Pigment vs. Adaptive: Which FP&A platform wins in 2026?

Pigment and Workday Adaptive Planning both claim to modernize planning and eliminate spreadsheet chaos. But when you dig into verified user reviews, you’ll notice a different reality with longer time-to-value, fragile integrations, admin drift to IT, and AI features that sound promising but have yet to deliver real impact.

In this guide, we’ve broken down how Pigment and Adaptive actually perform in the real world. We’ve pulled insights from G2, Capterra, and finance forums to map where each platform delivers and where they introduce friction for fast-moving finance teams.

You’ll see side-by-side comparisons, hidden costs surfaced, and a clear view of which tool is truly built for finance to own. If you're preparing to invest in a platform that will shape how your team works for the next 3–5 years, this guide will help you ask the right questions and avoid unpleasant (and expensive) surprises.

Pigment: strengths, limitations, and best fit

Pigment is a cloud-native FP&A platform that positions itself as a modern alternative to legacy EPM tools, with spreadsheet-inspired modeling and dynamic dashboards designed for cross-functional finance teams. 

It's commonly used to support core planning use cases such as top-down budgeting, revenue modeling, workforce planning, and departmental forecasting, particularly in mid-market and enterprise environments looking to replace fragmented spreadsheet workflows with a more collaborative, scalable planning option.

Its appeal lies in its planning workflows that feel familiar to spreadsheet users, but offer more structure and cross-functional alignment. While Pigment’s UI gets mixed reviews, once they overcome the challenge of learning its proprietary syntax, finance teams moving off Excel or Google Sheets often find Pigment’s what-if analysis capabilities useful for iterative modeling. 

However, the complexity of that proprietary syntax is an important consideration. Pigment’s SQL-like modeling language, while powerful, requires a steep learning curve, especially when building multi-dimensional models across subsidiaries, currencies, or cost centers. 

Another key consideration when evaluating Pigment is that it ships with far fewer native integrations than most of its competitors. While it connects with a few of the most common tools finance teams need (e.g., ERPs, CRMs, etc.), businesses needing to connect anything outside of Pigment’s 18 integrations will need third-party tools, custom integrations, and/or ETL workarounds. This can significantly impact implementation timelines, especially when enterprises need to replicate their existing planning logic or build custom allocation frameworks.

Pigment’s performance under enterprise-scale planning workflows has also drawn mixed reviews. Users report slower responsiveness when working with large datasets and complex scenarios. Reviews also indicate that administrative ownership often drifts from finance to technical teams or external consultants as complexity grows, which increases the total cost of ownership. 

What are Pigment’s core strengths?

• Familiar modeling environment: Pigment’s spreadsheet-like interface allows finance teams to model assumptions and drivers with a familiar setup, supporting multi-dimensional structures that Excel or Google Sheets can’t handle.
• Collaborative planning workflows: The platform supports shared model access with real-time commenting, version control, and role-based permissions.
• Scenario building and fast what-if analysis: Pigment helps teams to create and iterate on multiple planning scenarios, adjusting key variables without needing extensive model rework or configuration.
• Custom reporting and visual dashboards: Built-in dashboards and presentation layers allow teams to build dynamic reports within the platform, reducing dependency on external visualization tools.

What do Pigment users struggle with?

• Very limited native integrations: With only 18 prebuilt integrations, Pigment typically requires third-party software or custom-built integrations to connect with all the systems modern businesses (especially those with a more diverse tech stack) need for robust FP&A. 
• Setup friction for advanced models: Building and maintaining complex multi-dimensional models, including modeling business logic, configuring assumptions, and establishing workflows, demands significant effort and technical fluency from implementation teams and often requires costly support from external consultants.
• Performance suffers with scale: Users report slower responsiveness and lag in model recalculations when working with larger datasets or intricate forecast structures.
• Drift in finance-owned administration: Pigment typically requires ongoing support from IT, analytics teams, or external consultants to maintain financial models as the business evolves, undermining the goal of “finance-owned” FP&A, where finance teams are able to operate independently without outside assistance.

