Building enterprise value

By translating audit results into root-cause themes, governance insights, and forward-looking risk signals, internal audit can enable faster action and better prioritization. This shift can position the chief  audit executive as a strategic adviser who strengthens confidence in management decisions.

Internal audit can add value by delivering timely points of view on what is changing, which controls may break under stress, and what trade-offs management is making. This can position internal audit as an active adviser in executive decision-making, improving decision velocity and confidence, rather than as a retrospective reviewer.

Audit leaders should be able to understand technology risks around data lineage, access and segregation of duties, model governance, third-party data exposure, and human and machine decision accountability. They also should be able to use technology to modernize how assurance is delivered through continuous risk assessment, analytics-enabled auditing, and workflow transformation approaches.

In an age of cloud reliance, third-party concentration, and AI-enabled threat vectors, boards may want clearer assurance that cybersecurity and transformation spend is translating into risk reduction. By elevating reporting from compliance status to enterprise resilience insights, internal audit can reinforce its role as a trusted strategic adviser to leadership and the audit committee.

OECD Pillar Two is pushing multinationals to rethink end-to-end modeling, data readiness, and ongoing reporting across jurisdictions. A challenge is shifting from interpreting the rules to operating the rules through faster reconciliation of tax, finance, and tax and finance inputs. A stronger operating model can enable more timely analysis, clearer governance,and scalable execution across countries and entities.

Today’s tax leaders may be prioritizing “tax-sanitized” data, automation, and governance so tax can scale compliance while improving accuracy and cycle times. This can include building repeatable data pipelines and ownership models—not one-off reporting fixes.

The tax leader role appears to be increasingly outward-facing, with executives, boards, investors, and regulators expecting crisp narratives that translate technical tax positions into business outcomes and risk posture. This can elevate the importance of proactive stakeholder relationships and consistent, decision-useful messaging.

Today’s tax leaders are likely expected to maintain a defensible posture while also identifying opportunities that enhance performance and stakeholder value through an efficient tax posture. A goal is alignment, including a tax strategy that supports enterprise objectives while managing risk.

Controllership may be expanding from financial stewardship to strategic decision support and business partnering, connecting accounting outcomes to performance, risk, and enterprise trade-offs. This shift can help finance leaders shape priorities earlier in decisions, not only to validate outcomes at period-end.

Finance leaders may be treating data as infrastructure, using governed data platforms, robotics, analytics, AI, and cloud strategies to improve integrity while increasing efficiency, with a goal of faster outcomes, stronger controls, and better return on investment.

AI and GenAI are moving into core finance workflows, increasing expectations for productivity and better analysis. But trust, governance, and integrity tend to remain adoption barriers. Today’s controllers should define where automation fits and where judgment should remain, helping to ensure that data and model integrity support, rather than undermine, confidence in outcomes.

As controls evolve toward more data-driven, continuous “control-intelligent” models, controllership talent strategies may be shifting from staffing for today to developing for tomorrow. Recruiting, retention, and reskilling become important for building capability in analytics, automation, and enterprise partnering, without losing the stewardship foundation.

FP&A leaders may be expected to move beyond annual reporting and scenario planning into proactive, predictive decision support on things like capital allocation, pricing, cost actions, and investment pacing. Scenario planning should be always-on, with governance that can pivot quickly as macroeconomic assumptions change.

FP&A teams are often held back by siloed data, legacy systems, and manual workarounds that can decrease speed and trust. By connecting operational and financial signals through common definitions and tighter system integration, FP&A leaders can improve speed, visibility, and forecast quality while reducing manual reconciliation.

FP&A teams are often overextended, consumed by data gathering, reconciliations, reporting packs, and one-off asks, leaving limited capacity for transformation and higher-value analysis. Changing this involves deliberate upskilling and operating model choices on what to standardize, automate, centralize, or enable through self-service, so insight work is protected.

