1. Core banking platforms have been in production at many credit unions for decades. The data structures they created weren't designed for modern querying, and replacing them isn't a realistic option for most institutions. What does that mean for any analytics tool trying to sit on top of that infrastructure?

Given that core platforms are sticky and any migration takes years, a modern analytics tool needs to work with where the data already is: the core databases, the on-prem data warehouses, or even the cloud data warehouses. Furthermore, core data is often connected to other data sources that track member journeys, e.g., CRMs, CDPs, web analytics, and so on. This requires linking all these sources together, interpreting the complex schemas and data structures of each, performing in-situ data transformations to make them queryable, understanding the domain-specific semantics needed to serve business users, and making the relevant pieces of data accessible to stakeholders securely and in natural language — all of which is fortunately possible today with AI.

2.  The argument for specialization is the industry context which has to be built in, not bolted on. How much of Tursio's value sits in that embedded context versus the underlying technical architecture and how long did it take to build that knowledge layer?

Tursio's primary value sits in the embedded context, not the underlying technical architecture; the architecture is what makes that context fast to build and keep current, but the context itself is the differentiator. In fact, industry context is critical for any AI to work. Unfortunately, there is no one-size-fits-all: each organization has its own nuances, even within the same industry, and all of them must be incorporated for the AI to behave just like that organization. Tursio’s architecture assists that by ingesting database schemas, BI reports, and query histories to bootstrap a context graph that mimics the current organizational understanding — a well-defined process that completes within a few weeks. In addition, many of the nuances remain undocumented and constitute the bespoke knowledge in people’s heads. Tursio records feedback from all interactions, from users and agents, and attaches it as annotations to relevant portions of the graph. This part of the process is ongoing, and it treats context as a living data structure that continually evolves over time.

3. Trust is the word that comes up in every regulated industry conversation. What does it mean for an institution to trust an AI system that is making queries against their most sensitive data and how does the deployment model address that rather than just the contractual terms?

Trusting an AI system is a leap of faith, and it is our job as a vendor to facilitate that. Tursio does this by deploying on-premises and connecting to data sources within the credit union environment, so that all responses come from their data and are not hallucinated from external knowledge. On-premises deployment further keeps the access boundaries within the credit union, protecting member data from any accidental leakage. Also, Tursio uses LLMs from credit unions’ own Azure subscription, wherein all prompts are fully private and guaranteed not to be used for any model training anywhere. By securing data sources, data access, and LLM prompts, Tursio puts the credit union fully in control of their AI deployment and helps build trust from the get-go.

4.  The "your subscription, your tokens" model you've described changes the vendor incentive structure. When the vendor's revenue doesn't scale with token consumption, why does that matter specifically in an AI/BI context where query efficiency has a direct cost implication? 

Many AI applications are LLM wrappers that meter usage with tokens; essentially, they resell tokens and rely on growing token usage over time. This is a problem for AI/BI since the number of queries can grow very quickly and vendors have little incentive to optimize them. Tursio flips this model by allowing credit unions to plug in their own Azure subscription with their choice of Azure-compliant models, while Tursio's revenue grows with the size of the context (data sources and organizational nuances) that it helped the credit union build. This means Tursio is focused on optimizing token usage, rather than bloating it, since that will lead to more adoption and hence more context to be built. At the same time, the credit unions leverage the enterprise-grade governance and control provided by Azure across their entire AI estate.

5. The goal of natural language database search is that a non-technical executive can ask a question and get an answer without knowing how the data is structured. How close is that to working reliably in production and where does it still require human expertise in the loop? 

The reliability of natural language database search depends on the quality of the context. For Tursio, everything captured by currently documented semantics (schemas, query histories, Power BI reports) can already be answered reliably in production. However, human expertise is still needed to help capture the bespoke knowledge that is not yet documented or that keeps evolving over time. Tursio provides the tools and processes to continue capturing and vetting that additional context with expert assistance. In terms of analysis types, Tursio’s data search can be layered with agents like Copilot, Claude, or others on top, via its Model Context Protocol (MCP) server, for rich analysis ranging from descriptive all the way to prescriptive.

6. The data governance requirements that protect sensitive information also tend to restrict who can access it and how. How do you design an analytics system that is open to non-technical users while maintaining the access controls and audit trails that a regulator would require?  

