Graphwise’s Sumit Pal offers commentary on the hidden reason AI fails and how knowledge graphs can fix it. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

The competitive edge in AI today is not about the next model on the leaderboard. Achieving a successful journey from paper to production is the most critical cog in the Data-AI-Flywheel. However, it relies on something less glamorous: a strong data foundation that includes a data strategy and data infrastructure. For enterprises seeking to unlock the powers of AI, it’s not enough to just have data. The most critical cog is establishing a robust data culture and an understanding of how data is created, managed, shared, trusted, and used.

In fact, Deloitte found 91 percent of companies expect to address data challenges in the next year, showing the criticality of data readiness for powering AI solutions. To ensure a successful AI development and deployment in enterprises, organizations should consider the following approaches to address five key challenges:

Address Data Quality

Today data debt is most prominent in the form of data quality with missing, incomplete, incoherent, and incompatible data. As organizations ingest heterogeneous data from internal and external sources, data teams encounter challenges with inconsistent formats, duplicate records, incomplete fields, outdated entries, and inaccurate data. These arise due to fragmented data systems, lack of standardization, manual errors, and insufficient governance around data and business processes. Poor data quality disrupts business operations and leads to flawed analytics, unreliable insights, and misguided strategic decisions. Additionally, it erodes stakeholder trust and increases costs due to repeated cleansing and reconciliation efforts impacting customer experience, regulatory compliance, and competitive advantage.

Organizations are increasingly leveraging knowledge graph-powered platforms to overcome the persistent data quality challenges that hinder advanced analytics and AI initiatives. Knowledge graphs connect disparate data sources into a unified semantic layer which enables enterprises to automatically detect inconsistencies, eliminate duplicates, and enrich incomplete information through intelligent context linking. It also ensures data relationships are explicitly modeled and maintained, improving accuracy, traceability, and governance across systems. Data and knowledge platforms enhance data cleansing, entity resolution, and metadata management, providing continuous validation and insight generation. As a result, organizations can transform fragmented, unreliable data into trusted, interconnected knowledge assets—fueling more accurate analytics, explainable AI models, and faster, data-driven decision-making.

Eliminate Data Silos

In modern enterprises data, content, metadata, and knowledge silos represent one of the most critical barriers to achieving true digital intelligence and agility. This fragmentation leads to duplication, inconsistent taxonomies, and disconnected insights, making it difficult for teams to get a unified view of data. Metadata silos further exacerbate the problem by obscuring context and lineage, limiting discoverability and trust in the data. Similarly, knowledge silos prevent the flow of institutional expertise across teams, slowing innovation and decision-making. The result is a significant drag on productivity, poor collaboration, and a missed opportunity for leveraging enterprise-wide intelligence. Breaking down these silos requires a connected data foundation that unifies structured and unstructured information, harmonizes metadata, and enables knowledge to flow seamlessly across systems and stakeholders.

Knowledge graphs enable organizations to break down the silos that fragment enterprise intelligence by connecting disparate systems and unifying structured and unstructured data with a semantic framework. Knowledge-powered platforms provide a holistic, interconnected view of the enterprise’s information landscape – capturing relationships and context across data sources, enriching content with metadata, and linking business concepts to create a dynamic network of knowledge. This interconnected foundation allows advanced AI and analytics tools to access trusted, contextualized data, improving model accuracy, discoverability, and explainability. A data knowledge management-powered AI platform unifies, transforms fragmented data and knowledge islands into a cohesive intelligence fabric, empowering organizations to make faster, more informed, and more strategic decisions.

 Create Context and Semantics

Context and semantics are the necessary ingredients for modern data and AI platforms. As data proliferates across silos, it takes on different meanings leading to ambiguity and lack of trust, which creates downstream integration challenges. In most enterprises, data is rife with ambiguities and impreciseness, which makes it difficult to use effectively for building AI solutions. For data to be useful, it needs to be presented intuitively with contextual enrichment to end users. Context is the critical element for surfacing insights from data. Consider the word “Paris” and how to distinguish if it refers to the French city or Paris Hilton. Humans readily understand context, but machines require semantic structure to disambiguate. Reliable facts with precise semantics become especially important when implementing Generative AI. A semantic model grounds Generative AI systems, mitigating hallucinations and leveraging proprietary data.

A knowledge management platform elegantly handles heterogeneity of enterprise data integration. Providing a unified view across all data and metadata silos with a semantic layer, it is based on context and semantics enriched with metadata and domain specific ontologies, taxonomies and conceptual relationships. This semantic foundation enables GraphRAG —or Graph-based Retrieval-Augmented Generation—to go beyond traditional RAG. Instead of retrieving unstructured text chunks, GraphRAG connects queries to a trusted, context-rich knowledge graph that represents how data points relate to one another. This allows the system to retrieve reliable, explainable, and traceable information. This allows GraphRAG pattern to combine retrieval augmented generation with the semantic layer to retrieve reliable and explainable data for decision-making. This empowers end-users with accurate and traceable responses governed by semantic principles with actionable insights, while creating a foundation for advanced AI applications that require contextual understanding.

Integrate Structured and Unstructured Data

It is critical for modern enterprises to effectively leverage both structured and unstructured data for building powerful and accurate machine learning and AI solutions. Structured data provides the foundation for quantitative analysis and model training. However, the majority of enterprise data is unstructured, residing in emails, documents, chat logs, videos, social media, and other textual or multimedia formats. Ignoring this wealth of unstructured information leads to incomplete insights and biased AI outcomes. The challenge lies in integrating these diverse data types, which differ in format, quality, and accessibility, into a unified analytical framework. Without proper integration and contextual understanding, enterprises risk developing AI models that lack depth, accuracy, and real-world relevance. Successfully combining structured and unstructured data allows organizations to capture the full spectrum of intelligence, which enables richer predictions, more human-like AI interactions, and truly data-driven outcomes.

Knowledge graphs based on the Resource Description Framework (RDF) graph model, empowers organizations to build a unified semantic layer that seamlessly integrates structured and unstructured data. RDF-powered graph models can easily leverage semantic web standards that semantically integrate data from relational databases, documents, APIs, and content repositories mapped to a common, machine-interpretable format. This preserves the meaning, context, and relationships across diverse data sources, allowing AI and analytics systems to reason over information rather than simply process it. This is possible through intelligent entity linking, ontology management, and metadata enrichment, transforming fragmented datasets into a connected knowledge ecosystem. This enhances discoverability and interoperability and powers explainable and context-aware AI solutions.

Establish Data Governance and Explainability

Strong data governance and explainability are essential pillars in building trustworthy, compliant, and effective machine learning and AI solutions. As organizations increasingly rely on data-driven algorithms to automate decisions and derive insights, the lack of proper governance can lead to biased models, inconsistent data usage, and compliance with ever-evolving regulations. Without clear lineage, accountability, and oversight, it becomes difficult to ensure that data feeding AI systems is accurate, ethical, and secure.

Black-box models erode stakeholder trust and hinder adoption, especially in regulated industries like finance, healthcare, and insurance. Explainability, which is the ability to understand and articulate how AI models arrive at their predictions or recommendations, is a critical cog for enterprises to achieve responsible AI. Doing so not only mitigates risk but also enhances confidence in AI-driven decisions, enabling organizations to deploy accountable AI solutions.

