Ask three different AI assistants the same business question, say “What was our regional revenue last quarter?”, and there’s a real chance you’ll get three different answers. Not because the AI misunderstood the question, but because each assistant pulled its definition of “revenue” from a different place.

That’s the uncomfortable truth behind AI’s rapid arrival in business intelligence: AI is very good at understanding what you’re asking. It’s far less reliable at deciding what the answer should mean, unless that meaning is governed somewhere outside the AI itself.

AI didn’t cause data fragmentation; it exposed it

Most enterprises don’t run analytics through a single tool. Sales might rely on one BI platform, Marketing on another, Finance on a third, each with its own version of “revenue,” “customer,” or “margin” built directly into the tool. We’ve written before about how this patchwork of source systems and parallel metric definitions creates what looks like professional, well-designed reporting that simply doesn’t agree (One Definition of Truth: Semantics as the Backbone of Trusted AI).

As long as people were the ones reconciling these differences in a meeting or a spreadsheet, the cracks were manageable. AI removes that human buffer. When a chatbot or an AI agent answers instantly and confidently, nobody stops to question which version of “revenue” it used. The fragmentation that was always there becomes far more visible, and far more costly.

That cost is concrete, not abstract. Someone still has to reconcile the numbers when two AI tools disagree, which means the “faster path to an answer” that made AI attractive in the first place gets spent right back on manual verification. Decisions slow down while people figure out which number to trust, at exactly the moment AI was supposed to speed that up. And every unresolved discrepancy chips away at confidence in the tool itself: once a business user catches an AI assistant giving two different answers to the same question, they tend to stop trusting it for anything that matters, and go back to checking the source report by hand. That’s the real cost of a governance gap: not one wrong number, but less time saved, slower decisions, and less trust in the tool that was supposed to speed things up.

Why AI is becoming the front door to analytics

None of this makes AI’s appeal any less real, it just raises the bar for how it needs to be implemented. It’s easy to see why AI is spreading so quickly across BI. It gives business users a shortcut past dashboards, filters, and report builders straight to an answer, phrased in plain language. A CFO can ask “why did margin drop in Q3?” without knowing which table holds the data or how to write the query.

This shift matters for three reasons in particular:

That last point carries a large “if.” The benefits only hold when the logic behind the AI’s answer is consistent, no matter which interface asked the question.

Three jobs that shouldn’t be done by the same layer

The most common mistake in AI-BI projects is letting one component, usually the language model itself, handle three fundamentally different responsibilities at once:

Consumption is where the question comes in, and the answer goes out. It’s the conversation: natural language, follow-up questions, summaries. This layer can and should look different depending on who’s asking: an executive might use a BI assistant, a finance team might work inside an everyday productivity app, an automated workflow might ask the same question through an API. Different interfaces are fine, as long as they don’t quietly become different versions of the business.

Context is what the enterprise actually means by “Revenue,” “Customer,” or “Active Account”: the metrics, hierarchies, calculations, and access rules that give a raw number business meaning. This is the layer that needs to be defined once and reused everywhere. If it isn’t, every AI tool ends up reconstructing its own interpretation from whatever schema or prompt it has access to, which is exactly how three assistants end up with three different answers to the same question.

Execution is where a governed request becomes an actual query against enterprise data. This is where consistency matters most: a business rule that must produce the same answer every time shouldn’t be regenerated probabilistically by a language model with every new request. When the AI owns both the interpretation and the query logic, small variations can creep in before the data is even touched, and the same question can quietly take a different path depending on the model, the prompt, or the day.

Put simply: the conversation can be flexible. The meaning behind it can’t be. And the query that finally reaches the database needs to behave consistently every time.

Where a universal semantic layer fits in

This is precisely the gap a universal semantic layer is built to close. It sits between the AI (or any BI tool) and the underlying data platforms, holding the governed definitions, relationships, and access rules that every interface should draw from, rather than letting each tool, human or AI, work out its own version.

We’ve covered how this protects sensitive data and supports compliance in more detail elsewhere (The Universal Semantic Layer: The Key to Data Security and Usage Compliance). For AI specifically, the value is slightly different: it’s less about access control and more about trust in the answer itself. When metric definitions live in one governed place, an AI agent doesn’t need to guess what “revenue” means. It inherits a definition that’s already correct, already approved, and already consistent with what a human colleague would see in a dashboard.

