Commercial banks don’t need another tool to replace what’s already working. They need an intelligence layer that makes every other tool work better.
If you work in commercial banking today, chances are your technology stack is already quite substantial. Between CRMs, BI dashboards, integration middleware, and the spreadsheets most teams still rely on, there’s no shortage of software for you to manage in your day-to-day work.
Most of it is doing exactly what it was designed to do: tracking client interactions, managing cash positions, and reporting on what happened quarter over quarter. Increasingly, AI tools are also making routine tasks more manageable and efficient, from drafting client communications to summarizing industry research. Each piece of the modern banking tech stack plays a specific, important role.
But, if you ask any relationship manager whether they walk into client meetings knowing exactly what’s happening inside that client’s business – where working capital gaps are forming, which suppliers are being paid late, or what share of spend could move to a more efficient payment method – the answer is almost always no. They don’t lack in technology, but none of it was built to meet that particular need.
Today, that gap is filled by advisory intelligence. Rather than a replacement for any of the tools banks already use, advisory intelligence is the insights layer that sits alongside and above them, turning raw client financial data into specific, forward-looking recommendations that bankers can act on right away. In short, it equips every other tool in a bank’s tech stack with better, smarter insights.
Understanding exactly where advisory intelligence fits and exactly what it does and doesn’t do is the first step toward putting it to work.
Overview of the modern banking tech stack
Commercial banks have invested many years and significant resources into assembling the technology that supports their daily operations. For most large banks, the core set includes all or some combination of the following:
- Customer relationship management (CRM) platforms like Salesforce that manage client relationships and track pipeline activity, often layered with banking-specific tools like nCino for lending workflows and Moody’s Creditlens for credit risk
- Revenue intelligence tools that analyze sales conversations, improve forecasting, and coach reps on deal execution
- Business intelligence (BI) and analytics platforms that report on portfolio performance, product usage, and historical trends
- Integration platform as a service (iPaaS) and other integration tools like MuleSoft or Workato that connect different systems and automate data flows across the internal tech stack
- Embedded ERP platforms like Fispan that integrate a bank’s payment and cash management capabilities directly into clients’ ERP and accounting systems
- Spreadsheets through Microsoft Excel or other programs, which are used by nearly every banking team for client prep, ad-hoc analysis, internal reporting, pricing models, and more
- AI agents and large language models (LLMs) that are increasingly used to accelerate research, summarize data, draft communications, and support a growing range of day-to-day tasks
These tools have more than earned their place. Most banks aren’t looking to rip them out, and for good reason. They’re deeply embedded in banks’ existing processes, compliance frameworks, and workflows, and each was selected for a specific purpose. The question isn’t whether they’re working – in most cases, they are – it’s whether they’re working together and with the best possible data behind them to give bankers the full picture they need.
Upon close inspection, there’s a consistent gap across these tools that becomes clear once you examine what each was designed to do – and, just as importantly, what it was never designed to do.
CRMs and pipeline management
CRMs form the backbone of relationship management across virtually every sales-driven organization, including commercial banks. In most banking environments, Salesforce serves as the core CRM, often extended through specialized layers like nCino (which manages lending deal workflows and credit origination) and Moody’s Creditlens (which supports credit risk assessment and portfolio monitoring). Together, these tools track client contacts, log interactions, manage deal stages, and provide a centralized record of a bank’s engagement across each account.
For commercial banking teams, CRMs serve an important organizational function. They help managers understand which accounts are being actively worked, where in the pipeline opportunities might emerge, and which relationship managers are engaged with which clients. They’re especially useful for ensuring nothing falls through the cracks at the activity level.
What a CRM can’t do is tell a banker about a client’s actual business.
A CRM knows that an RM had a call with a client last Tuesday, for example, but it doesn’t know that the client’s accounts payable cycle has shifted by 15 days over the past quarter, or that 40% of their supplier payments are on terms that would convert well to a commercial card program. That kind of intelligence requires a data source that starts with the client’s own financial systems, rather than the bank’s internal activity logs.
In short, CRMs capture what banking teams are doing, not what the client’s business is actively experiencing.