Knowledge graph-powered platforms also enable organizations to have visibility into data lineage, provenance, and quality across disparate sources. This ensures that every dataset feeding a machine learning model is traceable, validated, and compliant with governance policies. Additionally, the semantic context and AI-driven insights make model behavior interpretable, supporting explainability and transparency in decision-making processes. By integrating governance, metadata management, and knowledge relationships into a single ecosystem, enterprises can develop trustworthy, auditable, and responsible AI solutions while accelerating the creation of reliable data products that drive informed business outcomes.

Key Takeaways

As enterprises increasingly rely on AI and machine learning to drive innovation, the persistent challenges of poor data quality, fragmented silos, and the absence of standardized semantics and robust governance threaten the reliability and trustworthiness of these solutions.

AI applications are evolving from simple prompt based systems to being powered by autonomous, contextually enriched autonomous multi-agents. Enterprise-scale knowledge management is becoming increasingly imperative to power these next generation AI systems. In the race to become AI-driven, incorporating architectural principles of knowledge graphs for semantics and data management for context engineering, is a facet organizations cannot afford to ignore.

AI success increasingly depends on how effectively organizations connect and contextualize their data. Knowledge-driven architectures, anchored by semantic layers and governed relationships, provide the structure needed to transform raw data into insight, and insight into confident decisions. These foundations make AI not only more accurate, but also explainable, traceable, and compliant by design.

The next generation of AI systems will not be defined by larger models, but by smarter data. By weaving semantics, structure, and governance into the heart of enterprise intelligence, organizations can move beyond experimentation to operational excellence. Better yet, they will build AI that learns responsibly, reasons transparently, and earns lasting trust.

The post The Hidden Reason AI Fails & How Knowledge Graphs Can Fix Them appeared first on Solutions Review Technology News and Vendor Reviews.

The Solutions Review editors are exploring how and why companies should develop a marketing strategy that prioritizes Generative Engine Optimization (GEO) over traditional SEO best practices.

The ongoing, but probably irreversible, shift from traditional search engines to generative AI platforms represents one of the most significant disruptions to digital marketing since Google’s PageRank algorithm fundamentally restructured how information gets discovered online. While SEO practitioners spent decades optimizing for ten blue links, the emergence of ChatGPT, Perplexity, Claude, and similar platforms has created an entirely new paradigm where AI systems are synthesizing information and presenting direct answers rather than offering pathways to websites. This isn’t merely an evolution of search marketing—it’s a complete reimagining of how brands must position themselves in the information ecosystem.

With that in mind, the Solutions Review editors are exploring how enterprise technology brands can pivot their search engine optimization (SEO) strategy into a generative engine optimization (GEO) strategy, and why doing so is no longer a question of whether to develop one, but rather how quickly it can be launched.

The Fundamental Difference Between SEO and GEO

Search engine optimization operates on a relatively straightforward premise: convince algorithmic crawlers that your content deserves prominent placement in results pages, then capture clicks from users who have signaled their intent through query formulation. The entire framework assumes that users will navigate to your domain, consume your content on your terms, and potentially convert within your controlled environment.

However, generative engine optimization functions on entirely different mechanics. AI platforms don’t drive traffic to your website, but consume your content as training data or reference material, then reconstitute that information within their own response frameworks. The result is that users never have to leave the AI interface, since it provides them with synthesized answers that may draw from dozens of sources simultaneously, with attribution ranging from explicit citations to complete opacity depending on the platform and query type. While these AI platforms are still learning and the methods they use to learn are evolving (in response to legal and ethical developments, among others), the results are clear: people want them to stick around.

This transformation means that traditional conversion funnels could be on the edge of total collapse. The moment of engagement isn’t when someone clicks through to your site but when an AI model incorporates your perspective, data, or framework into its response. Your marketing success depends not on capturing attention through search result placement but on becoming an authoritative source that AI systems reliably reference when addressing queries in your domain.

Understanding How Generative Engines Process Information

Most current generative AI platforms operate through a combination of training data and retrieval-augmented generation (RAG). The training data represents a snapshot of internet content up to a specific cutoff date. Meanwhile, RAG systems enable models to access and incorporate more recent information through web searches or real-time document retrieval. This architecture creates several optimization opportunities that differ fundamentally from traditional SEO.

For one, training data integration means that high-quality, authoritative content published before the model’s cutoff date becomes baked into the model’s understanding of topics. The AI doesn’t need to retrieve this information because it already understands concepts through the patterns it learned during training. Consequently, content that influences training data shapes how models perceive entire topic areas.

RAG systems present different dynamics. When an AI platform performs retrieval to answer queries, it evaluates sources based on relevance, recency, and authority, then synthesizes that information from multiple retrieved documents. The goal isn’t to rank first in a traditional sense but to be included in the retrieval set and to provide information structured in ways that models can easily extract and integrate into coherent responses.

As you can imagine, platform-specific approaches vary significantly. Some AI systems provide explicit citations with links, creating a new form of referral traffic. Others synthesize information without attribution, making brand recognition and repeated exposure across multiple sources the only viable strategy for establishing mind share. Still others allow users to access source documents directly, transforming the AI interface into a discovery layer rather than a final destination.

Strategic Pillars for Generative Engine Optimization

Understanding how generative engines work is one thing, but knowing how to optimize your brand’s content and identity for them is another. That’s where the term Generative Engine Optimization (GEO) comes from. As new a strategy as it is, it’s already gaining momentum and proving to be a powerful addition to marketing strategies across markets.

Authority Architecture

Traditional SEO prioritizes domain authority as a holistic metric aggregating backlinks, traffic, and trust signals across an entire website. GEO, meanwhile, requires a more granular approach to authority that focuses on topical expertise and source credibility within specific knowledge domains. Additionally, counter to traditional SEO wisdom, which encourages broad keyword targeting, GEO often rewards extreme specialization. Being the unambiguous authority on a narrow topic makes you indispensable for AI systems addressing that subject, while being one of thousands of reasonable sources on a broad topic makes you easily substitutable in synthetic responses.

Building authority for AI systems means establishing your organization as the definitive source for specific concepts, frameworks, datasets, or methodologies. This requires moving beyond keyword-focused content toward creating comprehensive resources that demonstrate genuine expertise. AI models trained on authoritative sources internalize not just individual facts but entire conceptual frameworks, making depth and interconnection more valuable than breadth.

Verification mechanisms also matter more in generative contexts than in traditional search. AI platforms are increasingly incorporating source quality assessments into their retrieval and synthesis processes. As a result, organizations that can demonstrate expertise through credentials, peer review, institutional backing, or verifiable track records gain disproportionate influence in how models represent information in their respective domains.

Structured Knowledge Representation

Generative AI models excel at extracting structured information from unstructured text, but explicitly structured content makes this process exponentially more reliable. Organizations that format knowledge in ways that align with how AI systems process information gain significant advantages in retrieval and synthesis accuracy. Schema markup, which provided marginal benefits in traditional SEO, becomes far more valuable for GEO. Structured data enables AI systems to understand the relationships between entities, extract specific data points, and maintain accuracy when synthesizing information across multiple sources.

Semantic clarity in content structure helps models parse meaning correctly. This means moving away from creative headline writing and metaphorical language toward explicit, unambiguous expression of concepts. While traditional SEO sometimes rewards clever wordplay that captures long-tail searches, GEO favors straightforward articulation that models can confidently interpret and replicate.

Documentation formats that separate context from core information improve extractability. When AI systems retrieve content, they need to distinguish between background explanations and actionable insights, between qualifications and central claims, and between your perspective and the consensus. Content that makes these distinctions explicit through clear structural elements gets represented more accurately in AI responses.