When does Pigment make sense?

Pigment is built for teams that want to move fast, model collaboratively, and replace spreadsheet chaos with a structured planning platform. It fits best in mid-market and lower enterprise environments where the goal is to drive alignment across departments without adopting heavyweight EPM tooling.

The platform works well for planning structures that aren’t deeply complex—think single-entity or lightly multi-entity orgs, stable currency environments, and limited intercompany logic. And, its built-in connectors may also suffice for companies with minimal integration needs and relatively centralized data stacks.

Pigment can also work for teams that are open to training up power users who can work directly within Pigment’s modeling language. Otherwise, ongoing external support will likely be necessary to maintain the models. 

For fast-scaling finance teams navigating frequent structural shifts like M&A activity, international expansion, or layered GTM motions, Pigment’s integration limitations, scaling constraints, and dependency on technical support may introduce friction. In these scenarios, organizations should evaluate whether the trade-off between speed of modeling and depth of control aligns with their longer-term needs.

Adaptive: strengths, limitations, and best fit

Workday Adaptive Planning is a mature, cloud-based FP&A platform built for mid-market and enterprise finance teams seeking standardized planning workflows and governance controls. 

Common use cases include budgeting, top-down forecasting, and multi-year financial planning across departments and geographies. For organizations already using Workday’s ERP or HCM suite, it's an appealing option due to its ecosystem alignment and native integrations within the Workday stack.

Adaptive supports collaborative input from other business users while maintaining tight control over planning logic, workflows, and approvals. Its architecture is especially useful for teams with stable planning cadences and centralized oversight. 

However, that structured environment can quickly become a constraint for businesses where the only constant is change and where flexibility is needed to keep up with it. 

Adaptive implementations can take five months or more due to the time needed to configure models, customize workflows, and align data hierarchies with existing systems. As the complexity increases, administrative ownership often shifts from finance to IT or even external consultants.

Adaptive offers more than 100 data connectors through its online marketplace. However, all of them appear to be developed by third parties, which will impose additional costs. In addition, users report that it is challenging for them to sync real-time data or manage changes in Adaptive when source systems are updated. And, the modeling flexibility within the platform itself tends to lag behind newer tools when logic or business structures need to be quickly changed.

What are Adaptive’s core strengths?

• Structured planning framework: Adaptive provides a centralized environment for budgeting, forecasting, and reporting, with built-in templates and workflows.
• Collaborative input with governance controls: Adaptive supports cross-functional contributions while maintaining approval workflows, version tracking, and access permissions.
• Support for multi-entity structures: Adaptive includes budget hierarchies and consolidation workflows that allow finance teams to plan across business units, geographies, or cost centers with consistent structures and rollups.

What do Adaptive users struggle with?

• Reliance on external consultants: Users commonly complain about having to rely on external consultants for building and maintaining models, both of which can significantly drive up the total cost of ownership (TCO). 
• Performance issues: Users report slow performance and reduced UI responsiveness with complex models and large data volumes, which limits their ability to scale.
• Complex implementation: The complexity of the system typically requires Workday consultants or certified partners to properly configure it to fit business needs, which increases implementation complexity, timelines, and cost. 
• Costly integrations: While Adaptive integrates natively with other Workday tools out of the box, other integrations are developed by third-party providers and sold through the larger Workday marketplace—an approach to integration creates costly dependencies for most customers. 

When does Adaptive make sense?

Adaptive’s fit hinges less on company size and more on the maturity of planning operations and the need for formalized workflows. 

It can work well for mid-market and enterprise companies with stable organizational structures, predictable planning cadences, and a clear need for centralized oversight. Its governance features make it a reliable option for organizations with strict process requirements and clear lines of ownership. 