Strategy Mosaic is built around exactly this separation. AI handles the conversation. Mosaic holds the business logic. A deterministic SQL engine turns governed requests into queries, keeping the part of the system that must never vary out of the hands of a language model that, by design, generates a slightly different answer every time you ask it the same thing twice.

The takeaway for CFOs and IT leaders planning AI adoption

AI in analytics isn’t a passing BI feature: it’s fast becoming the main way people ask questions of enterprise data. That’s a good thing for adoption and accessibility. But it only remains a good thing if the business logic behind every answer is governed centrally once and applied consistently, regardless of which tool, model, or interface the question came through.

The goal isn’t to bolt AI onto every existing BI tool separately. It’s to make sure every tool, human-facing or AI-facing, draws from the same, trusted definition of what the business actually means.

Want to explore what a governed, AI-ready semantic layer would look like in your own data landscape? Get in touch with CRMT.
As a Strategy partner, we help organisations move from fragmented reporting to a single, trusted source of business logic.

The post Ask AI, Get a Different Answer Every Time: The Governance Gap Behind Conversational AI appeared first on crmt.com.

We continue our blog series highlighting the standout sessions from this year’s Adriatic Controlling Conference with Beyond Historical Concepts: Driving Future-Ready Controlling, presented at the 21st Adriatic Controlling Conference (26 May, Hotel Mons, Ljubljana) by Christian Brumm, Head of Finance Operations Excellence at Zalando and co-author of Agile Konzepte im Controlling, and Pia Burkarth, Finance Director at Veridos Mexico and board member of the International Association of Controllers (ICV).

The two speakers set out to answer a deceptively simple question: what leadership principles and structures does controlling actually need today to create real value for the business, and how can a function built for decades on planning and analysis make the leap into a world defined by constant change?

Insights from the Adriatic Controlling Conference
1: From Reactive Reporting to Proactive Controlling: Lessons from Zavarovalnica Sava
2: When Dashboards Replace Guesswork: Resalta’s Journey to Real-Time Operational Controlling
3: Beyond Historical Concepts: Building a Future-Ready Controlling Function

The BANI world: beyond uncertainty into incomprehensibility

The talk opened with a simple observation: businesses today no longer operate only in the familiar VUCA environment (volatile, uncertain, complex, ambiguous). Increasingly, they find themselves in what has been termed a BANI world (brittle, anxious, nonlinear, incomprehensible). According to the speakers, global value chains, political instability, technological disruption such as artificial intelligence, and rapidly shifting market conditions are all forcing controlling to rethink how it leads and how it makes decisions, compared with the more predictable business environment of the past.

From complicated to complex: the Cynefin framework

To explain when agile methods are appropriate and when traditional ones still hold up, the speakers introduced the Cynefin framework, which sorts business challenges into four categories: simple, complicated, complex, and chaotic. In both simple and complex situations, where cause and effect are relatively clear, established, traditional planning methods still work well. In complex and chaotic environments, however, where the link between actions and outcomes is not known in advance, a different approach is required: experimentation, continuous learning, self-organizing teams, and leadership that supports the team rather than simply controlling it.

As the speakers put it, this amounts to a genuine paradigm shift: from a world in which organizations were built on planning, knowledge and facts, to one shaped by experimentation, learning, and empowered teams.

Mindset as the link between purpose and tactics

One of the more thought-provoking parts of the talk centered on a model in which mindset, meaning the beliefs, values and principles that guide a team, is what connects purpose (the shared goals and direction) with tactics (processes, tools, roles, meetings). Without alignment between why we do something and how we do it, the speakers argued, an agile transformation simply cannot last. They were also clear that there is no one-size-fits-all answer: every organization and team needs to adapt its own processes, roles and tools to its specific circumstances.

Closely related is the concept of a growth mindset, drawing on the work of psychologist Carol Dweck: people with a growth mindset treat challenges as opportunities, persist through setbacks, learn from criticism, and draw inspiration from other people’s success rather than feeling threatened by it. According to the speakers, it is precisely these people who drive organizational transformation.