Revenue intelligence
Revenue intelligence platforms have become standard tools for sales organizations that want to understand their pipeline health, improve their forecasting accuracy, and coach their sales reps on what’s working and what’s not. Some commercial banking teams have adopted similar tools (whether off-the-shelf platforms or internally built equivalents) to bring more rigor to their own sales processes across treasury, commercial cards, and more.
These tools are genuinely valuable for what they do. They can flag that a deal is losing momentum, highlight which talk tracks are correlating with higher close rates, and give sales leaders a clearer picture of where the quarter and year are headed.
Revenue intelligence is built entirely on seller-generated data like CRM records, sales call recordings, email threads, and pipeline stages. It looks inwards and analyzes a bank’s own activity to answer internal questions about sales performance. It does not look outwards to answer questions about what’s happening inside a client’s business, because it has no access to that external data. For example, a revenue intelligence platform could tell a banker that a particular deal hasn’t been touched in two weeks, but it can’t tell them that a specific client has an emerging working capital gap or a cardable opportunity that could warrant a sales conversation. That’s where advisory intelligence comes in.
Business intelligence and analytics
Most commercial banks use some form of BI and analytics tooling to generate reports on portfolio performance, product penetration, transaction volumes, and other metrics. These tools aggregate internal data and present it via dashboards or traditional reports that inform strategic decision-making at the leadership level.
BI platforms are useful for understanding where the bank has been and how it has performed in the past. For example, they can show a team lead that commercial card adoption has grown by 12% year over year or that a specific segment of their portfolio is underperforming on treasury product penetration. That kind of retrospective analysis has real value when it comes to planning and resource allocation.
What BI platforms typically cannot do is surface forward-looking, client-specific opportunities from real-time financial data. They look at a bank’s historical client records – including which products a client has used, which transactions have cleared, or what their average balance was last quarter – but they don’t consider what’s actively happening inside the client’s business today.
Advisory intelligence works from a fundamentally different dataset and in a fundamentally different timeline. It draws from continuously updated, transaction-level financial data from a client’s ERP and accounting systems, so it gives bankers on-demand access to forward-looking insights they can pull just before a client meeting – rather than having to wait for the next quarterly, retrospective report. Those insights might include shifts in supplier payment patterns, emerging cash flow gaps, and spend signals that are ripe for a card conversion.
iPaaS and integration platforms
iPaaS and other integration platforms like MuleSoft, Workato, and Boomi play an important role in the modern banking tech stack. They connect isolated internal systems, automate the flow of data between different applications, and help ensure that information moves smoothly and efficiently across the bank’s tech infrastructure. Put simply, they’re designed to move data from point A to point B.
For banks, iPaaS tools are a bit like plumbing. They’re responsible for syncing data between the CRM and the core banking system, automating reporting feeds into the BI dashboard, and ensuring that client records stay consistent across multiple internal platforms.
What iPaaS tools don’t do is standardize, interpret, enrich, or analyze the data they move.
An integration platform can transfer a file from one system to another, but it can’t tell a banker what that file means for a specific client relationship. It can’t categorize a client’s supplier spend into banking-relevant opportunity areas, infer payment behaviors from raw transaction records, or surface a recommendation that a particular account is a strong candidate for a commercial card program.
Because both iPaaS and advisory intelligence tools deal with data, the two are sometimes conflated in the market, but they serve fundamentally different purposes. iPaaS is an infrastructure layer that connects internal systems and moves data between them. Advisory intelligence is an insights layer that connects to clients’ external financial systems, enriches their data, and generates specific, actionable recommendations for bankers. A bank doesn’t need an iPaaS tool to use advisory intelligence, and an iPaaS tool alone can’t deliver the kind of client-level analysis that advisory intelligence provides. That said, for banks that do already use an integration platform, advisory intelligence can add an analytical engine that turns their connected data into banking-specific insights bankers can put to work.
Embedded ERP banking
This is the category where the distinction between advisory intelligence and existing banking tools becomes especially important to understand. These embedded platforms work with the same client systems that advisory intelligence draws data from (ERPs and accounting software), but they serve an entirely different purpose.
