According to Gartner, 64% of financial decisions are now powered by data, yet only 9% of finance professionals fully trust the financial data they rely on. That gap between how much organizations depend on their data and how much they trust it is one of the most consequential problems in finance today, and it rarely gets the urgency it deserves.

Why Financial Data Quality Problems Are So Common

Most companies do not lack data. They lack agreement on what the data means and confidence that it is current and complete. Numbers live in the ERP, the CRM, and a handful of spreadsheets, with each system carrying its own definition of basic terms such as revenue, headcount, or customer. When someone asks a simple question, getting a reliable answer requires pulling from multiple sources and reconciling them by hand.

About 83% of financial institutions lack real-time access to transaction data and analytics due to fragmented systems. That fragmentation extends well beyond financial institutions. Most mid-market companies have grown through new systems, new acquisitions, and new reporting requirements without ever building a coherent data foundation underneath all of it.

What Good Financial Data Actually Looks Like

Good financial data has a few defining characteristics, and none of them require a massive technology overhaul to achieve. Every key term, such as revenue, gross margin, or headcount, has a single agreed-upon definition that holds true whether the number shows up in the board deck, the CRM, or the finance team’s internal model. Each metric also has one trusted source rather than five competing versions, so everyone in the organization knows exactly where to look for the official number.

Good data is accessible without requiring a spreadsheet expert and an afternoon to retrieve, and it is current enough to inform the decisions it is meant to support. Data that is accurate but six weeks old does little to help a decision that needs to happen this week. Finally, good data infrastructure is documented well enough that it survives staff turnover. When the person who built a report leaves the company, the definitions, sources, and processes behind it should not disappear with them.

This Is a Business Problem, Not Just an IT Problem

Many organizations route data quality issues to IT or a BI team by default. That approach misses the root cause. Data inconsistency in finance is almost always a business definition problem before it becomes a technology problem. Two departments calling the same metric by different names, or calculating it with different assumptions, will not be resolved by a new dashboard tool on its own.

Fragmentation has become a strategic constraint that affects how quickly an organization can innovate, comply, and compete, extending well beyond a back-end technical issue. The companies that make real progress on data quality start with the business questions of which metrics matter, how each should be defined, and who owns it. The technology decisions follow once those questions are answered.

The Cost of Leaving It Unresolved

Research shows that employees can waste up to 27% of their time dealing with data issues, including validating, correcting, and searching for accurate information. For a finance team, that is nearly a third of total capacity spent on work that adds no analytical value.

The less visible cost shows up in decision quality. When leadership cannot fully trust the numbers in front of them, decisions slow down, get second-guessed, or get made on incomplete information. More than a quarter of data and analytics professionals cite poor data quality as a barrier to data literacy, with some organizations estimating losses exceeding five million dollars annually as a result. Those losses rarely show up as a single line item. They accumulate through delayed decisions, duplicated work, and a leadership team that has learned to be skeptical of its own reporting.

How to Fix It Without a Massive IT Project

Solving financial data quality problems does not require ripping out every system the organization runs on. It requires a structured approach that starts with definitions and ownership before it touches technology. The first step is identifying the handful of metrics that matter most and agreeing on a single definition for each one. The second is establishing a clear source of truth for each metric. The third is documenting the process well enough that it survives staff turnover.

This work involves designing the underlying data infrastructure, building governance frameworks, and establishing master data management so the numbers feeding into dashboards and AI tools are something leadership can actually rely on. Without that foundation, even the most sophisticated analytics or AI investment will produce outputs nobody trusts.

Key Takeaway: Financial data quality problems are usually a business definition issue before they are a technology issue. Fixing them starts with agreeing on what the numbers mean and where they live, not with buying new software.

Struggling with inconsistent data across your finance function? Let’s talk about what a clean data foundation looks like for a company like yours.

The post What Good Financial Data Actually Looks Like (And Why Most Companies Don’t Have It Yet) appeared first on The Alliance Group.

After months of planning, configuration, testing, training, and change management, the new ERP system is finally operational. The project team celebrates. Leadership breathes a sigh of relief. The implementation is officially complete.

And then something unexpected happens.

Six months later, employees are still using spreadsheets.

Reports still require manual manipulation.