Temporal Optimization

The time dimension functions differently in generative engine optimization compared to traditional search. SEO frequently emphasizes freshness signals, rewarding recently published or updated content with temporary ranking boosts. GEO creates a bifurcated temporal landscape where both historical influence and real-time relevance matter, but through separate mechanisms.

Content that influences training data carries significant weight in how models understand fundamental concepts, creating a strong incentive to publish authoritative frameworks and original research as early as possible to claim conceptual territory before competitors. Simultaneously, RAG systems create demand for continuously updated information on evolving topics. Organizations that maintain current and accurate data on developing situations position themselves as essential sources for real-time analysis and synthesis.

One potential outcome of this new approach is a divergence in content strategies, with one avenue focusing on foundational content that aims to shape AI training data, and the other on dynamic content intended for retrieval systems. Organizations that recognize this split and allocate resources to both types of content appropriately will outperform those applying uniform approaches across all material.

Tactical Implementation Approaches

Translating GEO principles into operational reality requires moving beyond conceptual frameworks into concrete content development and technical optimization practices. The strategic pillars outlined above provide directional guidance, but execution demands specific techniques for structuring information, formatting content, and establishing entity relationships that generative AI systems can reliably process and incorporate. The tactics that follow represent new approaches to content creation that prioritize machine extractability alongside human readability, recognizing that your primary audience now includes AI systems that will mediate how humans ultimately encounter your ideas.

Entity Optimization

Generative AI models understand information through entities and their relationships rather than through keywords and phrases. Optimizing for entity recognition means ensuring that your organization, products, executives, methodologies, and other important entities get consistently and accurately represented in AI knowledge bases. As such, variations in how you refer to your organization, products, or concepts can create ambiguity that models may resolve incorrectly, potentially conflating your entities with those of competitors or fragmenting understanding across multiple representations.

Meanwhile, entity-relationship articulation clarifies how different concepts are connected. Explicitly stating relationships between your organization and industry standards, between your products and use cases, and between your executives and their areas of expertise helps models build accurate knowledge graphs that inform how they discuss your entities in synthetic responses.

Multi-Modal Content Strategy

Text-focused optimization makes sense when targeting search engines that primarily process written content. Generative AI platforms are increasingly incorporating multimodal capabilities, which enable them to understand and generate images, analyze videos, and process other content formats beyond pure text. For example, AI systems can now process video transcripts and, increasingly, visual content from video frames. This means that organizations producing video content should ensure comprehensive transcription, time-stamped descriptions of visual elements, and structured metadata that help models understand not just what’s said but also what’s shown.

Visual content optimized for AI interpretation understandably requires different approaches than visual content designed for human consumption. Alt text, captions, and the surrounding textual context help models understand images, but layout, diagram structure, and the visual information hierarchy also play a role. Charts, infographics, and data visualizations that clearly label axes, include legends, and maintain high contrast support accurate AI interpretation.

Multi-modal content, specifically video, will likely become significantly more valuable for GEO as models improve at cross-modal reasoning. Organizations that invest in creating rich media content with strong structural signals and comprehensive metadata will establish a competitive advantage as AI platforms incorporate these capabilities into their retrieval and synthesis workflows.

Measurement and Analytics for GEO

Traditional SEO metrics focus on rankings, traffic, and conversions within your owned properties. GEO is something altogether new, though, and requires developing new measurement frameworks that account for influence and presence within AI platforms that don’t drive traffic to your site.

One way to track your company’s GEO efforts is through citation tracking across AI responses, which provides a direct measurement of when platforms reference your content. Organizations serious about GEO need to develop monitoring capabilities that track both explicit citations and implicit incorporation of your ideas and frameworks. Some tools have emerged to monitor these citations, although the landscape remains immature compared to traditional SEO analytics. Teams should keep a close eye on that market as it develops.

Brand mention analysis in AI responses measures when platforms discuss your organization, products, or executives without necessarily citing specific content. This softer metric captures presence in the AI information ecosystem even when direct attribution doesn’t occur. Another approach is to track competitive positioning in AI responses, which reveals how models present your organization relative to competitors. When users ask comparative questions or request recommendations, understanding how AI platforms position your offerings relative to alternatives provides crucial strategic intelligence.

The challenge is that generative AI platforms provide limited visibility into when and how they use content, making measurement inherently more difficult than traditional analytics. Organizations should expect to invest significantly in developing proprietary measurement approaches until the analytics ecosystem matures.

Looking Forward: The Evolution of Generative Engine Optimization

The current state of GEO represents an early-stage territory, as organizations figure out how to influence AI systems that are themselves rapidly evolving. Several developments are likely to dramatically reshape the field over the next few years, so brands must ensure their marketing and GEO-centric strategies can adapt to whatever new trends emerge. Here are a few predictions for what trends could be on the horizon:

• Retrieval mechanisms will become more sophisticated, incorporating quality signals that reward authoritative sources and penalize low-quality content farms attempting to game generative systems.
• Personalization in AI responses will fragment the optimization landscape. As models learn individual user preferences and tailor responses accordingly, universal optimization strategies become less effective. Organizations will need to consider how to remain relevant across diverse, personalized contexts rather than optimizing for a single, canonical response.
• Commercial integration of AI platforms will create new paid placement opportunities alongside organic optimization. Early signals suggest that sponsored content within AI responses may evolve into a significant revenue stream for platform operators, although business models remain uncertain.

Success in the generative AI era requires accepting that you may never control the environment where your ideas get consumed. Instead, you can influence the knowledge ecosystem that AI systems draw from, shaping how they think about and present information in your domain. Organizations that embrace this shift and develop robust GEO capabilities now will establish advantages that compound as generative AI becomes the dominant interface for information discovery.

Want more insights like these? Register for Insight Jam, Solutions Review’s enterprise tech community, which enables human conversation on AI. You can gain access for free here!

The post From SEO to GEO: How to Develop a Marketing Strategy for Generative AI Engines appeared first on Solutions Review Technology News and Vendor Reviews.

Zdravko Kolev, Manager of Product Development at Infragistics, explains why AI is forcing development teams to rethink their approach to software development. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

A revolution is underway in software development, driven by the emergence of low-code/no-code platforms and the integration of AI. The aim of coding solutions is not to replace developers, but to make them more productive by handling mundane tasks, allowing them to focus on higher-level design and innovation.

All forms of programming are likely to coexist in the near future, rather than one prevailing over the others. Traditional programming will remain for intricate systems, while low-code/no-code and AI will address different needs. As the role of AI continues to expand, we will observe how this technology changes development mindsets and roles, with developers transitioning into “prompt engineers” who guide AI to produce the desired code output.

Exploring Programming Paradigms

In a nutshell, low-code and no-code platforms allow for the creation of software applications with minimal coding knowledge. Low-code no-code software uses visual interfaces, drag-and-drop components, and pre-built templates. These platforms democratize app development, enabling non-programmers, or citizen developers, to build applications faster and more efficiently. At the same time, C-level executives, development team leaders, and enterprise architects can focus on digital innovation, achieving a faster time-to-market. There are tools like Google Forms for simple data collection, as well as platforms like WordPress, Shopify, and App Builder for more complex solutions.