Agile leadership as the foundation for transformation

Burkarth pointed out that as the environment grows more complex, the role of the leader has to change with it. An agile leader’s main job is to set a clear direction and purpose, a kind of “North Star”, to guide the team toward self-organization, to trust decisions to the people who actually hold the relevant knowledge, and to delegate responsibility based on each person’s motivation and skill level. At the same time, leaders still need to strike a balance between genuine care for their people and the decisiveness the business requires, since deadlines and commercial expectations don’t disappear simply because a team becomes more agile.

Zalando in practice: controlling as “co-pilot”

In the second part of the talk, Brumm walked through how Zalando’s finance function evolved toward genuine business partnership. Rather than settling for the generic label “business partner”, the team at Zalando chose the metaphor of the co-pilot: someone who doesn’t fly the plane on behalf of the business, but who offers additional data, flags different scenarios, and helps shape the direction, all while knowing both the “aircraft” and the “pilot”, that is, the company and its people, inside out.

Living up to that role, Brumm explained, took a real investment in understanding the actual drivers behind the business, not just producing standard financial metrics. He illustrated the point with the image of an iceberg: a surface-level view of the customer and their needs is only the tip; genuinely understanding the business means diving into the underlying processes and data hidden beneath the waterline.

The post Beyond Historical Concepts: Building a Future-Ready Controlling Function appeared first on crmt.com.

To understand where your organization spends its energy, walk your team through this three-step diagnostic audit.

1. The Data vs. Decisions Friction Audit

The goal of introducing AI into your CPM processes is not to replace human expertise, but to remove the tactical friction that bogs it down. AI handles the heavy lifting of data processing, so your team can focus on what human intelligence does best: strategic big-picture thinking and subjective analysis.

To identify where your team is stuck in “data mode,” ask them:

• What CPM tasks are we still running manually? Look for legacy spreadsheets used for data mapping, manual status tracking, and multi-entity data entry.
• Where do our automated processes stall? Identify automated pipelines that still require heavy manual oversight, double-checking, or error reconciliation.
• Is our data foundation AI-ready? If your financial and operational data streams are siloed, your team will inherently spend more time fixing data than leveraging it.

2. Quantifying the Time Drain (Where are the Hours Going?)

To flip the ratio from data to decisions, you need to know exactly how your team’s hours are split. Audit your current resource allocation across these three primary pillars:

3. The Non-Financial Data Strain: ESG and Beyond

Modern CPM no longer operates within a pure financial silo. Finance teams are now forced to pull data from disparate systems across the enterprise—ERPs, HR systems, supply chains, and, increasingly, ESG and carbon-emission trackers driven by CSRD frameworks.

When these non-financial, often unstructured data streams (such as vendor contracts or sustainability metrics) are handled using legacy tools, the “data trap” tightens. The team spends days just trying to make sense of the formatting.

Advanced platforms solve this natively. CCH Tagetik utilizes AI to systematically ingest, validate, and bridge financial and ESG data into a unified, audit-ready record. Instead of wasting time verifying compliance, your team can focus on the financial impact of sustainability decisions.

The post AI in CPM: Is Your Finance Team Spending Time on Data or Decisions? appeared first on crmt.com.

AI-Ready Data Series — Part 3

In the previous article, we focused on one of the most important foundations of trustworthy AI: context.

Lukas Zimmermann’s session at our AI Success Starts with AI-Ready Data event showed why AI-ready data is not just about moving data into a modern platform or building another dashboard. AI systems need business meaning, governance, and consistency, which is exactly why semantic layers are becoming such a critical part of enterprise AI architecture.

But once organizations solve the context problem, another challenge quickly arises: can the infrastructure behind AI actually support enterprise-scale use?

In real business environments, AI is no longer just a chatbot or a proof of concept running on isolated datasets. It becomes part of operational systems, analytics processes, and decision-making workflows, often across large volumes of both structured and unstructured data.

And that changes the requirements completely. One of the strongest messages from Dirk Beerbohm’s session (Exasol) at the same event was that successful enterprise AI depends on far more than the model itself. It depends on the infrastructure, governance, and data architecture supporting it.