Processes take longer than expected.

Users are frustrated.

Leadership starts by asking a difficult question: If we invested all this time and money into a new ERP system, why aren’t we seeing the results we expected? The truth is that this experience is far more common than most organizations realize.

Many ERP implementations successfully go live but fail to fully deliver on the business outcomes that justified the investment in the first place. The good news is that in most cases, the problem is fixable.

Go-Live Is a Milestone, Not the End of the Journey

One of the biggest misconceptions surrounding ERP implementations is that success is defined by whether the system launches on schedule.

In reality, go-live is only the beginning.

The true measure of success is whether the organization achieves the business objectives that drove the project.

Those objectives often include:

• Improved reporting
• Greater operational visibility
• Process efficiency
• Reduced manual effort
• Better decision-making
• Stronger controls
• Scalability for future growth

A system can technically go live while still falling short of these goals.

Unfortunately, many organizations discover this only after the implementation team has moved on and day-to-day operations resume.

The System Works. The Processes Don’t.

One of the most common post-go-live challenges has little to do with the technology itself.

Instead, the issue is often process design.

Many organizations implement new software while carrying over old processes, approvals, workarounds, and habits from legacy systems.

The result is a modern ERP supporting outdated ways of working.

Common symptoms include:

• Duplicate data entry
• Manual reconciliations
• Excessive approvals
• Spreadsheet-based reporting
• Workarounds outside the system
• Inconsistent workflows across departments

In these situations, the ERP may be functioning exactly as designed.

The business processes simply were not optimized alongside the technology.

Reporting Still Requires A Lot of Manual Work

For many finance and operations leaders, reporting is where disappointment becomes most visible.

One of the primary reasons companies invest in ERP platforms is to improve access to timely, reliable information.

Yet many organizations continue to spend significant time:

• Exporting data into Excel
• Combining reports manually
• Reconciling conflicting information
• Building custom spreadsheets
• Creating management reports outside the ERP

When this happens, leadership often concludes that the system isn’t working.

In reality, the issue may be related to reporting configuration, data structure, user adoption, process design, or dashboard development.

The ERP contains the information. The organization simply hasn’t unlocked its full potential.

User Adoption Is Lower Than Expected

Technology alone does not create transformation.

People do.

Even well-designed ERP implementations can struggle when users are not fully trained, engaged, or confident in the system.

Common warning signs include:

• Employees reverting to spreadsheets
• Inconsistent data entry
• Resistance to new workflows
• Limited use of available functionality
• Departments creating alternative processes

These behaviors often emerge because users don’t fully understand how the system supports their work or because training is focused primarily on transactions rather than business outcomes.

Without ongoing support and reinforcement, adoption challenges can persist long after go-live.

The Business Changed, but the System Didn’t

Another common issue occurs when the organization evolves after implementation.

A company may have:

• Acquired another business
• Added new products or services
• Expanded geographically
• Changed operating models
• Experienced significant growth

What worked during implementation may no longer align with the current needs of the business.

In these situations, leaders sometimes assume the ERP was implemented incorrectly.

More often, the business has simply outgrown certain design decisions and configurations.

The solution is optimization, not replacement.

How Do You Know Whether You Need a Quick Fix or a Bigger Overhaul?

Not every ERP issue requires a major project.

In fact, many post-go-live challenges can be addressed through targeted optimization efforts.

Signs you may need focused enhancements include:

• Reporting limitations
• Workflow inefficiencies
• User adoption challenges
• Dashboard improvements
• Minor configuration adjustments
• Process refinements

However, broader intervention may be necessary when:

• Core business processes are misaligned
• Data quality issues are widespread
• Critical functionality was never implemented
• Significant work continues outside the system
• Multiple departments are struggling to achieve expected outcomes

The key is understanding the root cause before investing in additional technology or customization.

Many organizations spend money addressing symptoms rather than solving the underlying problem.

Why Post-Go-Live Optimization Often Delivers the Greatest ROI

ERP implementations understandably focus on achieving a successful launch.

That means many valuable enhancements are deferred until after the system is operational.

Once users gain real-world experience, opportunities become easier to identify.

