Automation is not AI and it's hurting your finance team

Here’s a startling reality: BCG’s Center for CFO Excellence reports that only 45% of finance executives can quantify the ROI from their AI initiatives, with the median reported ROI at just 10%. Furthermore, only 44% of surveyed executives have moved their AI initiatives beyond pilot phases into scaled deployment. 

This disconnect isn't because the technology failed, but because many finance leaders fail to understand that their “AI deployment” is just an automation upgrade.

For finance and accounting teams that sit on mountains of messy, unstructured data and tackle decisions that can swing millions of dollars, this hiccup can be a make-or-break factor.

Finance teams have always embraced technology with open arms; they went digital first, and now they are deploying AI, or is that just automation? Well, if this is your AI pilot, it screams of sophisticated automation:

• Research workflows: Generating routine reports, benchmarking against standardized metrics, and scheduling regulatory updates to keep everyone informed.
• Financial data processing: Automating the boring, repetitive stuff like processing invoices, handling accounts payable and receivable, and sorting transactions based on set rules.
• Document management: Automatically filing contracts, routing compliance documents to the right people, and pulling out basic data points.

The gains are obvious: JPMorgan Chase saved 360,000 manual hours annually by deploying automation technologies across its operations in the commercial banking division alone. The RPA market reached $22.79 billion in 2024, driven by these efficiency gains

It’s no surprise that when CFOs see these systems running, they believe: “We’re using AI effectively.”

Until exceptions happen and automation cannot handle it, or it breaks when the data format changes. If your vendor sends an invoice in a new format, your automation stops working. If a contract contains non-standard language, automation cannot interpret it.

And someone asks: “Why won’t our system actually think about this?”

That’s where the misconception kicks in: Wait! Are we truly AI-deployed?

Automation follows pre-set rules and commands to execute predefined tasks. Automation doesn’t ‘think’, it’s obedient to the rules you give it, and follows them religiously.

So, when it runs into a never-before-seen contract type, a weird accounting scenario, or a sudden change in regulations, it doesn’t know what to do. It lacks contextual insights, so it can see that ‘the numbers do not add up’, but it has no clue why. 

Similarly, when something doesn’t fit the “normal” rules or workflow, like a complex or unusual transaction, automation struggles to handle it without human intervention. It also cannot learn from new information and adjust, so it needs constant reprogramming.

Beyond the hassle, here’s what happens when you rely on automation alone:

• Contract analysis - Automated systems may process large volumes of data efficiently, but they struggle with nuanced, interconnected tasks like multi-document contract analysis.
• Cross-system validation - Automation often fails to reconcile data inconsistencies during ERP migrations or when different platforms don’t communicate properly.
• Complex accounting research - Applying detailed accounting standards like GAAP or IFRS to tricky, real-world cases often requires human judgment that automation lacks.
• Revenue recognition complexity - Figuring out when and how to recognize revenue in complicated deals (e.g., multi-element arrangements) can’t always be automated.
• Regulatory intelligence - Automation cannot interpret trends in SEC comment letters because it relies on understanding business context and strategic implications

AI doesn’t just crunch numbers. It actually gets what it’s looking at. AI tools can understand data (recognize patterns), learn from it (use data to improve), and act on it (predict or find unique insights without being programmed to do so).

That means AI can analyze your unstructured contracts, interpret accounting guidance, and adapt to new situations without human intervention.

Automation tools can only fetch key dates and dollar amounts from a 50-page master service agreement. But with AI, you can spot performance obligations under ASC 606, flag clauses that could trigger revenue recognition issues, and get tailored suggestions for adjusting your accounting treatment based on similar past contracts.

Context Awareness: Real AI doesn’t just pull out numbers and dates from a contract. It connects the dots and understands the relationship between fragmented information. It knows how terms of the contract ripple through your revenue recognition, cash flow, and compliance obligations.

For instance, AI can pull out contract value, duration, renewal terms, as well as spot ASC 606 performance obligations and ASC 842 lease accounting triggers from a contract, and instantly link it to related quotes, orders, invoices, and amendments to give you the entire financial picture.