AI-Ready Data Series
1: AI Success Starts With AI-Ready Data: What “Ready” Really Means
2: One Definition of Truth: Semantics as the Backbone of Trusted AI
3: The infrastructure challenge behind enterprise AI

The real AI challenge starts after the pilot phase

Most AI discussions today still focus on models, copilots, and prompts. But according to Beerbohm, the real complexity begins once organizations move from experimentation into production-scale AI usage.

Traditional analytics environments were built around structured data: ERP systems, CRM records, financial transactions, and reporting platforms.

These systems are predictable because the data is organized and governed. The problem is that most enterprise knowledge does not exist in structured tables.

Documents, emails, PDFs, operational notes, customer feedback, logs, and contracts represent a much larger portion of business information, and most of it remains inaccessible to traditional analytics approaches. Beerbohm described this as the “hidden” part of enterprise data: large volumes of unstructured information that organizations rarely use effectively.

AI changes this completely. Large language models enable the extraction of meaning, entities, sentiment, and context from unstructured information in ways that were previously too complex or too expensive to implement. But this also introduces entirely new operational requirements.

AI workloads are different from traditional analytics

One of the most important distinctions in the session was that AI workloads behave very differently from classical BI or reporting environments.

Traditional analytics systems primarily process structured queries and rely on predefined models.

Beerbohm explained that modern AI architectures increasingly aim to process AI workloads as close to the data as possible to avoid unnecessary data movement, reduce complexity, and improve performance.

This becomes especially important once AI moves beyond isolated pilots and starts supporting operational processes in real time.

In other words, AI is not just another application layer added to the existing infrastructure. It changes the architecture itself.

Governance becomes even more critical

As AI capabilities grow, so do governance challenges. One of the recurring themes of the event was that organizations must retain control over how AI accesses, interprets, and uses enterprise data.

This is particularly important in regulated industries such as banking, insurance, and telecommunications, where explainability, traceability, and compliance are non-negotiable requirements.

Large language models can generate highly convincing outputs, but organizations often cannot fully trace how those outputs were created. Beerbohm highlighted this as one of the major limitations of large language models in enterprise environments.

That is why AI should support decision-making, not autonomously execute decisions without governance and human oversight.

And that question is as much about infrastructure as it is about data.

Why sovereignty and deployment models matter again

Another major theme was sovereignty. Over the last decade, many organizations adopted cloud-first strategies. But AI workloads are forcing companies to reevaluate where data should reside and where processing should happen.

Beerbohm presented architectures in which AI processing occurs directly within controlled environments, without sensitive metadata or schema information leaving the organization’s infrastructure.

For many organizations, especially those operating in regulated sectors, hybrid and on-premise AI environments are becoming increasingly important parts of enterprise architecture.

The goal is no longer simply “cloud-first.” The goal is to place workloads where they make the most operational and regulatory sense.

AI-ready means infrastructure-ready

The first two articles in this series focused on trust, semantics, and business context. But AI readiness does not stop at the data layer.

The organizations that succeed with AI will not necessarily be the ones experimenting with the largest models.

They will be the ones building the strongest foundations behind them.

The post The infrastructure challenge behind enterprise AI appeared first on crmt.com.

How do you manage a portfolio of 229 active projects across seven countries, spanning two distinct business models, with some contracts running for 25 years? That was the opening question posed by Aleš Jurak, Chief Operating Officer at Resalta, during his presentation at this year’s Adriatik Controlling Conference in Ljubljana.

The answer lies in data architecture.

Insights from the Adriatic Controlling Conference
1: From Reactive Reporting to Proactive Controlling: Lessons from Zavarovalnica Sava
2: When Dashboards Replace Guesswork: Resalta’s Journey to Real-Time Operational Controlling
3: Beyond Historical Concepts: Building a Future-Ready Controlling Function

From Excel to a Data Warehouse

Resalta was founded in 2011 as a joint venture between Gorenje, Geoplin, and Energetika Ljubljana. Today, it is part of the Scottish group Aggreko and operates as one of the region’s leading energy services providers, with activities spanning solar power plants and energy storage, comprehensive building energy retrofits, heating and cooling systems, and decarbonization services.

In 2019, Resalta introduced a standardized 150-step project management process, initially tracked in Excel. As the portfolio grew and geographic reach expanded, it became clear that the process needed to be digitalized.