Managing data access controls is one of the first questions when opening sensitive data to querying by non-technical users. Given the shared context graph that Tursio operates, it is uniquely positioned to enforce access controls centrally, in one place. Specifically, Tursio integrates with existing enterprise authentication (e.g., Microsoft Entra ID) to enforce access control at a fine-grained level. Admins can create one or more logical datasets that define the querying scope (tables, columns, etc.) from each database and decide which users (Entra IDs) or user groups (Entra groups) have access to which datasets. They can further apply table-, row-, or column-level access controls (TLS, RLS, CLS) on a given dataset, again via their Entra ID. Likewise, Tursio’s MCP server gatekeeps all access via users’ Entra IDs, so they only get to search what they have access to. In addition to Entra ID, the Tursio search portal lets admins organize users into roles such as admins, owners, users, and viewers, and personalize the search experience and visibility for each role — e.g., viewers cannot download any data artifacts. For enterprises managing data governance via Microsoft Purview, Tursio can connect and sync definitions and access controls across all data products. All data controls, as well as data access, are logged along with the identifying Entra IDs and roles for audit purposes. These logs are further compressed in-situ as they grow, or users can connect them to other log-management systems, such as Splunk or Grafana, for observability.

7. You've written about natural language becoming the interface layer for data not as a feature on top of existing BI tools, but as a replacement for the query-and-dashboard model. How far away is that as the production reality for organizations, and what has to change at the infrastructure level before it gets there?

The query-and-dashboard model has one fundamental problem: the questions come from business users, but they must wait through long dashboarding cycles before getting the answers. Since these questions are in natural language anyway, LLMs can now connect business users directly to enterprise data, cutting down layers of inefficiency and unleashing new levels of productivity. Still, the gap remains in the context layer that is not yet built, which is actually an infrastructure problem. Building and managing context that can be harnessed to serve business users is the change organizations must go through. Tursio is one such effort in this direction, but the trend of opening highly valuable business data to business users and agents is likely to amplify in the near future. That also means rewiring the data and BI teams into context teams that build reliable context with proper security and access controls built in to make it ready for agents. Most organizations are already thinking in that direction, but it is still early days, with efforts being contained and siloed to avoid any big disruption. I expect organizations to build more confidence and move far more quickly as the context infra matures over the next 6-12 months.

8. As CEO, you're making product bets today on where data access is in three to five years. What are you building toward that the market isn't asking for yet and what does the current state of the technology tell you about where the problems still are?   

Today, data is fragmented across systems and processes; it is dirty, with incomplete or even incorrect values; and it is ambiguous, with lots of loaded semantics needed to interpret it — in short, it is very hard to use. Making AI work on such a data estate is highly challenging yet sorely needed — data migration and integration are tediously long, data cleaning is painfully hard, and data semantics are excruciatingly slow to capture. At Tursio, we are building a disambiguation layer that abstracts clean, unambiguous data — usable by AI — from the organization-wide messy data underneath. Automatically inferring this disambiguation layer, refining it over time, enabling sophisticated data access over it, and keeping a human in the loop throughout are some of the challenges we are working on. As LLMs become increasingly more capable, we believe all of these challenges are going to be addressable.

By Carey Madsen, VP and CMO, The Fletcher Group

94% of B2B buyers now use AI during the buying process, and most marketers are working hard to insert their brands into those buyer recommendations. But you’re probably making it harder than you need to.  

Here’s a scenario that plays out every day in B2B: a company earns a strong media placement in a respected trade publication. The story is sharp, well-positioned, and reaches the right audience. Then it disappears. Posted once on LinkedIn, shared internally, and forgotten. Sales never sees it. The website never references it. No one writes a follow-up post that builds on the insight. The executives who could have amplified it don’t.

This is what happens when PR and marketing operate in silos. Coverage and content don’t travel far  and in 2026, that has consequences that go beyond missed amplification. It affects how often your brand appears in AI-generated answers.

The way B2B buyers research and evaluate vendors has changed fundamentally over the past two years. Buyers no longer follow a neat funnel. They may read a trade article, which prompts a question, so they ask ChatGPT or Claude. The answer frames their next steps, which might include a visit to your website to read an FAQ or case study, an industry report, or to a competitor’s site instead.