Businesses that are already invested in the Workday ecosystem may find some cost benefits there. However, that benefit will be offset to the extent that other integrations are necessary because most require either a third-party connector or a custom-built integration. That offset will also include additional costs associated with maintaining those integrations, as they often require ongoing external support for updates and changes in response to evolving business structures and models. 

With Adaptive, businesses navigating frequent change might soon find the rigidity in its modeling capabilities too limiting. Teams looking for agility, rapid scenario building, or more finance-owned administration may find the platform difficult to adapt without sustained technical or consultant support.

Pigment vs. Adaptive comparison: a deep dive into their features and user experience

The table below is based on verified reviews from G2 and offers a brief overview of how Pigment and Adaptive stack up against each other. At first glance, it might appear that Pigment beats Adaptive hands-down. However, there’s more to evaluate to determine which platform might work best for your team and which might require trade-offs you’re not willing to accept. We’ll cover those in the sections that follow the table. 

The architectural differences between Pigment and Adaptive shape everything from how quickly teams can get started to who ultimately “owns” the system—the finance team or IT. 

Pigment leans into a modern, spreadsheet-inspired experience aimed at faster modeling and cross-functional collaboration. Adaptive, by contrast, offers a more structured enterprise planning environment with tighter integration coverage—but at the cost of flexibility and onboarding speed.

Modeling capabilities

Pigment offers the ability to maintain a rolling forecast and reforecast as needed, and finance teams can spin up scenarios and adjust assumptions with minimal friction. Once users get over the hurdle of learning its proprietary syntax, they can usually iterate on their models easily enough. 

In comparison, Adaptive offers rolling forecasts and reforecasting, as well as traditional top-down planning. But it’s more rigid and doesn’t offer the same level of agility when logic or assumptions need to evolve fast. This rigidity is due to its complexity—Adaptive is configurable, but configuration requires admin expertise. 

Integrations

Adaptive offers significantly broader integration support (100+ systems) than Pigment (18), making it a stronger fit for enterprise teams managing multiple ERPs, CRMs, and HR systems. But integration alone doesn’t guarantee data fluidity. 

While it provides few integrations out of the box, Adaptive’s marketplace offers 100+ third-party connectors to common ERPs, CRMs, and HRIS systems—far more than Pigment—to help reduce the need for custom-built data pipelines. However, they don’t come free with your Adaptive subscription. 

For businesses with a diverse data stack, both platforms will require workarounds to connect to all the systems they need for robust FP&A and incur additional costs for IT or external technical support to build and maintain those connections. As a result, they also require disciplined data hygiene and careful maintenance to support live forecasting or cross-system reporting, especially when operational data shifts frequently.

Platform complexity 

Based on our research, implementation timelines for Pigment vs. Adaptive average 4–5 months. However, to realize the ROI, it can take Pigment users an average of 14 months, and Adaptive can take up to 21 months. 

These numbers speak volumes to the inherent complexity built into most FP&A platforms built for enterprise-level businesses—an issue that Pigment and Adaptive have in common. 

In addition to their reliance on technical experts to connect, configure, and maintain integrations, which can stretch implementation timelines, both platforms take a long time for users to learn how to leverage them effectively. 

In this context, the long journey to ROI that Pigment and Adaptive users experience makes sense because the longer it takes for users to adopt a software, the longer it takes to extract its full value. 

Collaboration features

Neither Pigment nor Adaptive fully democratizes planning for non-finance users without added training or workflow design to distill out the complexities in navigating and interacting with their data. Users also note that building reports can take a lot of time to learn, particularly for non-technical users. 

While Adaptive facilitates collaboration with access controls, users have complained that its UI isn’t very intuitive. For example, while some of its features bear a resemblance to Excel, they often have an entirely different functionality than expected, which creates additional confusion. Others have pointed to inadequate learning materials that fail to offer sufficient support for users to apply best practices and fully leverage the software's capabilities.