The approach was incremental: first, master data management was established on the Webcon platform; then a data warehouse was built to consolidate data from multiple business process management systems across different countries. CRMT built the data foundation, with visualization and analytics delivered via the Strategy platform.

Four Controlling Challenges That Don’t Forgive Poor Architecture

Jurak identified four fundamental challenges that, in long-term investment portfolios, simply cannot be managed without the right data architecture:

Five Dashboards

The solution Resalta operates today consists of five interconnected operational dashboards:

One Logic, One System

What sets Resalta’s approach apart is a unified data foundation that serves all business models, turnkey projects, and long-term energy performance contracts. Although these models differ in duration, risk allocation, and revenue dynamics, they share the same execution phases. A single controlling system is therefore not only feasible, but the logical choice.

“Our ambition was for this to happen significantly faster,” Jurak acknowledged. Interim acquisitions, integrations, and corporate reporting requirements slowed the build. Even so, the message is clear: high-quality operational controlling for complex, geographically dispersed portfolios is not primarily a question of tools. It is a question of data architecture and the discipline to maintain it.

If you recognize these challenges in your own organization and would like to explore how a similar transformation could work in your environment, get in touch. Our team would be happy to walk you through the options and help you identify where to start.

The post When Dashboards Replace Guesswork: Resalta’s Journey to Real-Time Operational Controlling appeared first on crmt.com.

At this year’s 21st Adriatik Controlling Conference, dedicated to the modernization of controlling, one session stood out. Matic Hren, Information Solutions Architect at Zavarovalnica Sava, took the stage to walk a room full of finance and controlling professionals through a real-world case study: how a single infrastructure decision fundamentally changed the quality and timeliness of business decision-making and why it matters for any large organization.

Insights from the Adriatic Controlling Conference
1: From Reactive Reporting to Proactive Controlling: Lessons from Zavarovalnica Sava
2: When Dashboards Replace Guesswork: Resalta’s Journey to Real-Time Operational Controlling
3: Beyond Historical Concepts: Building a Future-Ready Controlling Function

A bottleneck you’ll recognize

Zavarovalnica Sava, Slovenia’s second-largest insurer and the parent company of the international Sava Insurance Group, was facing a challenge familiar to many complex organizations. ETL processes were slowing down, and data was reaching the warehouse with a six- to eight-hour lag. The existing architecture performed full nightly reloads from core systems. An approach that was becoming increasingly unsustainable as business volumes grew and the group expanded into new markets.

The controlling and business intelligence teams felt the impact most acutely. Most of their time was consumed by waiting for data, validating completeness, and producing backward-looking reports, leaving little room for genuinely value-adding analysis.

Sound familiar? You’re not alone.

The fix: Change Data Capture

The Zavarovalnica Sava team found the answer within their existing toolset. They were already using Qlik Talend as their primary integration and orchestration platform, which they extended with Qlik Replicate,  a tool built on Change Data Capture (CDC) technology. Rather than moving entire datasets on a schedule, CDC reads transaction logs and pushes only the records that have actually changed into the analytics environment.

The results were immediate and measurable: data latency dropped from several hours to under 15 minutes. The analytics layer now operates in near-real-time sync with core systems, and the platform reliably processes up to 400 million row-level changes per month across individual tables.

What this means for controlling

The post From Reactive Reporting to Proactive Controlling: Lessons from Zavarovalnica Sava appeared first on crmt.com.

Imagine the following scenario: your finance department reports revenue that is 10% higher than the sales team’s figures. Meanwhile, executive management receives a third and fourth version of the exact same key metric from two different AI agents. Sounds familiar?

In an era when companies are scrambling to implement artificial intelligence as quickly as possible, a fundamental truth is often overlooked: AI is only as smart as the organized, consistent data that feeds its models.

In this post, we address key questions about the future of data management and the role of modern solutions like Strategy Mosaic.

1. Do universal semantic layers even stand a chance of success in this era of rapid AI development?

The short answer: Not only do they stand a chance, but they are also becoming critical infrastructure for survival!

The semantic layer market is projected to be among the fastest-growing segments in data intelligence between 2025 and 2031, with a forecasted annual growth rate of 30%. This explosion is driven precisely by the advancements in artificial intelligence.