Embedded ERP banking tools like Fispan enable banks to integrate their payment, cash management, and treasury capabilities directly into clients’ ERP or accounting environments, including platforms like NetSuite, Sage Intacct, and Microsoft Dynamics 365. In practice, that means a business client can initiate payments, manage their accounts payable, view balances, and reconcile transactions without ever leaving their accounting software – because the bank’s core capabilities are embedded and delivered within their accounting flows instead of through a separate banking portal. This is valuable because it streamlines payment execution, reduces manual steps, and strengthens a bank’s position within a client’s financial operations.
With embedded ERP banking tools, banking data moves into the client’s ERP. Advisory intelligence works in the opposite direction. It pulls the client’s financial data out of the ERP and transforms it into specific insights that bankers can act on.
Embedded ERP banking tools and advisory intelligence aren’t competing tools, but complementary ones. In fact, advisory intelligence can help drive adoption and usage of a bank’s embedded banking products.
While an embedded ERP banking tool can help a client pay suppliers more efficiently, advisory intelligence can identify which suppliers represent the strongest opportunities for a bank’s embedded products and quantify the potential value of those conversions. In other words, advisory intelligence doesn’t just sit quietly alongside embedded banking infrastructure, but actively helps banks get more value from it.
Spreadsheets and manual analysis
No discussion of the banking tech stack is complete without acknowledging the tool that still underpins more day-to-day banking work than any other: spreadsheets.
For many relationship managers, treasury officers, and card sales specialists, spreadsheets are where the real work happens. They’re used to prepare for client conversations, build one-off analyses, track opportunities, format reports, and organize the data that flows (or doesn’t) between other systems. Platforms like Microsoft Excel are flexible, familiar, and universally available for most bankers, which is why they’ve had staying power even as banks invest in more sophisticated tooling.
The challenge with spreadsheets, of course, is that they require manual effort at every step. Someone has to gather the data, clean it, format it, interpret it, and keep it up to date. For a banker managing a handful of large corporate accounts, that might be feasible. For a banking team responsible for hundreds or thousands of mid-market relationships, it’s not. Trying to make it work is where opportunities slip through the cracks.
Bankers will always reach for spreadsheets – and advisory intelligence isn’t out to replace them.
What advisory intelligence does is eliminate the most time-consuming part of the spreadsheet process, which is gathering, structuring, and interpreting the underlying client data. With advisory intelligence, the insights that would previously take days of manual analysis to compile are available on demand, and they can be delivered in formats bankers are already comfortable with, including downloadable reports and data exports they can pull directly into their own spreadsheets.
AI agents and LLMs
AI tools are becoming a meaningful part of the way bankers work. Whether it’s using an LLM like ChatGPT or Claude to draft a client email, summarize a research report, or analyze a document, more and more banking professionals are incorporating AI models and agents into their daily workflows.
This trend is both real and accelerating. But it raises an important question: What data are these AI tools working with in the first place?
An AI agent can draft a compelling client pitch, but only if it has access to relevant, accurate, client-specific financial data to ground its output. Without that, the result is likely a generic template that sounds good but doesn’t reflect what’s happening in the client’s business. The difference between a banker walking into a meeting with a standard AI-drafted summary versus tailored, data-backed recommendations is the quality and specificity of the data feeding the AI.
This is where advisory intelligence can play a foundational role. By structuring, enriching, and delivering client financial data in a format that’s purpose-built for banking use cases, advisory intelligence provides the high-quality input layer AI tools need to generate genuinely useful, client-specific outputs. Advisory intelligence doesn’t compete with AI agents, but it does make them significantly more effective.
Where advisory intelligence fits in
If you map these different tools across the commercial banking tech stack, a clear pattern emerges.
Every existing tool category serves one of several functions: it either manages the bank’s internal operations, tracks the bank’s own sales activity, connects the bank’s systems and data, or helps bankers work more efficiently with the information they already have. Not one was built to look outward at clients, tell a banker what’s happening inside each of their clients’ businesses right now, or analyze what that means in terms of specific growth opportunities.