Currently, 7 in 10 developers (71.8 percent) are using low-code/no-code tools, and 90.4 percent of developers report that low-code tools are boosting developer productivity in their organization, according to a recent App Builder survey. Moreover, according to Gartner, “by 2025, 70 percent of new applications developed by organizations will use low-code or no-code technologies, up from less than 25 percent in 2020.” Meaning that low-code/no-code tools are here to stay.

AI’s Role in Programming

AI in programming enables the generation and review of code based on natural language prompts, potentially reducing the time to market for new software. Unlike the deterministic models of the low-code/no-code approach, AI can produce variable outputs, which might require more oversight for consistency. AI programming can also enhance productivity by handling repetitive tasks and providing quick solutions for prototyping or small-scale applications.

However, there is a risk of poor quality when integrating AI capabilities into common and already determined workflows. Additionally, if AI capabilities are not properly managed by skilled developers, the result could be subpar performance or low-quality outcomes.  For example, AI-generated code may not capture the full context of a large, complex system because it lacks a comprehensive understanding of the entire system, leading to integration challenges. That’s why team leaders, CTOs, CIOs, and their development teams must understand what exactly AI does so they can utilize it effectively.

According to the 2024 Stack Overflow Developer Survey, “Developers are increasingly adopting AI tools but remain skeptical about their accuracy and ability to handle complex tasks, viewing them as complementary rather than a replacement for human expertise.”

The survey found that developers learning to code (27 percent) were more uncertain about AI as a threat than seasoned coders (18 percent). Six out of 10 (62 percent) professional developers are using AI tools this year, compared to 44  percent last year. Three-quarters (76 percent) of the developers surveyed are currently using or plan to use AI tools, and 72 percent have a positive attitude towards using AI tools at work.

One of GenAI’s key promises was its ability to deliver quick solutions and speed up time-consuming processes. However, 31 percent of developers are skeptical that these tools offer accurate solutions, and 45 percent believe AI tools are not good at handling complex tasks. AI is not threatening to replace developers because it has not yet replaced humans in the development process.

Traditional Programming

Traditional programming is well-known because it has been the foundational approach to software development for decades. It involves writing code from scratch, offering the highest level of customization, and providing control over every aspect of an application, from scalability to performance.

The following development statistics illustrate the traditional development approach:

• The software development market is predicted to rise to  $858.10 billion by 2028, with a CAGR of 5.27 percent.
• About eight in ten (84.7 percent) of software development projects focus on enterprise applications.
• JavaScript is the most widely used programming language, and it is preferred by 65.82 percent of professional developers.
• About half  (54 percent) of software engineers report being more productive when working from home.
• The Internet of Things (IoT) is expected to include over 75 billion devices in 2025.
• Nearly seven in ten (69 percent) of businesses have adopted cloud computing technology.
• Approximately 15 percent of businesses utilize third-party modern frameworks, such as Ignite UI for Angular, which provide pre-built, high-performance UI components. These components simplify complex development tasks while still allowing developers the flexibility and control they need to customize applications according to their specific requirements.

The events here are likely to occur as low-code tools, such as App Builder, AI agents, and models, grow in popularity and traditional programming becomes relegated to complex, large-scale systems that require fine-tuned performance or meet specific technical requirements.

The Business Advantages and Challenges of Each Paradigm

There are three core pillars to examine when evaluating the business advantages and challenges of each software development paradigm: time to market, customization vs complexity, and skill evolution.

Time to Market

According to the App Builder’s survey, a majority (43.5 percent) of developers save up to 50  percent of their time when they use low-code tools on a project. This enables rapid prototyping and deployment, particularly for simpler applications.

When to use each method for rapid deployment:

• Low/No-Code: Ideal for businesses needing rapid deployment of simple to moderately complex apps where customization isn’t critical.
• Traditional Programming: Best for highly customized, complex systems where performance and scalability are paramount.
• AI: Useful for both prototyping and enhancing productivity in existing workflows, especially for junior developers learning best practices.

Customization vs. Complexity

While low-code/no-code platforms accelerate development, they can become challenging when trying to achieve high levels of customization or when dealing with complex systems. Custom solutions might be more cost-effective for highly specialized applications. Low-code and no-code platforms must provide clear guidance to users within a structured framework to minimize mistakes, and they may offer less flexibility compared to traditional coding.

AI tools can be easily used to generate code, suggest optimizations, or even create entire applications based on natural language prompts. However, they work best when integrated into a broader development ecosystem, not as standalone solutions.

Skill Evolution

Low-code/no-code tools help bridge the gap in skilled labor but can also lead to over-reliance. Developers will need to adapt, focusing not just on coding but also on managing low-code and AI tools and understanding how to best “prompt” them for desired outcomes. For novice developers, exposure to all these technologies is beneficial. However, it’s strongly recommended to gain hands-on coding experience before relying too much on AI or no-code/low-code solutions. Understanding the fundamentals is key to using advanced tools effectively. New developers should engage with code manually to understand its mechanics before moving to automated or visual tools.

How the App Development Sector Will Adjust to Current Trends

The future of software development appears to be a blended approach, where traditional programming, low-code/no-code platforms, and AI each play a role. The key to success in this dynamic landscape is understanding when to use each method, ensuring C-level executives, team leaders, and team members are versatile and leverage technology to enhance, rather than replace, human ingenuity.

Let me share my firsthand experience. When I asked my developers a year ago how they thought using AI tools at work would evolve, many said: “I expect that as the tools improve, I’ll shift from mostly writing code to mostly reviewing AI-generated code.” Fast forward a year, and when we posed the same question, a common theme emerged: “We are spending less time writing the mundane stuff.”

My goal is to emphasize the shift toward more inclusive development environments while also highlighting the need for in-depth technical skills to manage these systems effectively. As AI and low-code/no-code tools evolve, so must developers. Adaptation to new ways of creating software is what drives business growth, workflow efficiency, and innovation.

The post How Development Teams Are Rethinking the Way They Build Software appeared first on Solutions Review Technology News and Vendor Reviews.

Quantum’s Skip Levens offers commentary on turning data hoarding into a strategic advantage. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

In the past, data hoarding was often viewed in a similar light to physical hoarding—a costly and inefficient practice that cluttered storage systems with outdated and irrelevant information. Organizations that held onto data far beyond its perceived usefulness or beyond compliance requirements were often criticized for wasting valuable storage resources, which were expensive to maintain. With no thought for its future value, the focus was on keeping only the most relevant and recent data–anything beyond that deemed unnecessary and subject to deletion.

However, the landscape has shifted dramatically in recent years due to two major developments: the rise of cloud storage and the advent of artificial intelligence (AI). Cloud storage, both private and public, has made it easier and more cost-effective for organizations to store vast amounts of data as data objects. Meanwhile, AI has emerged as a game-changer, with its potential to learn and improve from every piece of data it processes. As a result, organizations that were once criticized for their data-hoarding practices now find themselves at a significant advantage if they can implement a data management lifecycle strategy that leverages their data for insights and business value.

AI’s Insatiable Appetite for Data

Today, the most valuable asset in any organization is not just data itself, but the AI models that can be trained and refined using that unique data. The narrative has shifted from questioning the value of retaining all data to recognizing its critical role in AI development. While many assume that AI success is all about investing in powerful GPUs, the reality is that the availability of extensive, diverse datasets is equally important.