Organizations often discover ways to:

• Eliminate manual work
• Improve reporting visibility
• Streamline workflows
• Automate approvals
• Increase user adoption
• Strengthen controls
• Enhance decision-making capabilities

In many cases, these post-go-live improvements generate more measurable business value than the initial implementation itself.

The organizations that achieve the greatest return on their ERP investment are often those that view optimization as an ongoing process rather than a one-time project.

You Probably Don’t Need a New ERP

When frustration builds, leadership sometimes begins questioning whether the wrong system was selected.

While system replacement is occasionally necessary, it is far less common than many organizations assume.

More often, the ERP is capable of delivering the desired outcomes.

The challenge lies in configuration, process alignment, reporting design, user adoption, governance, or system utilization.

Before considering another major technology investment, organizations should first evaluate whether they are fully leveraging the capabilities they already have.

The answer is frequently no.

How Alliance Can Help

Alliance helps organizations maximize the value of their ERP investments through our Business Systems and Transformation services. Our team works with finance, operations, and technology leaders to assess system performance, identify root causes of post-go-live challenges, improve reporting and workflows, strengthen user adoption, and align ERP capabilities with business objectives.

Whether your implementation is six months old or several years old, we can help determine what’s working, what’s not, and where the greatest opportunities for improvement exist. Schedule a system health check with our team to identify opportunities to improve performance, reduce manual effort, and maximize your ERP investment.

The post Your ERP Is Live. Now What? Why So Many Implementations Disappoint After Go-Live appeared first on The Alliance Group.

Statistics show that 68% of CFOs say they have been slow to adopt AI because they do not know where to start, which is a more honest answer than most of the AI coverage in the market gives them credit for. The technology is moving fast, the vendor claims are loud, and the practical guidance for finance leaders has not kept up.

Here is what’s really happening inside finance teams today, without the hype.

Where AI Is Genuinely Delivering

The most important thing to understand about AI in finance right now is that the real wins are concentrated in specific, well-defined tasks, not sweeping transformation. The organizations seeing results are not trying to automate the entire finance function. They are identifying the processes where AI removes the most friction and starting there.

Accounts payable and transaction processing

Automated invoice matching, approval routing, and exception flagging have been the highest-adoption AI use case in finance for good reason. The top AI use cases running in finance teams include accounts payable automation at 37% and error and anomaly detection at 34%. These are high-volume, rule-based processes where AI reduces manual effort without requiring the organization to have perfectly clean data or a sophisticated AI infrastructure underneath it.

Management reporting and variance analysis

Management reporting and variance analysis is cited as the fastest-paying AI use case, with a three-to-six-month payback period. AI tools that pull data from multiple sources, surface variances automatically, and generate narrative summaries of results are compressing work that used to take finance teams days into hours. For CFOs who have long wanted their team to spend more time on analysis and less on report production, this is where AI is delivering a visible and measurable return.

Forecasting and scenario modeling

Finance teams are using AI to run scenario models faster, update forecasts more frequently, and surface signals in the data that manual analysis would miss. The human judgment still lives with the finance leader. What changes is the speed and depth of the analysis they can bring to a decision. Teams that previously updated their forecast monthly are building the infrastructure to do it continuously, and AI is what makes that operationally feasible.

Anomaly detection and controls monitoring

AI-powered tools that flag unusual transactions, identify patterns inconsistent with normal activity, and surface potential errors before they reach the close are adding a layer of oversight that manual review alone cannot provide at scale. For finance leaders managing growing transaction volumes with flat or shrinking teams, this is where AI reduces risk rather than just reducing effort.

Where the Hype Still Outpaces the Reality

Honest AI adoption requires being just as clear about what does not work yet as what does.

Fully autonomous financial close, AI-generated financial statements without human review, and end-to-end agentic finance workflows are all areas where vendor demonstrations are ahead of practical deployment. The technology exists in controlled environments. It does not yet exist reliably in the messy, multi-system, historically inconsistent data environments that most mid-market finance teams operate in.

45% of finance teams remain in limited pilot mode, with only 17% using AI in core workflows, and the most common barrier is not a lack of willingness. It is data readiness. AI tools are only as good as the data they are trained on and connected to, and most finance environments have years of inconsistent data, fragmented systems, and undocumented processes that create real limits on what AI can reliably do right now.