Without a universal semantic layer, advanced AI agents:

• Hallucinate metrics and provide inconsistent answers.
• Lack a proper audit trail (making it practically impossible to determine how they arrived at a final result).
• Operate in silos, which spells the end of a single source of truth.

2. Universal Semantic Layer vs. Self-Directing AI: The Reality of Both Approaches

We are currently witnessing a duel between two fundamentally different approaches in the market. Let’s look at the reality of both worlds.

Approach A: Decentralized, automatically generated semantic layer based on AI

This is a concept in which an AI agent either reads raw data, infers relationships, and builds a temporary knowledge graph, or executes queries using RAG/LLM. While it sounds appealing, in practice, it quickly hits the following brick walls:

• Security risks: An AI that discovers data on its own requires extremely broad permissions and is a recipe for data leaks and overexposure. Mosaic, on the other hand, federates data and enforces access control at the semantic layer.
• Lack of business context understanding: AI does not inherently know business logic. The term “revenue” in finance does not mean the same thing as “revenue” in marketing. AI will pick the statistically most likely definition, overlooking nuances such as deferred revenue or currency differences. Consequently, finance will report 10% higher revenue than sales, and the CEO will receive conflicting numbers from different AI agents.
• Inevitable hallucinations: LLM models are probabilistic by nature. Without clearly defined metrics, hierarchies, transformations, and row-level security, AI will have to guess at joins, filtering, and calculations. Asking the same question (“What is our NPS by region?”) in three different chats could yield three completely different values.
• Absence of Data Governance: Who owns the definition? What happens when regulations change (e.g., ESG, GDPR, SOX)? Who performs the audit? AI cannot take responsibility. In regulated industries (banking, pharma, public sector), this is an immediate dealbreaker.

The Breaking Point: This approach might work satisfactorily for a few months in a small company or a startup with a single team. The moment you cross the threshold of 50 metrics or multiple teams, the system inevitably collapses.

Approach B: Governed and centralized hybrid semantic layer (e.g., Strategy Mosaic)

This approach combines human oversight over business logic with AI acting as an accelerator. Of course, this approach also has its challenges if not executed correctly:

• The Bottleneck Risk: If data teams build the layer in silos without business alignment, the solution can become costly and struggle to adapt quickly.
• Requires Organizational Maturity: Without a strong data culture, an advanced tool will serve as nothing more than a “glorified dashboard” rather than becoming the operational foundation for AI.
• AI Assists, but Humans Decide: The AI within Mosaic excels at AI-assisted data modeling and semantic tagging, but final validation and business logic configuration must still be driven by humans. Skipping this human validation leads to the exact same pitfalls found in Approach A.

Overview: Which approach actually works in an enterprise environment?

3. What is the best approach moving forward?

Modern universal semantic layers, like Strategy Mosaic, solve exactly what AI cannot do on its own. They don’t move or copy data; instead, they virtualize it across various sources (Snowflake, Databricks, BigQuery…) and serve strictly governed, accurate metrics to AI agents via interfaces like MCP, Python, and REST.

The trend is clear. The semantic layer is the new critical infrastructure of the AI era. Without it, you will continue to get incredibly fast—yet entirely wrong—answers.

About the Company and the Strategy Mosaic Solution

Strategy Mosaic is a modern, AI-ready semantic layer (Universal Intelligence Layer) developed by Strategy. Unlike traditional semantic layers from the 1990s business intelligence era, Mosaic operates as a federated enterprise solution that connects modern data sources without data duplication, ensuring that all your AI agents speak the same business language.

Let’s keep the conversation going! Are your AI agents already using a unified semantic layer, or is your company still reconciling different interpretations of the same metrics?

If you are interested in how Strategy Mosaic could be integrated into your data ecosystem, or if you simply have additional questions about the future of semantic layers, please reach out to us. We would be happy to discuss your challenges and help you find the right solutions.

The post Data Chaos or Single Source of Truth? Pure AI vs. a Hybrid Semantic Layer appeared first on crmt.com.