In Pigment, one user noted that with a more user-friendly UI, the platform might allow broader use by business areas outside of finance. However, another user lamented that Pigment’s access controls are very difficult to work with. 

From these comments, it would appear that even if non-finance users were able to navigate the platform more easily, access controls—a key feature needed for safe, effective collaboration—could present a core challenge. 

What AI capabilities do Pigment and Adaptive offer? 

Both platforms are making strides in AI-driven planning and forecasting, but results vary depending on the quality of the data and the AI-related expertise an organization can bring to it.

Pigment offers the following types of AI agents:

• AI Analyst, which scans data within the system to surface trends, detect anomalies, and reveal what’s driving your key metrics.
• AI Planner, which proposes structured next steps based on goals and market context.
• AI Modeler to help build and optimize planning models, including writing formulas, flagging model inefficiencies, and updating workflows. 

Adaptive offers: 

• AI forecasting, blending historical financials with operational data to automatically create forecasts. 
• AI-assisted planning through a conversational interface that allows users to ask planning questions in plain-English prompts and get recommended next actions.
• Anomaly detection using machine learning (ML) to flag and alert users of any unusual patterns in the data coming into the system. 

To get reliable results from any AI tools, organizations must provide data that’s clean and well-structured. This often isn’t possible without the investment of significant time in data operations such as data cleansing and transformation, particularly when consolidating data from multiple connected systems. 

In practice, users say Adaptive’s AI feels broader but more complex to configure, noting that using its AI features effectively requires training and specialized administrative knowledge. 

Pigment vs. Adaptive: where both platforms fall short for dynamic finance teams

While Pigment and Adaptive each improve on legacy planning tools, they introduce new friction for finance teams managing rapid growth, frequent structural change, and constrained internal resources. 

Across verified reviews, G2 scores, and Drivetrain’s analysts’ comparison analysis, three pain points consistently emerge: long onboarding timelines, fragile data pipelines, and ongoing reliance on consultants or IT, often with cost transparency issues layered on top.

These shortcomings show up in different ways. With Pigment, it’s the integration gaps and performance limitations that get in the way. With Adaptive, it’s the platform’s structural rigidity and difficulty in making changes when needed. 

However, both result in the same core frustration of finance teams who end up spending more time working with IT and/or external consultants to manage the tool than they do modeling the business.

Increasing time-to-value and TCO

Both platforms promise more efficient planning—but users frequently report that the road to value is longer and more resource-intensive than expected.

Despite its modern interface, Pigment’s time-to-ROI averages 14 months, and setup often demands more technical fluency than first anticipated. While it’s faster to stand up for simple models, complexity increases overhead fast. 

Teams can expect to almost immediately encounter friction in configuring multi-dimensional structures, requiring technical help or external consultants to maintain accuracy and version control at scale. 

What might sound like a self-serve experience may actually require unexpected and ongoing professional services and/or an engagement with external consultants. 

Time-to-ROI takes even longer with Adaptive, averaging 21 months. Implementations often require long lead times to align workflows, approval hierarchies, and reporting formats. 

Change management is heavy, and administrative ownership frequently shifts from finance to IT, adding coordination layers and increasing long-term operating costs.

In both cases, the total cost of ownership is often underestimated at the outset, especially when ongoing model maintenance, integration oversight, and external services are factored in.

Platform complexity that drives dependence on external partners

Both Pigment and Adaptive create dependencies on external partners that slow adoption and prevent teams from fully leveraging their features.

With Pigment, even routine tasks require admin involvement (e.g., unlocking needed reporting fields), which fragments workflows and hinders productivity. Users also cite inadequate and cumbersome access rights, making it difficult for organizations to scale adoption.

Pigment capitalizes on its inherent complexity to promote its partner ecosystem—a business decision that creates vendor lock-in by design and can limit your company’s agility and ability to fully leverage your investment in the platform. Pigment does this by limiting access to advanced configuration, integration, and scenario-building training (including certifications). Instead of making those resources broadly available to its customers, Pigment makes them available only to its partners. All user certification pathways also funnel through the company’s implementation partners. 