The organizations that move fastest on AI are not the ones with the most ambition. They are the ones who invested in getting their data infrastructure right first.

The Real Question for Finance Leaders

The question most CFOs should be asking is not “should we adopt AI?” The truth is, AI adoption across finance departments has reached 97%, up from 76% in 2025, with more than 75% of AI investments already generating positive returns within 12 months. The adoption question is largely settled. The question that requires thought and guidance is where to start, in what sequence, and on what foundation.

Starting with the highest-visibility AI use case is rarely the right answer. Starting with an honest assessment of where the biggest friction points are, what the data looks like, and what the team has the capacity to absorb is how organizations build AI adoption that compounds over time rather than stalls after the first pilot.

The Alliance Group’s AI Readiness Rapid Assessment is designed exactly for this moment. In three weeks, it delivers a diagnostic across people, process, data, and technology, a findings report with prioritized recommendations, and a 90-day sprint plan that tells leadership not just where AI can add value, but what needs to be in place first to capture it. The output is a decision-ready roadmap, not a slide deck full of possibilities.

Optimism about AI grows significantly with maturity, with 23% of organizations further along in adoption describing themselves as much more optimistic compared to just 7% of those just starting out. The organizations that are most confident about AI are the ones that moved past the conversation and started somewhere specific. That is the clearest signal available about how to approach this.

Key Takeaway: AI in finance is delivering real results in specific, well-defined areas right now. The finance teams winning with it are not doing everything at once; they are starting with the right use cases, on the right data foundation, with a clear roadmap for what comes next.

Curious what AI could realistically do for your finance team? Schedule a conversation with our AI and Analytics team.

The post AI in Finance: What It’s Actually Doing Inside Finance Teams Right Now appeared first on The Alliance Group.

Approximately 55% to 75% of ERP implementations fail to meet their objectives. That figure is not driven by bad software. It is driven by decisions made before implementation began, specifically in the selection process, where the wrong criteria get prioritized and the right questions never get asked.

Why ERP Selection Goes Wrong

The ERP market is designed to sell software, not to help you choose the right one. Vendors invest heavily in demos that showcase what the platform does best, which is rarely an accurate representation of how it performs in your specific environment. By the time a company realizes the gap between the demo and its day-to-day reality, the contract is signed and the implementation is underway.

Most selection processes also default to feature comparison. Teams build requirement lists, score vendors across categories, and choose the platform with the highest combined score. The fundamental flaw to that approach is that it treats ERP selection as a product decision when it is actually a business transformation decision. The technology is almost always the least complicated part of the problem.

40% of organizations underestimate staffing requirements and another 40% discover organizational issues that should have been obvious from the start, which reflects how consistently the non-technical factors get underweighted during selection.

What to Get Right Before You Sign

Most ERP decisions are made on incomplete information, optimistic estimates, and vendor-driven timelines. These are the areas that deserve the most scrutiny before any contract is signed.

Understand your processes before you evaluate platforms

The most expensive ERP customizations trace back to a single root cause: the organization selected a system before fully understanding how its own processes worked. Every customization added to close a gap between the software and the business adds cost, extends timelines, and creates a maintenance burden that compounds for years.

A structured current-state assessment, done before vendor conversations begin, surfaces the requirements that matter and separates the real needs from the perceived ones. It also creates the documentation that vendors need to give you an accurate scope and cost estimate, rather than the optimistic version they default to in a competitive selection process.

Evaluate fit, not features

The most capable ERP platform is not automatically the right one for your business. Fit depends on your industry, your transaction complexity, your reporting requirements, your integration landscape, and where you plan to be in five years. A platform that requires significant customization to match your current processes is not a good fit, regardless of how impressive it looks in a demo.

Mid-market companies, in particular, often get sold enterprise-grade platforms that carry enterprise-grade implementation complexity and cost, when a more targeted solution would have delivered better outcomes faster. The question is not which platform has the most functionality. The question is which platform requires the least compromise between what the software does and how your business operates.

Get independent guidance on the selection itself

There is an inherent conflict of interest when the firm helping you select an ERP is also the firm that will implement it. Implementation revenue is significant, and that creates pressure, whether conscious or not, to recommend the platform the firm knows best rather than the platform that fits the client best.