The New Reality: From Silos to Synergy

Let’s break down why Operational Transfer Pricing (OTP), Tax Provisioning, and the Global Minimum Tax (GMT) are now inextricably linked:

1. Operational Transfer Pricing (OTP): The Foundation

OTP is about setting and managing the prices for transactions between related entities within a multinational enterprise (MNE). This includes everything from the sale of goods and services to intellectual property licenses and financial arrangements. Effective OTP ensures that these transactions are conducted at arm’s length, minimising audit risk and optimising the allocation of profits across jurisdictions.

The Traditional View: OTP was primarily a compliance exercise, often managed through year-end adjustments to align actual results with target margins.

The New Reality: OTP is now the real-time engine of your tax lifecycle. Every intercompany invoice, every cost allocation, and every profit split directly influences the financial results that flow into your general ledger. If your OTP isn’t executed accurately and consistently throughout the year, it creates a domino effect of issues down the line.

2. Tax Provisioning: The Measurement & Reporting

Tax provisioning is the process of estimating and accounting for income taxes in a company’s financial statements. It involves calculating current and deferred tax liabilities and assets, determining the effective tax rate (ETR), and providing disclosures in accordance with accounting standards (e.g., ASC 740, IAS 12).

The Traditional View: Tax provision teams would use year-end financial results (after TP adjustments) to perform their calculations.

The New Reality: The tax provision is no longer just a rear-looking exercise. It’s a forward-looking forecast that needs to anticipate the impact of OTP decisions and the impending GMT rules. Volatile or unexpected TP adjustments can drastically alter your ETR, making accurate quarterly provisioning a significant challenge. Moreover, the detailed financial accounting data used for the provision is the same data that underlies GMT calculations.

3. Global Minimum Tax (GMT / Pillar Two): The Ultimate Arbiter

The Global Minimum Tax, a key component of the OECD’s Pillar Two initiative, introduces a 15% effective minimum tax rate for large MNEs. If an MNE’s effective tax rate in a particular jurisdiction falls below this threshold, a “top-up tax” is triggered, payable either in that jurisdiction or in another.

The Traditional View: There was no global minimum ETR to contend with. Tax planning focused on optimising rates where possible.

The New Reality: GMT fundamentally changes the game. It’s not just about what your local tax rate is, but what your jurisdictional ETR is. And that jurisdictional ETR is calculated based on financial accounting numbers, heavily influenced by your OTP decisions and then measured through your tax provision process. Unexpectedly low ETRs, perhaps due to aggressive (or poorly executed) transfer pricing, will now result in a tangible, unavoidable top-up tax cost.

The Critical Connection: Data, Data, Data!

The convergence of these three areas highlights a singular, overarching need: a single source of truth for your financial data, granular enough to meet the demands of each function.

• OTP relies on real-time transaction data to ensure arm’s length pricing.
• Tax Provisioning consumes entity-level financial data to accurately calculate current and deferred taxes.
• GMT mandates the use of financial accounting data (with specific adjustments) to determine jurisdictional ETRs and top-up taxes.

If these data streams are disjointed, inconsistent, or lack the required granularity, your MNE faces:

• Increased Compliance Risk: Non-compliance with transfer pricing rules, inaccurate tax provisions, and miscalculated GMT liabilities.
• Higher Costs: Unexpected top-up taxes, penalties, and increased advisory fees.
• Inefficiency: Manual reconciliations, duplicated efforts, and delayed reporting.
• Lack of Strategic Insight: Inability to model the impact of different TP strategies on your ETR and GMT exposure.

Building the Integrated Tax Ecosystem

Transitioning to an integrated tax lifecycle requires a strategic approach, typically involving:

1. Data Transformation: Investing in robust data management systems to centralise, standardise, and enrich financial data across all entities. This means bridging the gap between ERP systems, consolidation tools, and tax-specific platforms.
2. Process Harmonisation: Breaking down departmental silos and fostering collaboration between transfer pricing, tax accounting, and international tax teams. Standardised processes ensure consistency and reduce manual handoffs.
3. Technology Enablement: Leveraging tax technology solutions that can integrate data, automate calculations, and provide real-time analytics for OTP, tax provision, and GMT. This could involve advanced TP engines, tax reporting software, and Pillar Two calculation tools.
4. Proactive Planning & Modeling: Using integrated data and technology to model the impact of different business decisions and transfer pricing strategies on your global ETR and potential GMT liabilities before they occur.