So, if your finance team needs to build complex models and create highly customized, detailed reports, you’ll need to hire and work with a Pigment-certified partner—either on an ongoing services basis, or by negotiating for specific upskilling packages as part of your implementation.

With this model, deep expertise stays with Pigment’s partners—not your internal teams—reinforcing vendor lock-in, raising migration costs, and increasing the risk of critical knowledge loss if partners change. And what is positioned as agility becomes delayed decision-making, as you wait and pay consultants for answers.

Adaptive’s steep learning curve produces a similar outcome. While the company doesn’t explicitly restrict access to advanced training, users note that its training resources are often insufficient, lacking the depth and practical guidance they need. As a result, users find themselves forced to hire certified Adaptive consultants or pay for partner-led workshops to accomplish advanced tasks (e.g., custom model building, integrations, scenario-planning). 

 “Finding a clear learning path of the product that does not result in additional charges. Taking an accelerated class on formulas and having to pay for it or pay a consultant is not a solution when we already pay for the product.” – Senior Financial Planning Analyst and verified Adaptive user

The common thread here is that both solutions create complex environments where third-party dependence is woven into day-to-day operations. CFOs should weigh how these barriers to finance-owned FP&A—the ability of their teams to be self-sufficient—can undermine the efficiency and agility that the software they’re considering is supposed to deliver.

Persistent friction with data operations

Reducing friction in data consolidation is one of the core value propositions of any modern FP&A software. Yet, neither Pigment nor Adaptive platform fully resolves the data integration and reporting bottlenecks that finance teams hope to escape by leaving spreadsheets. 

Pigment’s 18 native connectors fall short for enterprises juggling multiple ERPs, CRMs, and warehouses. Most teams are forced to build external pipelines or manage manual uploads, introducing latency and persistent reconciliation work. 

While the platform provides custom dashboards, users often export into Excel or BI tools for board-ready formatting. This fragments workflows and undermines the goal of a unified planning environment.

As for Adaptive, for users who can afford the additional costs to access its broader integration system, data connections still demand disciplined architecture and careful stewardship to maintain real-time data flows. 

Changing source systems or adding entities often triggers cascading model updates, which require technical intervention. Reporting is structured but rigid, and finance teams may still need to manipulate outputs or layer in external tools to meet executive reporting standards.

Both tools rely heavily on third-party tools and/or external consultants for setting up and configuring data connections to critical data sources, leaving finance teams vulnerable to fragile integrations, versioning conflicts, and inconsistent definitions across models.

With any FP&A software, when maintaining integrations and models becomes a recurring challenge, administrative ownership shifts away from finance and toward technical experts (in-house or external consultants). However, with Pigment and Adaptive, this really isn’t a shift because both platforms require extensive technical support from day one of implementation—a level of operational drag that fast-moving finance teams increasingly resist.

How Drivetrain outperforms Pigment and Adaptive

Pigment and Adaptive can help finance teams move beyond fragmented spreadsheets and rigid Excel models. However, they don’t fully resolve the friction that slows fast-moving finance teams down. Extended onboarding cycles, fragile data infrastructure, consultant reliance, and underutilized AI features remain common pain points, especially as businesses scale or change rapidly.

Drivetrain is built to eliminate those limitations from day one. Drivetrain is an AI-native FP&A platform that combines modern modeling flexibility and collaborative planning with enterprise-grade governance, deep integrations, and embedded AI built for finance teams, not just data scientists. 

Where Pigment introduces integration gaps and performance ceilings, and Adaptive imposes rigidity and long implementation timelines, Drivetrain delivers faster time-to-value, higher adaptability, and lower long-term cost, all without forcing finance to hand over its control. In this section, we’ll show you how it addresses these challenges. But first, we need to tell you about Drive AI. 