GenAI and automation are becoming primary drivers of FP&A transformation, enabling faster data ingestion, more dynamic scenario modeling, and emerging finance agents that augment analysis and insight generation. To help them keep speed and trust, leaders should strengthen governance, ensure-executive level explainability, and maintain tight controls on data quality and assumptions.

With rising talent competition, geopolitical instability, and rapid technological advancement, companies are fundamentally rethinking the traditional playbook. Instead of lifting intact processes to lower-cost locations, leading organizations are asking: “How do we digitize and automate end-to-end before deciding where—or whether—we need physical hubs at all?” The sequence has flipped: Transform the work first, then optimize the footprint.

As enterprises shift from valuing cost reduction to demanding measurable business outcomes—revenue growth, margin improvement, competitive advantage—GBS must build true transformation capabilities or deliberately partner with specialized providers to access them. This isn’t about continuous improvement teams running Lean Six Sigma projects. It’s about outcome architects who coordinate cross-functional initiatives, data scientists who instrument value creation, and agile teams that deliver in sprints. Whether built in-house or borrowed from partners, transformation is becoming a distinct discipline, measured not by projects completed but by enterprise value unlocked.

Generative AI, intelligent automation, and cloud platforms are advancing at exponential speed—fundamentally changing what’s possible and dramatically compressing transformation timelines. What once required multi-year implementations can now be deployed in months. But technology alone isn’t the answer: Success requires reimagining workflows, ensuring data quality, and building the skills to design,govern, and optimize AI-powered processes. Organizations that treat technology as just a faster version of old processes will likely fail; those that redesign around its capabilities will be better positioned to leap ahead.

GBS success used to mean accessing large pools of low-cost labor. Now it means accessing specialized digital talent—AI engineers, automation architects, data scientists, outcome orchestrators—in a fiercely competitive market. The talent strategy has fundamentally inverted from “many and low-skilled” to “fewer and highly skilled.” This changes everything: where you locate, how you recruit, what you pay, and how you structure work.

Investors often compare companies across multiple frameworks and rating approaches with limited standardization. IR leaders should deliver disclosures that are reliable, supportable, and decision-useful amid competing methodologies and stakeholder demands.

MiFID II unbundled sell-side research and access, increasing transparency in the EU’s financial market, protecting investors, and shifting more engagement directly to issuers. As a result, IR leaders may be under new pressure as companies engage directly with investors and face new disclosure on product governance requirements that add to documentation processes.

Some investors today may be looking for tech-enabled, sustainability-focused, and sustainable companies. Meanwhile, companies may want to fund these transformation efforts via investors. This involves IR services that bring widespread retail investor growth through customized outreach plans, peer group research, targeting tools, and shareholder analysis to determine gaps between current and potential investors.

Many IR teams are facing new sustainability reporting standards, MiFID II regulations, and increased demand for virtual investor engagement—all on a restricted budget. Technology investments may be needed, but many companies haven’t built an IR strategy due to the increasing volume of communication that may be required to attract investors.

In a volatile interest rate environment, liquidity strategy can shift toward precise cash positioning that reduces idle cash while preserving flexibility. This means finance leaders should consider strengthening their forecasting and investing in policy guardrails to maintain coveted headroom and committed liquidity as market access tightens.

The challenge may no longer be only about hedge execution, but also about hedge effectiveness and the accounting and controls that explain earnings impacts. Stronger visibility and disciplined internal controls can help planning cycles remain credible even as tensions accelerate.

As treasury digitizes payments and expands straight-through processing, exposure to payment fraud, impersonation, and account compromise can increase, creating direct financial and reputational risk. Today’s treasury leaders should strengthen last-mile controls through segregation of duties, verification protocols, bank portal governance, and anomaly monitoring.

GenAI appears to be gaining traction in treasury for cash forecasting, cash positioning, and market risk management, as well as raising expectations for faster insight and decision ready reporting. Treasury leaders should capture value by improving data quality and model governance enabling capacity to be focused on solving exceptions, modeling scenarios, and driving greater visibility into cash positions.