However, organizations are realizing that even with vast data stores, it’s still not enough to fully train AI models. The demand for high-quality data has led to the rise of synthetic data, where AI models generate additional datasets to fill gaps. AI researchers now leverage synthetic data as a way to create entirely new training sets, augment real-world data, and reduce biases. This shift highlights just how valuable data has become—not just for internal use, but also as a tradeable asset. Organizations are now renting or loaning their datasets to partners to fuel AI initiatives, recognizing that even proprietary datasets might not be enough to keep up with AI’s growing needs. But it’s not enough to retain all the data, you also have to have a way to organize the data so it can be easily searched, accessible and useful to the business.

What Does Data Hoarding Look Like?

Data hoarding, at its core, is the practice and mindset of retaining every piece of data an organization generates, guided by a “just in case” mentality. As data flows throughout your organization, this data should be protected and managed. While this may seem straightforward, the types of data that organizations generate are diverse. Some common categories of data that organizations should consider retaining include:

• Customer Support Records and Transaction Histories: Organizations often keep detailed records of customer interactions and transactions, sometimes dating back many years, to analyze trends, improve customer service, or refine marketing strategies.
• Internal Communications: Emails, shared documents, call transcripts, and other forms of internal communication amongst employees are often stored, providing a rich resource for understanding organizational dynamics and decision-making processes.
• Research and Development Data: Whether generated internally or sourced externally, R&D data is invaluable for innovation and product development. Retaining this data allows organizations to revisit past ideas and leverage them in new ways.
• Backup Redundancies and Obsolete Software Versions: While these may seem like outdated remnants of the past, retaining backups and old software versions can be crucial for troubleshooting, compliance, and reference.

Data hoarding has been happening in other forms for centuries. Consider the Library of Congress, which has an overarching mission to protect a nation’s cultural legacy and so preserves documents dating back to the founding of the United States, or European museums and universities that maintain archives spanning hundreds or even thousands of years. The Vatican, for example, holds documents that are millennia old. These institutions preserve such documents for the same reason modern organizations should retain their data: for potential reference, analysis, and use in the future.

AI Use Cases and the Growing Importance of Data

Data fuels AI, and as AI adoption grows, so do its use cases. AI is now playing a critical role in various sectors, including:

• Surveillance and Security: AI is transforming surveillance through applications like line detection, crowd control, facial recognition, and integrating watchlists like the FBI’s Most Wanted list. AI-driven video analytics enhance real-time threat detection and public safety.
• Healthcare: AI models trained on vast medical datasets are accelerating drug discovery, improving diagnostics, and personalizing treatment plans.
• Financial Services: Banks and financial institutions use AI to detect fraudulent transactions, assess creditworthiness, and automate risk management.
• Retail and Customer Experience: AI-driven recommendation engines analyze past purchase behavior and browsing history to deliver personalized shopping experiences.
• Autonomous Vehicles: Self-driving technology relies on massive datasets to improve navigation, obstacle detection, and traffic pattern predictions.

Making Use of the Data

To successfully transform volumes of data into a valuable, competitive asset that drives innovation and business insights, organizations must implement a data lifecycle management strategy.

Many organizations today don’t have a complete lifecycle strategy. There are three key areas to a data lifecycle strategy: a working area, where data is actively worked on, cleansed, and mined for value; an area where that data is then backed up and protected; and finally, an archive area where all data is collected and retained for future AI model training and analytics.

Most importantly, as part of their data lifecycle strategy, organizations need to understand what data they have and the value in that data. Often, they don’t have a way to organize, tag, index, and catalog it, and therefore can’t understand the potential value their data presents to their business. Just like a card catalog in a physical library, your data “library” needs to be organized so it can be searched and accessed to be useful to the organization. Having an automated workflow solution in place that automatically organizes and categorizes your data to make it AI-ready is critical.

Turning Data Hoarding into a Strategic Advantage

Data hoarding, once considered a wasteful practice, has now become an essential strategy for organizations aiming to succeed in the age of AI and gain a competitive edge. The reality is that organizations need to start retaining all of their data—not because they will use it immediately, but because they cannot afford to lose the potential value that data may offer in the future.

However, simply hoarding data is not enough. Organizations must also ensure that their data is stored and managed, organized, tagged, and enriched in a way that delivers performance while being affordable and accessible. By doing so, organizations can position themselves to leverage their data for innovation and a competitive advantage and thrive in an increasingly data-driven world.

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Tigera’s Ratan Tipirneni offers commentary on how to navigate PCI DSS compliance in cloud-native environments. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

The digital marketplace continues its meteoric rise, with analysts forecasting digital commerce revenues surpassing US$4 trillion by 2025 and reaching more than 3.4 billion global consumers by 2029. As organizations increasingly adopt containers and Kubernetes to deliver scalable (e.g., an eCommerce platform handling a Black Friday rush), resilient (e.g., a mobile banking app that self-heals from a service failure), and agile systems (e.g., a fintech startup deploying new features daily), ensuring Payment Card Industry Data Security Standard (PCI DSS) compliance becomes both more critical and more complex.

While this rise is powerful, the very characteristics that make Kubernetes environments so effective, including their dynamic, automated, and distributed nature, are also what cause many traditional security models, built for static hosts or VMs, to become inadequate. In fact, CyCognito’s research shows 1 in 3 cloud assets contain an easily exploitable vulnerability or misconfiguration, speeding the path from gap to incident. Organizations adopting cloud‑native architectures using Kubernetes must rethink how they secure, monitor, and audit cardholder data environments (CDEs) or risk falling out of compliance, facing stiff fines, and losing customer trust after a data breach.

Why Traditional Security Fails for Kubernetes and PCI DSS

Built for static hosts and virtual machines (VMs), traditional security models are becoming inadequate in dynamic cloud-native environments. Why? They fail to address the core challenges of modern, distributed architectures:

• Ephemeral & Dynamic Workloads: Containers are short-lived, with infrastructure changing constantly. Static security controls and snapshot-based audits quickly become irrelevant, leaving compliance gaps.

• Complex Network Topology: Kubernetes’ dynamic service-based networking and API-driven communication create a challenge for traditional security tools. This is because internal, east-west traffic between services bypasses the traditional network perimeter, making it difficult to monitor and protect.

• Point-in-Time Audits vs. Continuous Enforcement: In cloud-native systems, non-compliance can emerge within minutes. Manual processes and periodic audits cannot keep up with the continuous change in production.

• Lack of Full Lifecycle Visibility: Compliance must span the entire application lifecycle, from build to runtime. Many traditional tools lack the ability to enforce consistent policies, from image scanning and CI/CD pipelines to real-time traffic monitoring and logging of policy changes.

While traditional security models are becoming inadequate in dynamic cloud-native environments, Kubernetes requires a different compliance approach.

A Real-World Example: Navigating the Transition

I work for Tigera, which regularly helps organizations achieve compliance in complex Kubernetes environments. To take one example, Nowcom, a provider of software for the automotive and finance industries, faced the very issues outlined above while modernizing its legacy, VM-based infrastructure. The company sought to embrace containerization for greater agility but had to ensure strict PCI compliance in its new environment.

Challenge: With its legacy systems relying on static firewalls and a manual deployment process that took hours, Nowcom needed a modern approach. The company was required to host services with strict regulatory and security needs, posing a significant challenge for its new cloud-native architecture.

Solution: Nowcom implemented a network security and policy framework that enabled granular, role-based network policies. This solution allowed the security team to enforce rules without impacting developers. By implementing microsegmentation and automated security measures, Nowcom was able to isolate its most sensitive workloads.