Drivetrain’s AI capabilities

Drive AI is a suite of capabilities built into all of Drivetrain’s core workflows. AI was a core design principle from day one. As a result, it is a truly native AI platform offering robust AI features built to deliver real, data-driven insights. 

With Drive AI, you have access to powerful AI features:

• The AI analyst is a conversational interface users can engage with anywhere in the platform to ask questions like “Why did revenue dip last month?” and get back clear, contextual answers with charts, variance breakdowns, and links to the underlying drivers.
• With one click, the AI model generator will combine data from your ERP, CRM, HRIS, or any other connected system, automatically select key metrics, and apply relevant business logic to create a flexible baseline model in seconds that you can adjust as needed. 
• ML-based anomaly detection scans live data across revenue, spend, cash, and headcount to flag anything unusual. Whether it’s a data quality issue or a business trend shift, the system notifies you before it turns into a problem.
• AI-powered transformations reduce data operations to a simple prompt, allowing you to dig deep into your data with plain-English commands. Ask Drive AI to “split revenue by product and region” or “filter for churned customers with ACV > $100k,” and it instantly delivers clean, structured outputs.

Lower TCO by design with no consultant dependency

One of the most consistent buyer frustrations with both Pigment and Adaptive is how hard it is to reliably estimate their TCO. Hidden implementation charges, reliance on consultants for admin tasks, and the overhead of maintaining integrations and model logic can really add up over time. 

With Drivetrain, you always know what you’re getting upfront. Drivetrain’s user-centric design naturally reduces long-term costs because it eliminates any need for external support or ongoing technical rework. 

Instead of waiting over a year to reach ROI with Pigment or Adaptive, Drivetrain can get you there fast. Drivetrain customers typically go live within 4–6 weeks and achieve their ROI within just six months. 

This is because the platform is built for finance-owned FP&A to provide enterprise-level capabilities without the enterprise-level complexity. With Drivetrain, teams can begin integrating with their systems and generating insights from day one. No technical overhead or extensive training required.

Implementation is managed in-house by Drivetrain’s onboarding team, with structured migration milestones and a dedicated customer success manager (CSM). Users can build their models fast with flexible, built-in templates or leverage the system’s AI. 

‍“From sales to proof of concept and through implementation, the folks at Drivetrain provided outstanding support and education. It was one of the best implementations I've experienced with any vendor.” —Chief of Staff and verified Drivetrain user 

Support is proactive and built-in. Whether it's onboarding new teams, evolving planning structures, or troubleshooting data flows, Drivetrain provides hands-on guidance, not just ticket-based escalation queues. Users also have access to an extensive library of self-serve learning content, and users can instantly get in-context answers to their questions about how to use the system from anywhere in the platform by simply asking Drive AI for answers. 

All of the power finance teams need, without the complexity

Drivetrain is designed with users in mind. Its focus on the front end is on delivering a clean, finance-first UX that balances the power it offers under the hood with the clarity users need in the driver’s seat.

Drivetrain eliminates the specialist dependency that slows progress and eats into the ROI for Pigment and Adaptive users, significantly reducing the TCO while empowering finance teams, giving them the ability to make structural or logic changes on their own without any need for consultants, coding, or IT support. Drivetrain makes finance teams agile and independent with a platform they can “own” from day one.  

‍“One of the standout features of Drivetrain is its intuitive interface. It's incredibly user-friendly, making it easy for both finance professionals and non-finance stakeholders to navigate.” —FP&A Manager and verified Drivetrain user 

Drivetrain’s AI copilots also simplify learning. No matter where they’re working on the platform, users can ask questions in plain language about how to use a given feature and get instant answers. This in-context learning makes the copilot feel like a natural extension of Drivetrain’s support team and makes the platform even easier to adopt.

And if there’s something a user is struggling with, Drivetrain’s support team is always there to help.