That’s why advisory intelligence occupies a distinct position in the banking technology stack that no other tool category fills.
| Tool category | What it does well | What it doesn’t do |
| CRM & pipeline management | Tracks banker-to-client interactions, manages deal stages and pipeline activity, and centralizes all contact and account information | Cannot reveal what’s happening inside a client’s business financially, as it only tracks a bank’s internal activities and touchpoints |
| Revenue intelligence | Analyzes sales conversations, improves forecasting accuracy, and coaches sales reps on deal strategy and execution | Runs on seller-generated data like sales calls, emails, and pipeline trajectory, so has no visibility into clients’ financial health, spend patterns, or working capital positions |
| BI & analytics | Produces retrospective reports analyzing historical portfolio performance, product usage, transaction volumes, and trends | Works backward from what already happened based on historical data; cannot surface forward-looking, client-specific opportunities from real-time financial data |
| iPaaS & integration | Connects disparate banking systems, automates data flows between different applications, and keeps internal tools in sync | Moves data between systems but doesn’t standardize, interpret, enrich, or analyze it; provides fundamental plumbing without intelligence on top |
| Embedded ERP banking | Embeds a bank’s payment, cash management, and treasury capabilities directly into clients’ ERP and accounting systems, streamlining payment execution and reconciliation | Pushes banking data and services into the client’s ERP but doesn’t pull client data out for analysis; facilitates transactions rather than generating insights from them |
| Spreadsheets & manual analysis | Flexible, familiar, and universally available as the default tool for ad-hoc analysis, client prep, and reporting across banking teams | Requires gathering, formatting, and interpreting data by hand; can’t scale across a large book of business or deliver real-time, automated insights |
| AI agents & LLMs | Accelerates research, summarizes information, drafts communications, and supports a growing range of day-to-day banking tasks | Only as good as the data it can access; without structured financial data, outputs are generic rather than client-specific and actionable |
| Advisory intelligence | Turns raw, on-demand client financial data from ERPs and accounting systems into specific, forward-looking recommendations that bankers can act on right away | Not a replacement for any of the above; operates as a distinct intelligence layer to fill gaps between existing tools and drive the consultative conversations banks want to have with their clients |
The critical point is that advisory intelligence is not competing with any of these banking tools for the same job. A bank wouldn’t choose between its CRM and advisory intelligence any more than it currently chooses between its BI dashboards and spreadsheets. Each piece in the stack answers a different question and fills a different need.
Advisory intelligence simply answers the question that no other tool can: What should we recommend to this specific client, based on what’s happening inside their business today?
How Codat works alongside existing banking tools
Codat is the only comprehensive advisory intelligence solution purpose-built for commercial banking. From the start, Codat’s platform was designed to be additive to the technology banks already use, not replace it.
In practice, this means Codat sits inside the ecosystem a bank has already built. Via a secure API, Codat connects directly to clients’ ERP and accounting systems, pulls their financial data in real time with their explicit consent, enriches it with banking-specific and contextual analytics, and delivers actionable insights that bankers can put to work immediately within their existing workflows.
The output isn’t a new system to learn, but a deep intelligence layer that makes every other system in the stack more valuable and more effective.
- Your CRM goes from an interaction log to a strategic account management tool, because bankers can pair their pipeline activity data with real-time insights into each client’s financial position and product-level opportunities.
- Your BI dashboards documenting historical portfolio performance are supported by forward-looking, client-level insights that help teams act on opportunities before they show up in a quarterly report.
- Your integration platforms continue to keep systems in sync while advisory intelligence adds the analytical and interpretive layer that turns connected data into banking-specific recommendations.
- Your embedded ERP banking tools gain a strategic driver that identifies which clients represent the strongest opportunities for payment, card, and treasury products, helping your team boost adoption and deliver more value.
- Your spreadsheets get fed with structured, on-demand insights that eliminate the manual data gathering and formatting that currently consumes hours of a banker’s week.
- Your AI models and agents get the high-quality, structured, client-specific data they need to generate reliable outputs that are genuinely useful instead of generic.
The bottom line? Advisory intelligence isn’t another platform to manage. It’s an infrastructure layer that elevates the entire banking stack.
See what advisory intelligence can do for your bank
Your tech stack already does a lot. But advisory intelligence can make it do more.
For commercial banks that want to move beyond managing activity and start driving more consultative, data-backed conversations across their entire portfolio, Codat’s advisory intelligence is the infrastructure that makes it possible.
Explore Spend Insights and Working Capital Insights to see our platform in action, or get in touch with our team to discuss your specific needs.