users can complete transactions without switching between applications.

Businesses also benefit from new revenue opportunities. By embedding financial products into their platforms, non-financial companies can generate additional revenue through transaction fees, lending, subscriptions, or partnerships with financial institutions.

For financial service providers, embedded finance creates new distribution channels and access to highly targeted customer segments. Instead of relying exclusively on branches, websites, or banking applications, financial institutions can reach customers through platforms where financial decisions are already being made.

Key Areas of Embedded Finance
Embedded payments remain one of the most established applications. Businesses can integrate payment capabilities directly into websites, marketplaces, mobile applications, and point-of-sale systems.

Embedded lending is also expanding rapidly. E-commerce platforms and business software providers can use transaction and customer data to offer financing or working-capital solutions at the point of need.

Embedded insurance allows customers to purchase insurance as part of another transaction. For example, travel platforms can offer travel protection during the booking process, while automotive platforms can integrate insurance into vehicle purchases.

Embedded banking goes further by allowing businesses to offer accounts, cards, money management, and other banking capabilities within their own ecosystems.

The Role of AI and Data
Artificial intelligence is expected to make embedded finance increasingly personalized. AI can analyze transaction patterns, customer behavior, and other data to identify relevant financial products and deliver them at the right moment.

For example, a business platform could identify when a small company may need additional working capital and present a financing option within its existing workflow. Personalized recommendations can improve customer experiences while helping providers manage risk more effectively.

However, increased use of financial and behavioral data also creates challenges around privacy, cybersecurity, transparency, and regulatory compliance. Companies entering embedded finance must establish strong controls for data protection and responsible use of AI.

What Comes Next?
The next phase of embedded finance will likely move beyond individual financial products toward complete financial ecosystems. Businesses may increasingly combine payments, lending, insurance, banking, and financial management into unified digital experiences.

As technology continues to remove the boundaries between financial and non-financial services, embedded finance could become an invisible layer supporting everyday commerce.

Ultimately, the future of financial services may not be defined by where customers go to access financial products, but by how seamlessly those products become part of the experiences they already use.

Stravito Launches AI Persona Builder for Teams

Mark Petays — Fri, 11 Sep 2026 04:32:12 +0000

Stravito, the customer intelligence platform helping global brands understand their customers, validate ideas, and make better decisions, introduced AI Persona Builder, a tool that turns a company’s own research into bespoke, interactive AI personas in minutes, with every claim traceable to the research behind it.

Marketing and insights teams can interview the personas and test campaigns, products, and strategic ideas against them, with the confidence that every claim can be sourced and checked to inform commercial decisions.

Built on Stravito’s Glass Box AI principles for transparent and explainable AI, AI Persona Builder answers the question, “How can I know that I can trust the AI persona I’ve created?”

Unlike generic AI or simulated customer intelligence tools that build personas from unknown web data, Stravito AI Persona Builder creates AI personas from a company’s own research and shows its work: each trait is scored against that research, linked to its exact source, and flagged as contested when sources disagree.

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Where the research doesn’t cover something, the persona says so rather than inventing an answer.

“As AI personas become more prevalent across marketing and insights teams, companies need to know whether they can rely on them. That’s why we built AI Persona Builder to be a glass box, not a black box: you can see the research behind every answer, how strongly it’s backed, and where it leaves room for interpretation. That transparency is what turns an AI persona into a decision-support resource teams can actually trust, whether they’re briefing creatives, pressure-testing packaging redesigns, or planning a new launch.”

— Thor Olof Philogène, Founder and CEO of Stravito

Because the evidence is always visible, AI Persona Builder works wherever a company’s research starts.

A full segmentation study can generate a richly evidenced persona in minutes, while a smaller number of reports, interviews, collections and videos can still create a useful persona, honest about where it is inferring.

In every case, AI Persona Builder complements a segmentation study rather than replacing it, and the persona is always as strong as the evidence behind it.

UST and Italdesign Unite on AI-Driven Future Mobility

Mark Petays — Fri, 11 Sep 2026 03:54:04 +0000

UST has completed the acquisition of a majority stake in Italdesign from the Audi Group, bringing together Italdesign's automotive design and engineering expertise with UST's capabilities in artificial intelligence, digital engineering, software-defined vehicles, and technology transformation.

The partnership comes as the automotive industry moves toward increasingly software-defined, AI-enabled, connected, and intelligent vehicles. By combining design, engineering, software, and AI capabilities, UST and Italdesign aim to help automotive manufacturers accelerate innovation from the initial concept through production.

Key Highlights
What was announced: UST completed the acquisition of a majority stake in Italdesign from the Audi Group.
AI and automotive engineering: The partnership combines Italdesign's automotive design and engineering capabilities with UST's expertise in AI, digital engineering, and software-defined vehicles.
Faster innovation: The companies aim to help automotive manufacturers move more efficiently from vehicle concepts to production.
Connected mobility: The combined capabilities are designed to support the development of intelligent and connected mobility products.
Preserving Italdesign's identity: Italdesign will continue operating under its existing name while benefiting from UST's global scale, technology expertise, and client relationships.
Who is affected: Automotive manufacturers and mobility companies seeking to develop software-defined, AI-enabled, and connected vehicles could benefit from the combined capabilities.
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Company/Industry Context
The automotive industry is undergoing a major technology transformation as vehicles increasingly rely on software, artificial intelligence, connectivity, and digital platforms. This shift is changing not only how vehicles operate but also how they are designed, engineered, developed, and brought to market.

Italdesign, with nearly six decades of experience, has established a global reputation for combining automotive design creativity with engineering expertise. Its multidisciplinary approach has contributed to the development of numerous vehicle concepts and production models.

UST brings complementary capabilities in AI, digital engineering, software-defined vehicles, and technology transformation. The combination is intended to address the growing complexity involved in developing next-generation mobility products.

Software-defined vehicles are particularly important to this transformation. Instead of relying primarily on fixed hardware capabilities, modern vehicles increasingly use software to deliver functionality, connectivity, personalization, and updates. This requires automotive companies to integrate traditional engineering with software development, data, cloud technologies, and AI.

The UST-Italdesign partnership therefore represents a broader industry trend toward bringing design, engineering, software, and AI together within a unified development process.

The companies also emphasized sustainability and a multidisciplinary approach as part of Italdesign's continued operations. At the same time, Italdesign will gain access to UST's global technology capabilities and customer relationships.