Integrations, real-time planning, and cross-functional collaboration

Integration friction is where most FP&A platforms, including Pigment and Adaptive, fall short. Drivetrain doesn’t. With Drivetrain, you get more than 800 native, plug-and-play integrations, including ERPs, CRMs, HRIS, billing platforms, data warehouses, and many, many more. 

Most of Drivetrain’s connectors can be installed and configured in minutes by the finance team, and with so many to choose from, even businesses with the most diverse tech stacks can get their data flowing into the system quickly. 

When a new region, product line, or cost center comes online, new data sources can be added directly without requiring technical support. Analysts can easily modify hierarchies, add new entities, update logic, or spin up forecasts without breaking downstream integrations or opening IT tickets. Built-in reconciliation, lineage tracking, and ML-powered anomaly detection ensure every refresh is validated and trusted.

Drivetrain also brings operations and finance closer. Collaboration features such as shared planning spaces, role-based permissions, and live dashboards help teams build, revise, and review forecasts together without duplicating logic across tools. With models and reporting flow from the same governed data layer, there’s no need to export anything to Excel or BI tools to prepare board-ready outputs. Planning becomes continuous and responsive, rather than locked in quarterly cycles or reliant on external help to maintain system health.

A decision framework for CFOs

Choosing the right FP&A platform is about finding the system that aligns with your business logic, data landscape, and planning velocity. They affect who owns your models, how fast your team can respond to change, and how confidently you can rely on your outputs.

To help you choose the best tool for your team, we’ve provided the framework below as a starting point to evaluate any FP&A platform for your specific planning requirements.

1. Map your data and integration environment

Start with the basics, your system landscape. Think about how many ERPs, CRMs, HR tools, and data warehouses your team touches just to get a forecast out the door. Then zoom out. Are you working across multiple entities, currencies, or regions?

If your setup is relatively simple and stable, you might be fine with a platform that offers fewer native connectors. But if your business is growing fast or constantly changing, you’ll definitely want integrations that can keep up. That means no patchy workarounds, no middleware dependencies, and no waiting on IT every time something breaks.

When you’re evaluating platforms, get specific:

• Are the integrations truly native, or are they stitched together behind the scenes?
• Can finance configure and manage them, or does it require external support every time?
• What happens when something changes upstream, and how quickly does that flow into your models?

A platform might look slick in a demo, but if the data can’t move cleanly and reliably, you’ll end up spending more time chasing down questionable numbers than analyzing them. Integration issues shouldn’t turn this into a standard practice. 

2. Understand how often your business changes and how fast your platform can keep up

Think about your planning cadence. If your planning structures are constantly changing, your platform has to keep up without needing a full rebuild every time something changes.

This is where many tools fall short. What seems fast in a demo often turns slow once you try to roll out changes across interconnected models or reports. 

That’s why, when evaluating vendors, ask them to show you how they handle real change. For example, ask:

• Can they add a new product or business unit and flow it through the forecast?
• What happens when you modify a driver like churn rate or sales ramp?
• Can they roll that change into dashboards without breaking anything?

Then dig into the details and find out how many steps it took to complete them and whether it's manageable by finance or requires external support.

3. Define who really owns the system once you're live 

Here’s the million-dollar question: Once you’re fully onboarded, who actually owns the platform?

Can your finance team add a new product line, tweak a forecast driver, or restructure a planning hierarchy on their own? Or do they have to log an IT ticket or, worse, bring in consultants just to make everyday changes?

This is where most tools start to show strain. They often start out feeling flexible, but as complexity grows, so does the reliance on admins or technical teams. Over time, finance loses control, and the responsiveness of the platform is barely passable.

To avoid this, when you’re evaluating platforms, don’t just ask about features. Focus on the day-to-day aspects like: 

• Can finance teams handle dimensional changes without breaking things?
• What edits trigger an IT escalation or require external support?
• How often will you need help just to keep things running?