Results: The results were transformative: deployment times were cut from hours to just minutes. The organization gained centralized visibility and control over its network security, which ultimately allowed them to achieve full PCI compliance. As Nowcom’s CTO Vimal Nair noted, “The ability to split [network] policies by roles was a game-changer for us… making network security transparent to developers.”.

7 Best Practices for Meeting PCI DSS Requirements in Containerized & Kubernetes Systems

IT leadership, security, and platform teams must adopt these practical, technical approaches to properly align with PCI DSS in Kubernetes environments:

1. Scope and Label Your Cardholder Data Environment (CDE)

Clearly define and inventory which services, pods, and clusters are in scope for PCI DSS. Use consistent labeling or metadata to distinguish these in-scope workloads from others, providing a clear map for security controls and auditors.

2. Enforce Microsegmentation and Zero Trust

Adopt a zero-trust model by implementing network policies that deny all traffic by default and only allow explicitly defined communication between services. This includes isolating CDE components to tightly control lateral movement, especially between microservices.

3. Encrypt All Data in Transit

Ensure all service-to-service communication is encrypted using mutual TLS (mTLS) or similar secure protocols. Critically, protect ingress and egress points where traffic enters or leaves the cluster to secure network paths to and from external services.

4. Shift Left: Secure Your Pipeline

Integrate security into the CI/CD pipeline to catch insecure container images and misconfigurations early. Use admission controllers to automatically block deployments that violate your compliance policies before they ever reach production.

5. Continuous Monitoring and Auditing

Maintain rich logs of network flows, policy changes, and Kubernetes API activity. Establish alerts for any policy drift or anomalous behavior that could impact compliance. This proactive approach ensures you’re always ready for an audit, rather than just at a single point in time.

6. Centralize and Automate Audit-Ready Reporting

Move away from manual reports. Preserve and version control all configurations and policy definitions. Automate the generation of compliance reports that map your current security posture to PCI DSS control requirements, providing clear evidence of enforcement for auditors.

7. Ensure Consistency Across All Environments

Use a unified policy framework that can enforce the same network, identity, and logging rules across multi-cloud, hybrid, and multi-cluster deployments. This prevents compliance gaps that can arise from inconsistent tooling or different cloud provider capabilities.

Future-Proofing Your Compliance Strategy

For any organization, a compliant security posture isn’t just about passing today’s audit, it’s about building a resilient, future-ready environment. The following considerations are critical for a long-term strategy.

Evolving PCI DSS 4.0 Requirements

The new PCI DSS 4.0 standard emphasizes continuous compliance and adaptability. A forward-thinking strategy anticipates these changes to avoid being caught off guard, as a reactive approach can introduce significant risk and cost.

Runtime Threat Detection & Response

Pre-deployment security is essential, but it isn’t enough. Modern threats are sophisticated and dynamic, requiring real-time detection of anomalous behavior in live systems. You must have mechanisms to detect and respond to a compromise the moment it happens.

Governance & Culture

Compliance is not purely a technical issue. It’s a matter of organizational discipline. Ensuring buy-in from all stakeholders, defining clear roles, and integrating security into your company culture are essential to prevent accidental non-compliance and maintain a secure CDE.

Balancing Performance, Cost, and Security

Every security control has a trade-off. IT leaders must evaluate how different tools for encryption, monitoring, or segmentation will impact system performance and operational costs. A successful strategy finds the right balance to scale efficiently without sacrificing security.

Next Steps: From Strategy to Secure Reality

Kubernetes and containers are now at the very core of modern digital commerce. While this shift introduces new security and compliance complexities, the path forward is clear and actionable. The key is to move beyond static, perimeter-based thinking and embrace a holistic, cloud-native security strategy. By diligently defining and isolating your Cardholder Data Environment, adopting a zero-trust model, and integrating security into your automated pipelines, you can meet PCI DSS requirements without sacrificing the agility and scale that drive your business.

The time to act is now. Assess your current posture against these best practices, identify your gaps, and invest in a strategic approach that unifies technology, governance, and culture. PCI DSS compliance is not a burden; it is a business imperative. Those who get it right will not only maintain customer trust but also gain a competitive advantage, positioning themselves to innovate securely in the ever-evolving world of digital commerce.

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Duncan Greatwood, the CEO of Xage Security, examines how AI agents and zero-trust security are shaping a new identity paradigm. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

Agentic AI is 2025’s hottest tech topic—and yet AI agents are being held back by the risks. Fears of rogue behavior abound, with cautionary tales such as a Replit agent deleting a customer’s entire codebase, leaving businesses hesitant to trust agent-based AI with critical tasks. Human leaders are understandably reluctant to put themselves in a position where they must answer for AI’s costly mistakes.

At the same time, ignoring agentic AI would be short-sighted. Well-governed agents that deftly accomplish their tasks promise significant efficiency gains and enable new ways of working. This presents CISOs and CIOs with a pressing problem. Agents need clear controls that keep them on track and restrict deviations that may have disastrous ripple effects. Current methods, such as prompt guardrails, are insufficient, as they can be easily bypassed by deliberate or accidental “jailbreak” inputs. Zero-trust identity-based controls can provide the necessary jailbreak-proof protections—provided they are extended to operate in the agent-based era.

Controlling AI Agents

Agents need to have identities applied to them, much like human users and machines do, but the controls placed on those identities should be tailored to meet the unique challenges that agents present. The paradigm needs to be built around both what makes agentic AI similar to existing entities and what makes it different from them.

What are the specific requirements for an agentic Zero Trust approach?

1. Agent identity for each agent, such as is provided in the A2A protocol/OpenAPI card
2. Authentication and entitlement management for agents
3. Enforcement of what agents can do with identity-based, jailbreak-proof, granular controls
4. Multihop entitlement delegation for user-to-agent and agent-to-agent controls
5. Least-privilege entitlements, delegating only what’s needed for the task at hand

Implementing these requirements stops attackers from gaining control over critical systems by using agents to escalate their privileges. It creates accountability, so it is always clear who is ultimately responsible for initiating an action. It stops rogue AI agent behavior by avoiding excessive entitlement delegation to autonomous agents. It also prevents data leakage by enforcing identity-based control over data retrieval and transmission.

With properly implemented Zero Trust for AI agents, each agent operates in a focused, controlled, and task-appropriate manner, avoiding the potentially catastrophic risks associated with unmanaged AI privileges.

Examples to Learn From

The Replit incident may be the most notorious example of rogue agent activity to date, but it’s just one example of misbehavior uncovered by prominent AI research. September findings from OpenAI and Apollo Research revealed that many leading AI models are capable of scheming, or concealing their behaviors to achieve alternative goals. They even detect when they’re being watched, and act accordingly.

It’s therefore irresponsible to give agents anything more than least-privilege access to operational systems—their controls need to consider and block every rogue possibility and ensure that efficiency gains don’t come at the expense of security and predictability.

Why Zero Trust is the Answer

Zero-trust principles, grounded in time-bound, identity-based access controls, are ideal for agents. Their missions are focused in scope and clearly defined, making them prime candidates for management with granular, identity-based access controls. It’s a framework that’s proven to be effective in both preventing and mitigating the effects of breaches.

Recent incidents, such as the $2.5 billion breach that affected Jaguar Land Rover, have served as reminders of how wide-reaching and tangible the effects of external cyber-attacks can be. Internal disruptions like agent misbehavior and data leakage can be just as costly, though, and applying the same Zero Trust safeguards to employees, chatbots, agents, and external parties is the best way to protect organizations from missteps (intentional or not) that cause compounding damage.

