For many mid-market leaders, forecasting feels like an impossible balancing act. On one side is the pressure to make decisions quickly in a competitive market. On the other is the reality that the data needed to support those decisions is often incomplete, inconsistent, or difficult to trust. The result is a familiar cycle: leaders delay action while waiting for better information, only to discover that the perfect dataset never arrives. The assumption is that better forecasts require perfect data. In reality, the organizations that forecast most effectively understand a different principle: forecasting is not about perfection. It is about confidence, context, and knowing when additional data will no longer meaningfully improve the decision. The Cost of Waiting One of the most common challenges organizations face is determining whether they have enough information to move forward. According to Altum’s Brendan Polke, that conversation should begin with a simple question: what is the cost of waiting? Organizations often focus on the gaps in their data while overlooking the business consequences of delaying decisions. Every week spent chasing additional information carries a cost, whether in lost revenue, missed opportunities, slower product launches, or competitive disadvantage. As Polke explains, “even if we only hit […]

Over the past decade, companies have invested heavily in data visualization. Power BI, Tableau, and their equivalents are now standard issue across the enterprise. And yet the promise of better decisions hasn’t fully materialized. Not because leadership teams aren’t capable, but because the tools have outpaced the data foundations they depend on. “Companies have spent 10 to 15 years building out visualization tools, but what most of them haven’t done is prioritize proper data organization as part of their corporate DNA,” says Matthew Gantner, Founder and CEO of Altum Strategy Group. “The dashboards look good. The underlying data isn’t structured to give leaders what they actually need.” The issue isn’t the display layer. It’s everything underneath it — data quality, data velocity, KPI relevance, and the organizational discipline to act on what the numbers are saying. Until those foundations are in place, dashboards risk becoming investments that haven’t yet reached their full potential. The partial insight trap Brendan, Client Delivery Manager at Altum Strategy Group, sees the same pattern from the implementation side. Companies default to standard metrics — utilization percentages, headcount ratios, basic financial summaries — often before the contextual modules that make those numbers meaningful have had a […]

For decades, finance teams have measured efficiency by how quickly they could process invoices, reconcile accounts, and close the books. Artificial intelligence is accelerating those tasks dramatically, but according to Altum Strategy Group’s Brendan Polke, that isn’t the most important story. The real opportunity isn’t simply automating accounts payable or accounts receivable. It’s giving finance professionals the time and information they need to create more value for the business. Organizations that approach AI solely as a cost-cutting exercise risk missing its greatest potential. The companies seeing the strongest results are using automation to eliminate repetitive work while allowing finance teams to focus on analysis, oversight, and strategic decision-making. Polke says, “Think of AI as a value-creating opportunity rather than a cost-cutting initiative. It’s a new way to run your financial operations, not simply a way to replace the old one.” Efficiency Is Only the Beginning Much of today’s discussion around AI focuses on speed. Finance teams can process invoices faster, automate data entry, generate reports, and reduce manual errors. These gains are significant, but they’re only the first step. Once organizations are no longer consumed by repetitive administrative work, they can begin asking bigger questions. Why are we paying for […]

Organizations have never had more visibility into their operations. Dashboards track revenue, utilization, customer activity, inventory and workforce performance. Executive teams can access more information, more quickly, than at any point in history. And yet a lot of organizations still struggle to make confident decisions. The problem isn’t a shortage of data. It’s knowing what to do with it. Most companies are reasonably good at measuring performance. Far fewer have figured out how to turn those measurements into foresight, meaning the ability to see where the business is heading before the trend is already in the rearview mirror. As investment in analytics platforms and reporting tools has grown, one thing is becoming clear: dashboards don’t create better decisions on their own. They create visibility. What organizations actually do with that visibility is a different question entirely. Dashboards Provide Signals, Not Answers One of the most persistent misconceptions in performance management is that more dashboards mean better decision-making. It doesn’t. In practice, designing dashboards well is as much an art as a science, and the dashboards that actually serve leaders are built for context, not just metrics. A high-performing employee might show low utilization simply because they complete work faster than […]

For many mid-market companies, data governance becomes a priority only after growth starts exposing operational cracks that can no longer be ignored. Reporting takes too long to reconcile. Different departments produce different versions of the same number. Teams spend more time manipulating spreadsheets than analyzing performance. Executives begin questioning where the data came from instead of what it actually means. At first, organizations work around the friction. Finance teams manually adjust reports before leadership reviews them. Operational teams build tracking systems outside official platforms. Employees create workarounds because the business still needs to move. But eventually growth exposes the underlying problem. According to Brendan Polke, Manager at Altum Strategy Group, the issue is rarely a lack of data. He says, “The data exists, but it’s usually fractured between departments and systems.” This fragmentation increasingly becomes a business issue rather than simply a technical one. As organizations scale, disconnected data environments create operational inefficiencies, slow decision-making and limit the company’s ability to use reporting, forecasting and AI effectively. When Systems Stop Speaking the Same Language Within companies, disconnected systems, implemented at different stages of growth, are rarely fully aligned. Sales forecasting sits inside the CRM. Financial reporting lives in accounting software. […]