4. Don’t stop at license fees. Look at the TCO

It’s easy to compare the platforms based on sticker price. But licensing is just the tip of the iceberg. What really matters is what you’ll spend to keep the platform running, useful, and adaptable over the next few years. 

Start by mapping the full picture:

• What does implementation actually cost, including onboarding and training?
• How many people will you need to manage the system once it’s live, and what skills will they need?
• Is training provided?
• What kind of support is included with your subscription?
• Will you need external consultants just to keep things updated?
• Are there add-on tools required for reporting, data prep, or integrations that aren’t included out of the box?
• How do renewal terms or usage-based fees escalate over time?

Some platforms may look lighter up front, but once you factor in ongoing support, integration upkeep, and technical lift for even routine changes, the long-term spend can creep up quickly. 

So when you’re comparing platforms, don’t just ask, “What does it cost to buy?” Ask, “What will it take to run this thing, month after month, year after year?” That’s where the real differences show up.

5. Pressure-test the AI on your actual data

Just about every FP&A platform says it has AI. However, most of it sounds great on a slide and falls flat in practice. If the AI can’t explain its outputs, speed up your workflows, or work with your data without hours of manual data cleaning and transformations, it’s not providing real value. 

So don’t settle for a canned demo. Ask vendors for a proof of concept built with your own data to demonstrate their AI capabilities. See how it handles real-world messiness like missing values, evolving drivers, and multiple dimensions.

Here’s what to look for:

• Can it surface variance drivers with clear lineage, not just flag that something changed?
• Does it pinpoint anomalies and explain why they happened, or just highlight outliers?
• Can it generate scenarios you’d actually use that are governed, auditable, and aligned with your model logic?

Then dig into the workflow side:

• Is the AI running directly on your governed platform data, or does it need a whole parallel setup?
• Can finance use it without calling in IT or writing code?
• Is it actually reducing cycle time or just giving you more to QA?

Finally, don’t skip the security angle. If the tool connects to external LLMs or APIs, find out if there are any guardrails in place. Also, ask how your financial data is being accessed, stored, and protected because speed isn’t worth it if trust is compromised.

6. Choose a platform that keeps up with where you’re headed 

Your business today will not be your business two years from now. Maybe you’re entering new markets. Maybe you’re shifting your pricing model, acquiring a competitor, or acquiring new revenue streams. Change is a constant, and your FP&A platform needs to keep pace without dragging you into another reimplementation.

The right tool won’t just fit your structure right now; it’ll adapt when that structure changes in real workflows without blowing up your models or forcing your team to pause planning just to rebuild the logic.

So ask the hard questions:

• How fast can the platform adapt when you add a new entity or change how you segment revenue?
• Can you tweak assumptions and governance side by side, or does one break the other?
• Will you need more admins and technical support to keep it stable as you scale?

The best FP&A platform doesn’t make you choose between control and speed. It moves with your business so you can spend less time reconfiguring and more time planning what’s next.

Choosing your FP&A partner

By now, the trade-offs between Pigment and Adaptive should be clearer. 

Pigment delivers collaborative planning workflows that can make it appealing for mid-market teams looking to replace spreadsheets. Adaptive brings structure and governance, particularly for companies already deep in the Workday ecosystem. But both platforms carry friction that dynamic finance teams cannot afford in today’s fast-moving business landscape: long implementation timelines, platform complexity that creates an ongoing and costly dependence on external consultants to build and maintain critical integrations. 

Drivetrain eliminates that drag with 800+ integrations, a powerful, yet simple-to-use platform built for finance-owned modeling at scale. And it offers AI capabilities that provide tangible value for fast-moving finance teams. 

With Drivetrain, you get enterprise-grade capabilities combined with true planning agility.

If you're evaluating next-generation FP&A tools, Drivetrain is the only platform designed to meet you where you are and grow as fast as you do.

Book your demo today to see how Drivetrain delivers.