How high-growth organizations use automation and AI to transform operations

Automation and the use of AI is a strategic necessity. Organizations that build fluency, empower their people, and embrace an attitude of strategic change are the ones that gain efficiency, unlock smarter decision-making, and scale sustainably in the years ahead. Automation is about progress, not perfection!

But with all the noise, many finance leaders and their teams still struggle with the same foundational question: Where do we begin?

Where to begin: Fluency as the foundation of automation

While automation promises efficiency and smarter decision-making, the noise around AI can create uncertainty. The first step is building organizational fluency — ensuring teams understand the capabilities, vocabulary, and intent behind automation initiatives.

Fluency is the starting point

Automation is most effective when team members understand both the what and the why, and when leaders reinforce that message consistently. This creates an environment where team members feel safe to learn through experimentation.

Before implementing new tools, organizations must build fluency — a clear, shared understanding of what automation is — and isn’t, how it fits into business workflows, and why it matters. Teams are far more likely to embrace new technology when they:

• Understand the purpose behind the change
• See clear alignment with business priorities
• Feel equipped to experiment and learn

Automation fluency isn’t a one-time milestone — it’s an ongoing journey.

Create comfort with change

Technology doesn’t fail — change management does. Experienced finance teams especially want to know the why before supporting any new system. Clarity builds trust, and trust drives adoption. Successful implementations:

• Anchor change with strong communication and clear purpose
• Include early and ongoing involvement across the team and gather feedback
• Set clear expectations and phased, manageable rollouts

Organizations that invest in training, foster experimentation, and create space for small early wins build lasting comfort with automation. Transparent communication, human-centered planning, and celebrating progress all help reduce resistance and build momentum.

Experienced teams need clear purpose

Any AI or automation project should be treated with the same rigor as any other major implementation — clear objectives, stakeholder alignment, and phased rollouts. Do not embrace a new tool until you know:

• The specific problem being solved
• The expected measurable results
• How the initiative aligns with strategic priorities
• Guard rails for team safety

Your teams will not jump into a new project until they know the purpose behind it, what the measurable outcome is expected to be, and how it links to the company’s broader strategic priorities.

Automation in action

Organizations are enhancing their close process, reporting, and analytics through purpose-built automation tools, allowing finance teams to work with more accuracy.

What all successful implementations share

• Clear requirements
• Process mapping before tool selection
• Stakeholder alignment — all users are united to change
• Continuous communication

Ultimately, success is more about people and processes than about the tools themselves.

Looking ahead: Data is the differentiator

Organizations that treat their general ledger as an operational asset rather than a reporting file are better positioned to unlock automation, analytics, and strategic insights to better foster growth. Granularity, structure, and data governance will matter more than ever. Finance transformation will hinge on three pillars:

People

• Upskill teams in analytics, automation, and AI literacy so you create comfort in seeing outputs
• Anchor your change with purpose so your teams know the road map they’re on and are empowered to drive change
• Reinforce adaptability and critical thinking — this is essential as automation increases

Processes

• Standardize and streamline before automating
• Clean up all data — make it available and know how it’s being used
• Prioritize governance, controls, and auditability

Data technology

• Treat technology as an enabler, not the finish line
• Ensure strong data quality — the benefits of automation depend on it
• Select tools that integrate well and provide measurable return

Generative AI is fundamentally changing how organizations access and interact with data. Instead of depending on engineers, manual downloads, or complex dashboards, team members can now ask natural-language questions and extract insights directly from datasets that may have been previously inaccessible.

Start small, but start now

Automation doesn’t require a massive transformation on day one. The most successful organizations begin with one low-risk, high-impact process. They automate it, measure the results, and share the story internally. That momentum becomes the foundation for broader adoption.

Leaders play a pivotal role: modeling curiosity, encouraging experimentation, and showing teams that AI is here to augment people — not replace them. When leaders explore new tools and ask questions, teams follow. Organizations thrive when they encourage continuous learning and improvement.

How will your organization choose to lead in 2026?