The era of isolated tax functions is over. Embracing the Integrated Tax Lifecycle isn’t just about compliance; it’s about strategic advantage. By connecting your operational transfer pricing, tax provisioning, and global minimum tax efforts through a robust data foundation, MNEs can gain unprecedented visibility, control costs, mitigate risks, and ultimately, navigate the complex global tax landscape with confidence.

Next steps

If your tax processes are still operating in silos, the risk is not theoretical. It is embedded in your data, your reporting, and your effective tax rate.

Start building your Integrated Tax Ecosystem today. Connect with our team to assess your current maturity and define the next concrete steps toward a fully aligned tax lifecycle.

The post The Integrated Tax Lifecycle – Why Your Transfer Pricing, Tax Provision, and Global Minimum Tax Are Now Inseparable appeared first on crmt.com.

Data management is now the pre-eminent concern. Legacy systems designed around the capture of monthly balances are simply ill-equipped to deal with the complex data demands of the modern era. Despite these shifts, research shows that only 11% of companies have completely transformed their financial close process.

The Strain of the Evolving Data Landscape

Modern finance functions are moving away from relying solely on strictly codified financial data. Today’s reality requires merging granular financial and operational data to drive business insight. This shift puts immense strain on the close process in three key areas:

• Data Accessibility: Finance functions need tools to quickly ingest data from a variety of sources without heavy IT involvement.
• Data Quality: Automated capture and validation controls are essential to maintaining high-quality data across new, unstructured data sources.
• Model Flexibility: Organizations need malleable, extensible consolidation models that can accommodate diverse, asynchronous dimensions.

Beyond Automation: Deep Insights with AI and NLP

Artificial intelligence (AI) has stormed to the top of the CFO agenda. Solutions like CCH Tagetik are already building proprietary AI capabilities into their platforms to transform the consolidation process:

• Accelerated Validation: AI isn’t just for mapping; it is used to investigate data anomalies and outliers, drastically speeding up the validation phase.
• Uncovering Hidden Drivers: AI identifies the drivers that most contribute to performance, allowing CFOs to enrich the story of their results with greater context.
• Narrative Reporting: Generative AI can automatically generate narratives and disclosures, saving significant time in the final reporting stages.
• The Future is Vocal: Natural language processing (NLP), which allows for speech-based report generation, is now clearly within reach.

The post Emerging Trends in Financial Consolidation: Is Your “Engine Room” Future-Proof? appeared first on crmt.com.

From Manual Checking to Smart Flow

For years, consolidation systems were built to just hold monthly balances. If you needed to understand a specific transaction or a new ESG requirement, you had to jump into a different tool or an endless spreadsheet.

Modern finance requires a nerve center, a place where data from your ERP, HR, and even external supply chain systems meet. Instead of wasting days “stitching” data together, a smart system handles the heavy lifting of collecting and validating data, so you can focus on what the numbers actually mean.

Start Where You Need, Grow When You’re Ready

One of the biggest worries for finance leaders is the “all-or-nothing” approach to new software. CCH Tagetik is designed differently. Think of it as a unified platform with specialized building blocks.

You can start exactly where your biggest headache is:

• The Core Close: Handle legal and group consolidation first.
• The New Standards: Plug in the ESG & Sustainability module when you’re ready to tackle CSRD or carbon reporting.
• The Tax Puzzle: Create harmony between finance and tax.

The real advantage? These aren’t separate tools that need to be “integrated” later. They all live in the same house and use the same data. When you change a number in one module, it’s instantly reflected in the others. No patches, no sync errors, just one version of the truth.

Let AI Do the “Grunt Work”

By moving to a modular but unified setup, you can finally put Artificial Intelligence to work where it matters. In CCH Tagetik, AI isn’t a gimmick; it’s a set of smart eyes that help you:

• Spot Anomalies: It flags weird data points before they mess up your final reports.
• Match Transactions: It automatically pairs up thousands of intercompany trades in seconds.
• Explain the “Why”: It helps identify the real drivers behind your performance, so you’re not just reporting history—you’re explaining it.

The post Why Your Monthly Close is Ready for a Makeover (and Where to Start) appeared first on crmt.com.