AI agents are both a critical innovation for businesses to employ and a new point of vulnerability where protective measures are urgently needed. Securing them needs to ensure both convenience and resilience, allowing agents to operate as efficiently as intended while also holding them accountable to their goals and restrictions. Zero Trust is a tried-and-true framework that enables organizations to do just that, leaving room to root new security measures in identity-centric principles that stop rogue behavior and abuse before it starts.

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Steve Oriola, CEO of Insightly by Unbounce, explains why a human touch is still essential in a marketplace where AI-driven CRM systems are king. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

Customer loyalty remains the ultimate performance metric. True loyalty isn’t a single transaction; it’s the repeat choice, the ongoing renewal, and the advocacy that follows a great experience. While human connection is the foundation of that loyalty, artificial intelligence (AI) is reshaping how organizations can scale it. CRMs exist to create and retain relationships. It’s the only software tool with “relationship” in the name. And while we seek to leverage AI in many ways, we can’t move too far in the direction of automation.

In today’s digital ecosystem, AI powers everything from creation to analysis. The key to the next generation of customer experience (CX) won’t be choosing between efficiency and empathy, but about achieving the right balance between the two.

The Dual Edge of AI in CX

AI is transforming the way businesses understand and engage with customers. Modern CRM and marketing automation platforms are now equipped with built-in AI capabilities that can analyze behaviour patterns, detect buying intent, and automate personalized outreach at scale. This level of efficiency drives faster service and reduces operational friction. Customers benefit from instant answers and seamless processes. But it also introduces a risk: automation without empathy.

Automated systems, when left unchecked, can miss nuance or fail to interpret emotional tone. Overreliance on AI can make interactions feel cold and transactional, eroding the trust and emotional connection that drive retention.

With over a decade of experience innovating with CRM, I’ve found that any type of automation works best when it amplifies human judgment, rather than replacing it. Businesses that deploy AI thoughtfully design workflows where technology supports empathy, rather than competing with it. The data backs this up. A recent Zurich study (2025) found that 73 percent of consumers avoid businesses that don’t demonstrate empathy, proving that even in a digital-first environment, emotional intelligence remains a decisive differentiator.

Building AI-Driven CRM Strategies Without Losing Trust

AI is redefining what customer relationship management (CRM) means. Beyond data organization, CRMs are evolving into intelligence platforms that can surface actionable insights and predict future outcomes. Here’s how leading organizations are leveraging AI for smarter, trust-centered engagement:

• Predictive revenue intelligence. AI-driven scoring models identify which prospects are most likely to close, helping teams focus on the highest-value opportunities.
• Sentiment and intent analysis. Natural language processing tools analyze tone, emotion, and urgency across communications, helping teams respond with empathy and precision.
• Automated health alerts. Machine learning models can identify at-risk customers based on low engagement or negative sentiment signals, enabling proactive outreach before churn occurs.

However, transparency is key. Customers must be aware when they’re interacting with AI and have clear pathways to human support when necessary. Trust is not built by hiding automation but by making it a known, seamless extension of human-led service.

Designing Workflows Where Automation Enhances Human Value

Automation should never replace human skill, but it can and should remove the friction that prevents humans from doing their best work. AI excels at routine and repetitive tasks, such as summarizing emails, scheduling follow-ups, or updating deal stages. Humans excel at empathy, problem-solving, and strategic insight.

In high-performing CX organizations, these two forces work together. For example:

• AI chatbots can handle FAQs or gather context before routing prospects to live sales representatives, reducing response time while enabling salespeople to focus on high-value opportunities.
• Marketing teams can automate data capture and lead scoring while focusing on creative campaigns, relationship building, and tailored proposals.

The best workflows are built around a simple rule: automate what doesn’t require empathy, and invest human energy where it does.

Leaders also need to invest in AI literacy across teams. Training employees to understand how AI surfaces insights—and how to act on them—ensures that adoption doesn’t feel threatening, but rather empowering.

The Future of CX: Human Empathy, Supercharged by AI

The evolution of customer experience depends on maintaining equilibrium—data with empathy, speed with sincerity. AI will continue to reduce the manual workload and accelerate responsiveness, but empathy will always be the differentiator that keeps customers loyal. Leaders must not only decide what to automate, but also consciously define what should remain human.

In the end, AI can make you faster, but empathy makes you unforgettable. The organizations that master both will lead the next era of customer experience—one where technology doesn’t replace humanity, but enables it at scale.

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SAS’s Jonathan Moran offers commentary on quantum AI in marketing and the next frontier of customer engagement. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

Marketing has always been an incubator for innovation, from the Mad Men era of intuitive campaigns to today’s data-driven, AI-enhanced customer engagement strategies.

Technology has been a key driver of change and innovation in marketing. And now yet another emerging technology is poised to redefine how marketers understand, influence, and engage with audiences. Quantum AI, the fusion of quantum computing and artificial intelligence, is still at an early stage but has the potential to fundamentally change marketing.

Evolving From Agentic to Quantum AI

According to a recent study, Marketers and AI: Navigating New Depths, 31 percent of adopters (marketers who are already using agentic AI) expect quantum computing to impact marketing within two years. These early adopters aren’t just dabbling; they’re building the infrastructure to eventually support thousands of autonomous agents that will operate alongside employees, making real-time decisions based on predicted outcomes, optimizing campaigns, and even creating digital environments.

The leap in readiness between adopters of new technology and planners (plan to use the tech in the next year) and adopters (plan to use the tech in the next two years) is striking. While only 16 percent of marketers overall say they understand quantum computing well, that number jumps to 49 percent among agentic AI adopters. These early adopters are not just experimenting with autonomous agents, they’re preparing for quantum’s computational power, its collaboration with AI technologies, and its undoubtedly massive impacts on marketing.

Why Quantum AI Matters for Marketers

Quantum AI combines the probabilistic power of quantum computing with the pattern recognition and decision-making of AI. Unlike classical computers that process bits as 0s or 1s, quantum computers use qubits, which can represent multiple states simultaneously. This makes them ideal for solving complex optimization problems, simulating customer journeys, and analyzing massive datasets in real-time.

For marketers, quantum AI’s vast potential can translate into:

• Faster audience insights: Quantum AI can create and microsegment audiences with more precision and speed than traditional solutions.
• Smarter behavior prediction: Enhanced data analysis leads to better forecasting, personalization, targeting and real-time AI decisioning.
• Real-time optimization: Quantum speeds up A/B and multivariate testing, feedback loops, and pricing models, enabling immediate strategy adjustments.

Unlocking Quantum’s Marketing Potential

Quantum AI is poised to revolutionize many core marketing functions, several being:

Audience Segmentation

Quantum AI enables the processing of vast datasets with more variables and attributes, allowing marketers to refine segments faster and more accurately. This leads to hyper-targeted campaigns with improved performance.

Customer Behavior Prediction

Quantum-enhanced machine learning models can deliver deeper insights into preferences, trends and patterns. This supports more precise personalization and dynamic content delivery based on real-time behavior.

Optimization

Marketing optimization often involves evaluating countless combinations of variables. Quantum AI can dramatically accelerate this process, helping marketers allocate budgets, adjust strategies, appropriately contact to avoid customer saturation, and maximize ROI with unprecedented speed.

Journey simulation

Quantum AI can simulate and help orchestrate complex customer journeys across multiple touchpoints, enabling marketers to anticipate outcomes and tailor experiences proactively.