Global study finds AI use rising in finance, but skills gaps and trust concerns remain widespread

The Challenge A cybersecurity services company was approaching ERP go-live after completing months of planning, design, and testing. From a program perspective, everything appeared to be in place. The system had been configured, testing had been completed, and teams expected a relatively smooth transition. But as go-live approached, the complexity of the transition became more apparent. Moving from a testing environment into production introduced a new set of challenges. The organization was not simply activating a system, it was transitioning live operations from one environment to another, while continuing to run the business. This created a critical gap. There was a defined cutoff point where transactions could no longer be processed in the legacy system, but the new system was not yet fully operational. During that window, business activity continued – payments were received, invoices needed to be issued, and operational demands did not stop. As one system is shutting down, another is standing up and what happens in between requires significant catch up. At the same time, expectations across the organization were not fully aligned. Many assumed that once testing was complete, go-live would be largely seamless. But moving from a sandbox environment into production requires rebuilding and revalidating […]

The Challenge A digital media and advertising company implemented invoice processing automation to improve efficiency within its finance function. On paper, the solution was working. Invoices could be processed faster. Workflows were structured. The system was capable of reducing manual effort and improving turnaround time. But adoption lagged. Instead of using the system as designed, members of the finance team continued to rely on familiar processes. Spreadsheets remained in place. Workarounds emerged. Tasks were being completed both inside and outside the system, often duplicating effort rather than eliminating it. The issue was not overt resistance. It was behavioral. Old habits die hard and members of the finance team were finding workarounds which meant they were working for the technology, vs. the technology working for them. At the core was a lack of trust. Finance teams, particularly those responsible for accounts payable, had a strong sense of ownership over their work. Accuracy mattered. Control mattered. The idea of relying on automation, even when validation steps were built in, created hesitation. Rather than trusting the system and verifying outputs, individuals continued to perform the work manually, then used the system to confirm what they had already done. This not only slowed adoption […]

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The Challenge A cybersecurity services company was closing the books and seeing deferred revenue balances climb without a clear explanation. The numbers didn’t add up. Financial reporting required increasing levels of manual adjustment. Leadership lacked clear visibility into what was driving the numbers. In one instance, the organization identified millions of dollars in deferred revenue without a clear explanation. The issue was not tied to a single transaction or project. It was systemic. At its core, the challenge stemmed from gaps in data, process, and system usage. Revenue recognition in POC environments depends on having a complete and accurate view of each project – when revenue is earned, when it should be recognized, and how it aligns to project completion milestones. Without that full picture, the system cannot apply revenue recognition correctly. Without proper data, the full story wasn’t there to determine how revenue should be recognized. At the same time, the organization was not consistently using the system to manage these processes. Instead, teams relied on spreadsheets to track revenue and used journal entries to adjust results within the system. These adjustments were intended to align outputs with expectations, but over time they created discrepancies, particularly in deferred revenue […]

Introduction For a growing retail and restaurant company, accounts payable and receivable were functional, just not functioning well. Invoices moved. Payments were made. But underneath the surface, a significant share of transactions required manual intervention, and that manual work was consuming time and money the business could not afford to keep losing. When Altum Strategy Group was engaged to assess and improve the AP/AR function, what emerged was not simply a process problem. It was a data problem, a controls problem, and ultimately a financial exposure problem that AI was uniquely positioned to solve. The Challenge The company’s AP/AR operations ran on a combination of manual lookups, spreadsheet edits, and file uploads that bypassed the core system entirely. Approximately 20% of all invoices — both inbound and outbound — were edge cases the system could not process automatically. The causes varied: mismatched part numbers, invoice totals that did not reconcile, and supplier entries appearing under multiple names across different distribution entities. Each exception required a staff member to investigate, make a judgment call, and manually correct the record outside the system before re-uploading it. The result was a processing backlog that had grown well beyond acceptable limits. The company’s target […]

Introduction In a high-volume, high-velocity, low-margin business, the finance function cannot afford to be slow. Every day the books stay open is a day leadership is flying without instruments. For a growing restaurant distribution company, that was the reality. Their accounting and operational systems had not kept pace with the business, and the gap was being filled by something no CFO wants to depend on: human heroics and Excel. Altum Strategy Group was brought in to change that — and over three years, we did. The Challenge The company had grown significantly, but its financial infrastructure had not grown with it. Reporting relied on manual effort across fragmented, siloed systems; at least ten separate applications that could not talk to each other and could not provide the unified view that leadership needed to run the business. Getting a clear picture of financial health required considerable manual intervention every reporting cycle. The consequences were predictable. The monthly close took 30+ days. Operational visibility was inconsistent. Headcount planning across locations was difficult to execute with confidence. Leadership could not easily identify where operations were overperforming, where they were underperforming, or where attention was most needed. The finance function was consuming capacity that […]