Earnings are over. The call went well, the board is happy and the team is ready to move on to the next quarter.
Except there’s still the post-earnings cleanup.
Questions and follow-ups need to be logged, contact records need updating, and changes in ownership need to be checked against recent engagement. Notes from the call need to make their way into the CRM. And too often, doing all of that means moving between systems that hold different pieces of the same investor story.
If you've felt more worn out this earnings season than the results justified, it's worth asking why. It's rarely the strategy that wears a team down. It's the administration wrapped around it. The reconciling and the re-entering that come from running a modern IR program on a stack of tools that were never built to talk to each other.
Ten years ago, most IR teams ran on a website, a distribution service, and a spreadsheet. Today the average program touches an engagement platform, a surveillance tool, a CRM, a consensus tracker, a virtual event platform, and a board deck built by hand from all of the above.
Each addition made sense. But over time, many teams ended up with a collection of systems that work well individually and create work between them.
A function that started as one job now runs on a collection of systems, each with its own login, data model, export format, and idea of who a given investor is.
AI is the new layer on top of all this. It becomes an issue when AI gets added as standalone tools as well.
A general-purpose AI assistant might help summarize a transcript or draft a set of talking points. That can be useful. But if it doesn’t have access to the data and context behind the IR program, the team still has to find that information, move it into the tool, provide the context and decide what should make its way back into the systems they use every day.
The individual task may be quicker. The workflow around it hasn’t necessarily improved because in a lot of programs it's just become another source of fragmentation, a bolted-on layer sitting outside the core workflow instead of built into it.
On an ordinary day, moving between systems is frustrating. During a high-pressure moment, it can become a real operational risk.
Say a significant holder increases its position. Surveillance picks up the change, but understanding what that change means could depend on information somewhere else: recent meetings, questions from management, event attendance, previous engagement or a change in sentiment.
The signal itself is useful, but the context around it is what helps the team decide whether it deserves attention.
This applies during earnings prep as well. Analyst commentary, consensus changes, investor questions and previous management responses may all contain clues about what the market is focused on. When that information is scattered, someone has to manually bring it together before the team can make sense of it.
This is where fragmentation starts affecting the quality of the work, rather than simply the amount of time it takes.
It’s easy to look at an IR stack with five or six systems and conclude that the answer is consolidation. But the number of tools only tells you so much.
A team could use several specialized systems that work well together and have relatively little manual overhead. Another could use four systems and spend hours every week moving data between them.
A better measure of complexity is the work that happens between the tools.
Start with four areas:
Duplicate work: Where does the same information have to be entered, checked or updated more than once?
Manual handoffs: Where does someone export a file, copy information, reformat data or move it from one system to another?
Context switching: How many places does someone need to look to answer one common IR question or complete one workflow?
Disconnected signals: Where does useful information exist but remain separated from the context needed to interpret it?
That last one can be particularly important in IR. A change in ownership is one signal, recent meeting history is another and digital engagement is an important one. You could look into them separately, and each could give you some information. But look at them together, and they can give the team a much clearer sense of where attention is needed.
You don’t need to audit every piece of technology your team uses to understand where the problems are. Pick three workflows your team repeats regularly. Investor meeting preparation, earnings preparation and board reporting are good places to start.
Map what actually happens from beginning to end. For each workflow, ask:
You’ll probably find the biggest sources of complexity are usually the small manual handoffs repeated every week, month and quarter, more than the biggest or most expensive tools. That gives you a much more useful baseline for evaluating your stack.
Instead of asking, “How many tools could we get rid of?”, you can ask, “How much work could we get rid of?”
Once you can see where the complexity sits, resist the urge to immediately replace everything. Start with the workflows where fragmentation creates the most work or introduces the most risk.
--> If meeting preparation requires someone to pull information from four places every time, could those data sources be connected so the context is already there?
--> If every interaction has to be manually logged in the CRM, could that record update as part of the workflow?
--> If board reporting starts with several exports and a reconciliation exercise, could more of the underlying information live together?
--> And if you’re introducing AI, can it work with the data and context already inside your IR program, or will your team have to continually bring that context to it?
Your goal should be to eliminate handoffs, duplicate work and unnecessary searches before worrying about the raw number of products in the stack.
IR teams still need specialized capabilities. Surveillance, consensus, relationship management, events and digital engagement all serve different purposes.
The opportunity is to make those capabilities work as part of the same operating model.
When evaluating technology, look at what happens between tasks. Does information captured in one workflow become useful in the next? Can the team see related signals together? Does the system update records automatically where appropriate? Can AI work across the information the team already has permission to use?
Those questions tell you far more about operational simplicity than the number of logos on your technology diagram. Don’t count how many things the technology can do. Look at how many things your team no longer has to.
Want to see where complexity is creeping into your IR workflows? See how the Q4 Platform connects the data, workflows and context your team relies on, so you can spend less time moving information between systems.