Real-world Applications Across Industries

The study reveals that quantum AI is already being explored across industries:

• Banking: Advanced predictive analysis for fraud detection, risk mitigation, and customer retention.
• Insurance: Real-time customer journey simulation to improve claims processing and engagement.
• Life Sciences: Hyper-personalization at scale for patient communications and trial recruitment.
• Public Sector: Synthetic data generation and dynamic pricing for citizen programs and services.

Barriers to Adoption: What’s Holding Quantum AI Back? 

Despite its promise, quantum AI faces hurdles. Another SAS survey, conducted in April and involving 500 business leaders globally, found that top concerns related to quantum AI include high cost (38 percent), a lack of understanding or knowledge (35 percent), and uncertainty around real-world applications (31  percent).

These barriers underscore the need for economic conversations around cost, enablement and education, and trusted partnership and ecosystem planning. As quantum evolves, organizations should make quantum education and research more accessible, explore hybrid quantum-classical solutions, and collaborate with companies and broader industry consortia already working on quantum technologies.

A Quantum AI Readiness Checklist for Marketers

If quantum AI still feels like science fiction, here are a few tips to begin your journey:

1. Master traditional, generative, and agentic AI first: Quantum AI builds on the foundations of traditional, generative, and agentic AI. Ensure your team is proficient in these technologies before leaping into quantum.
2. Build quantum into your innovation roadmap: Even if implementation is years away, start planning now. Identify potential use cases, assess data readiness and explore partnerships with quantum leaders.
3. Upskill your team: Invest in training that covers quantum basics, AI ethics and data governance. Encourage cross-functional learning among marketing, IT and data science teams.
4. Start small with hybrid models and projects: Explore hybrid quantum-classical architectures for optimization problems. These models offer a manageable entry point while delivering tangible benefits.
5. Focus on trust and transparency: As with any AI initiative, trust is paramount. Ensure explainability, oversight and ethical use are baked into your quantum AI strategy.

The Quantum Advantage: Speed, Scale, and Strategy

As agentic AI matures and the demand for real-time, hyper-personalized experiences grows, quantum AI’s speed, scale and computing power hold great promise to meet this demand. Marketers who embrace quantum today – by learning more about it and its potential application across marketing functions – will be positioned to lead the next wave of marketing transformation.

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Allie Kelly, CMO at Intentsify, explores AI, its role in intent data, and how it can unlock precision in B2B marketing. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

Artificial Intelligence (AI) has had a significant impact across various industries, reshaping strategies and transforming business models. B2B marketing is no exception. Traditionally, B2B marketers have leveraged intent data to identify prospective buyers and create campaigns targeting them. With AI transforming data analysis, uncovering precise patterns and higher-level buyer context, marketers can better leverage intent data to gain a deeper understanding of where buyers and buying groups are in the buying cycle, enabling scalable campaigns and more precise audience targeting.

With the shift from individual buyers to buying groups and the constantly evolving landscape, AI-driven intent data is crucial for ensuring successful go-to-market (GTM) strategies.

The State of Intent Data in B2B Marketing

In the current environment, B2B marketing teams are struggling to maximize ROI on intent data. To effectively leverage intent data and maximize ROI, teams must understand how buyer signals are sourced, scored, and categorized before applying the data to their campaigns. Without this deeper understanding, it can be difficult to reach buyers at the right time and in the most effective way. With intent data being collected from a myriad of sources, these insights often lack transparency and can become siloed, diminishing the potential effectiveness when creating buyer models.

As the buying cycle becomes lengthier and more complex, understanding the when and why in buyer engagement is key. Recent Forrester data highlights that 81 percent of buyers have expressed dissatisfaction with the B2B buying process and their chosen providers, underscoring the need for more precise approaches to intent-driven data strategies. Simultaneously, research also shows that 75 percent of B2B buyers prefer a rep-free sales experience, highlighting the need to maximize value at every touchpoint in the buyer journey.

The Impact of AI on Intent Data on B2B Marketing

Traditional intent data tools rely on static data, such as website visits and form fills, but AI models enable the rapid processing of behavioral signals and context in real-time. By shifting from reactionary insights based on previous data to predictive analytics and recommendations, marketers can take advantage of benefits including:

AI Driven Data Analysis – Maximizing Data Value

AI can enable marketing teams to analyze large stores of intent data, cutting through the noise and providing critical insights and recommendations for marketing and sales teams, allowing for more strategic and targeted buyer engagement.

Target Buyer Groups – Solution-Level Intent Modeling

Rather than leveraging category modeling, AI-powered intent data solutions can utilize solution-level intent modeling to differentiate the weight of each captured behavior, providing deeper insights at the account, buying group, or persona level.

Quality Data – Understanding Customer Intent

In the past, marketing teams have relied on volume-based metrics like clicks to gauge buyer interest. AI-powered solutions can provide key context related to customer intent by recognizing behavioral patterns and providing insights into the consumer’s stage in the buying journey and their level of interest.

How to Leverage AI-Driven Intent Data to Maximize ROI

As the B2B marketing space and the buyer journey continue to evolve, AI-powered intent data is emerging as a powerful tool for maximizing the effectiveness of GTM strategies and empowering marketing teams to engage buyers with precision. To maximize the ROI on AI-powered intent data, the marketing and sales teams must align by integrating insights into CRM tools.

However, before considering integration, here are four key elements marketers should consider when identifying the best vendor for their business:

Signal Fidelity Over Signal Volume

The era of vanity metrics is over. CMOs should demand intent data partners who can demonstrate why a signal matters, not just that it exists. Consider providers who offer granular transparency into signal weighting methodologies and can differentiate between passive content consumption and active problem-solving behavior. The question isn’t how many signals did we capture?—it’s how many signals actually predicted buying behavior? Insist on seeing decay curves, false positive rates, and retrospective conversion analysis.

Integration Architecture as a Competitive Moat

Intent data solutions should function as connective tissue across the entire revenue tech stack—not another data silo. CMOs should evaluate vendors on their ability to operationalize insights in real-time across CRM, MAP, advertising platforms, and sales enablement tools. The most sophisticated CMOs are building “intent orchestration layers” where AI-powered signals automatically trigger coordinated plays across channels. If a vendor can’t explain their API strategy and bi-directional data flows in the first meeting, keep looking.

Buying Group Intelligence, Not Just Account Scoring

Individual account scores are table stakes. The next frontier is understanding the composition, dynamics, and readiness of buying committees. CMOs should seek partners who can map relationship networks within target accounts, identify the emergence of new stakeholders, and detect shifts in buying group consensus. The winning vendors are those who can answer: Which three people need to align for this deal to progress, and what content will bridge their divergent priorities?

Adaptive Learning Systems Over Static Models

The most dangerous assumption is that buyer behavior remains constant. CMOs should prioritize intent data vendors who employ continuous model retraining based on actual conversion outcomes—not industry benchmarks. Partners should demonstrate how their AI adapts to each unique buyer journey, incorporate feedback loops from closed-loop revenue data, and evolve as market conditions shift. Ask the hard question: How does your model perform differently for us versus your other clients, and can you prove it?

Conclusion

As AI continues to evolve, its role in shaping the increasingly complex buyer journey has become more apparent. With traditional intent data becoming less effective, the future of B2B marketing success depends on a combination of human knowledge and AI-powered insights. This will enable marketers to make the most of their intent data through more informed decisions on GTM strategies and buyer engagement, giving them a competitive edge in the market.

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