If your messaging isn’t aligned and repeated across these channels, you haven’t made your brand known; and it’s difficult for buyers to find you, because they don’t know what you solve for. In a nutshell, vague messaging gets skipped, while consistent messages gets cited.

How Do B2B Buyers Research Vendors in 2026?

Forrester’s 2026 State of Business Buying report shows that purchasing is more collaborative, and dependent on validation from trusted sources than in previous search eras. Buyers rely on what Forrester calls a “buying network”  internal stakeholders plus analysts, peers, and earned media — to validate what they learn from any single channel, including AI tools.

The Forrester data paints a clear picture of just how early these decisions are forming:

·       92% of B2B buyers enter the process with at least one vendor in mind, and 70% of the journey happens before sales engagement 

·       9 out of 10 C-suite decision makers say they are more receptive to thought leadership than traditional marketing materials

·       94% of buyers use generative AI during the buying process, but 20% report inaccuracies—leading them to validate AI outputs against third-party sources 

Buyers use AI as a data point, then confirm what they find through media, analysts, LinkedIn, and your owned content. If your brand shows up in only one of those places, you’re missing other essential validation opportunities.

Why Do LLMs Favor Brands with Multi-Channel Presence?

This is where buyer behavior and AI visibility intersect. LLMs pull from media coverage, brand content, social conversations, and third-party validation to shape the answers buyers see. Brands that appear across more source types tend to be cited more often and with more context.

The rules of AI-fueled search are evolving in real time, but several patterns are already clear enough to act on:

·       Earned media drives the majority of AI citations. Muck Rack found that 82% of citations come from earned sources

·       Brand search volume is a stronger predictor of AI citation than traditional SEO authority like backlinks 

·       LLMs do not share the same resource pools, so, appearing on a wide range of relevant channels—owned, paid and earned—is necessary to be cited by all the most popular LLMs

In practice, this means disconnected or incomplete efforts across PR and marketing teams create visibility gaps that competitors can fill. When PR, content, and executive visibility aren’t aligned, you reduce the number of trusted signals AI systems rely on.

How Does One Asset Become Five?

The real value of integration is making one success work four times harder. This helps large companies absolutely dominate their space and lets smaller firms punch above their weight through efficient use of resources.

Here’s what that looks like in practice. Take a single starting point: your company releases original data or research on a trend that matters to your buyers.

•      Earned: The research is pitched to key trade publications and tier 1 business outlets. Stories are published, your CEO is quoted with a distinctive point of view.

•      Owned: The research becomes an un-gated blog post and report on your website, structured with clear headers, FAQ sections, and schema markup so both Google and LLMs can parse it effectively. Key data points are formatted as standalone, citable claims that start showing up in other earned media.

•      Shared: Your CEO and other executives post their own take on LinkedIn — not identical reshares, but distinct perspectives that create multiple entry points for key audiences. The company page amplifies with a summary post linking to the blog.

•      Third-Party/Paid: A LinkedIn sponsored content campaign targets decision-makers in your key verticals. An analyst briefing results in an informed industry expert that validates the narrative for media and prospect inquiries. The research serves as the foundation of a presentation or webinar at an industry event.

Does Integrated PR and Marketing Require a Large Budget?

No. In fact, smaller teams are often better positioned to do this well from day one, because they can’t afford to be spread too thin. Even some larger brands can’t activate all channels at scale, and trying to do everything at a surface level is worse than doing two things well. But whatever you do invest in, do it well, and set your campaigns up to compound across channels rather than exist in isolation. 

A single earned media placement that nobody amplifies, repurposes, or references on your website is a missed opportunity — and that’s true whether your budget is $50,000 or $500,000. A blog post that answers a question your buyers are asking but never gets shared by an executive or promoted to a targeted audience is content that only works in one way, instead of four or five.

Integration is a mindset about how assets get used, not a mandate to spend more. Start with what you have. Make each piece of content and each media win work across every channel you can reach. 

The Outcome: Consistent Presence Where Buyers Look

The B2B buyer’s journey is no longer a path you control. It is now made up of a network of sources — and increasingly, a network that AI tools reference on their behalf.

When PR, content, social, and paid efforts work together, your brand appears more consistently across those sources. That consistency builds consensus  and ultimately, trust.