The selection process should be structured and objective, with the client’s requirements driving the evaluation rather than vendor relationships or implementation preferences. Alliance does not own software licenses or resell platforms, which means the guidance is not influenced by which system generates the most downstream implementation work.

Scope the implementation realistically before you commit

One of the most reliable warning signs in an ERP selection process is a vendor or implementation partner that produces an unusually low cost and timeline estimate. Those estimates are designed to win the deal. The change orders come later.

Half of ERP projects require additional technology that was not included in the original plan, and budget overruns frequently exceed the original project cost by a significant margin. Realistic scoping requires an honest assessment of data migration complexity, integration requirements, customization needs, user training, and organizational change management, all of which get underestimated far more often than they get overestimated.

Plan for the people side, not just the technology

Organizations that engage ERP consultants report an 85% success rate in their implementations, compared to significantly lower rates for those that do not, and a large part of that difference comes down to how well the organizational change was managed. New software does not change behavior. A structured adoption plan does.

The companies that implement ERP successfully invest in understanding how the system will change the way people work, and they build a communication and training strategy around that understanding before go-live, not after. The ones that struggle treat training as a checklist item to be completed in the final weeks of the project.

The Decision Most Companies Get Wrong

The choice to use the same firm for both selection and implementation is the single decision that most consistently compromises the integrity of the selection process. Objectivity in selection requires independence from implementation incentives. When those are the same party, the selection becomes less about fit and more about what is easiest to sell and deliver.

An independent assessment of ERP fit, conducted before any platform is chosen and any implementation partner is engaged, gives leadership a defensible basis for the decision and a realistic picture of what the project will require. That clarity is worth considerably more than the time it takes to get it.

The ERP selection process covers needs assessment, structured RFP development, vendor evaluation, and negotiation support, with the full implementation lifecycle available once the right platform is confirmed. The approach is built around the client’s requirements, not the vendor’s preferred outcome.

Key Takeaway: ERP selection mistakes are almost always made before implementation begins. Getting the selection process right, with independent guidance and realistic scoping, is the most important investment a company can make before signing any contract.

Not sure which ERP is right for your business? Schedule an independent ERP Fit Assessment with our Business Systems team.

[/fusion_text][/fusion_builder_column][/fusion_builder_row][/fusion_builder_container]

The post Choosing the Right ERP: What No One Tells You Before You Sign the Contract appeared first on The Alliance Group.

That’s why Alliance has formally launched its NetSuite Practice, which is a dedicated capability built to help finance organizations implement, optimize, and fully leverage NetSuite as a core part of their finance infrastructure.

Why NetSuite and Why Now?

NetSuite has become one of the most widely adopted cloud ERP platforms. It gives finance teams a single, integrated environment for financial management, revenue recognition, procurement, multi-entity consolidation, and reporting. It does this by replacing the fragmented systems and manual workarounds that slow organizations down as they scale.

For private equity-backed companies managing rapid growth or integration across acquisitions, and for finance leaders under increasing pressure from investors and boards, a well-implemented NetSuite environment isn’t just nice-to-have, it’s foundational.

What Alliance Brings

Alliance professionals have been supporting NetSuite environments for years through ERP implementations, post-acquisition integrations, finance transformation initiatives, and technical accounting engagements across technology, professional services, life sciences, manufacturing, and PE-backed companies.

What sets Alliance apart is the combination of system expertise and deep accounting knowledge. We don’t just configure the platform; we make sure it’s built to support your financial reporting requirements, your close process, and your growth trajectory.

With the formal launch of our NetSuite Practice, we’re expanding that capability with dedicated resources and a structured approach across the full platform lifecycle, from initial implementation and finance process design through optimization, system integration, and ongoing operational support.

A Partner for the Long Term

Launching or optimizing a NetSuite environment is not a one-time project. It requires a partner who stays engaged through go-live, through the first close, through the next acquisition, and beyond. That’s how Alliance works.

If your organization is evaluating NetSuite, working through a stalled implementation, or looking to get more out of an existing environment, we’d like to talk.

Contact us to learn how Alliance’s NetSuite Practice can support your next phase of growth.

The post Alliance Launches Dedicated NetSuite Practice to Support the Office of the CFO appeared first on The Alliance Group.