Adaptive Reasoning: Automation panics and falls apart when something doesn’t fit the script. AI, on the other hand, adjusts. Throw it a brand-new contract structure, and it’ll understand the accounting principles, regulations, and scout for similar past cases to give you a solid, relevant answer.

If you need help with a tricky accounting question, AI can dig through FASB’s official guidance and interpretation docs, real-world use cases, and give you a clear answer with citation.

Predictive Insights: AI tells you what happened and why it happened. It spots patterns you didn’t even know to look for, like if some contract terms have higher default rates, or accounting treatments that often trigger audit adjustments, and then it tells you what to do about it.

You can have AI go through quarterly and annual reports to uncover trends, disclosures, and red flags that no one could have spotted. Think, financial metrics, risks, and storylines that matter, like spotting recurring audit adjustments or seeing early signs of compliance issues in peer companies.

Natural Language Understanding: You can actually talk to it and have a conversation about complex topics. Ask how your, say, lease accounting approach stacks up against industry peers, and it gets the nuances behind the question. No rigid query formats, just a conversation.

If you want to know “How do competitors handle variable consideration under ASC 606?”, all you need to do is ask AI. It can go through multiple SEC filings to pull the exact disclosures and data points you need, and explain them to you in simple language. 

Continuous Learning: The more you use AI, the smarter it gets. It learns from feedback and new data, improving over time, without you having to constantly rewrite rules or reprogram it.

The more financial data AI processes, the better it gets at reading your company’s financials and patterns, like linking contracts to invoices, policies to memos, and amendments to agreements, with sharper accuracy over time.

That’s the leap from “rules-based” to “intelligence-driven.”

Here’s how you can tell automation apart from AI at a glance:

Intelligent Automation

Artificial Intelligence

- Follows predetermined logic trees

- Comprehends relationships and implications

- Identifies known patterns in structured data

- Handles novel situations without reprogramming

- Makes existing workflows faster and more accurate

- Identifies trends and provides recommendations

- Flags unusual cases for human review

- Engages in sophisticated conversations

- Generates reports and data exports in predefined formats

- Learns and adapts from new information

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Here’s the thing: not every problem needs AI. Sometimes, good old automation is exactly what you need. Other times, you need a thinking assistant to help you figure out the ‘hows’ and ‘whys’. Knowing which technology to use can save you a lot of hassle and money.

If your task is high-volume, repetitive, and follows the same rules every time, go with automation. It’ll handle:

• Accounts payable/receivable - processing invoices and payments on autopilot
• Basic compliance reporting - generating the same filings on schedule
• Data entry and validation - moving info from one system to another without typos
• Scheduled reconciliations - checking balances against a fixed set of rules
• Standard calculations - tax, depreciation, anything with a formula that never changes

But if your challenge is complex, full of exceptions, or requires actual reasoning, that’s AI territory. AI steps in for:

• Complex contract analysis - reading between the lines in multi-element agreements
• Research and interpretation - applying accounting rules to tricky, unique situations
• Strategic decision support - finding trends in market data and peer benchmarks
• Exception investigation - figuring out why numbers don’t match or patterns shift
• Cross-functional insights - connecting the dots between financial results and operational drivers

Automation handles the “do it faster” work. AI handles the “help me think this through” work.

Here’s how finance and accounting teams can put AI in action and make processes faster and smarter:

Most automation tools can pull dates and dollar amounts from a contract, but that’s barely scratching the surface. True AI reads contracts contextually. It understands how clauses impact revenue recognition, flags non-standard terms that could change accounting treatment, and assesses performance obligations for compliance requirements.

Manual research feels like hunting for a needle in a haystack, and it tampers with timely decision-making. With AI, there’s no hunting for data. All you need to do is ask AI a question, and it gets back an immediate, citation-backed answer. You can uncover trends, disclosures, and red flags without reading hundreds of pages manually.

Many finance pros despise writing accounting memos because it requires sifting through complex rules and information from multiple sources to create an easy-to-follow analysis. It’s tedious and requires hours of research, cross-referencing guidance, and formatting for audit readiness. AI cuts that days-long grind into minutes of guided analysis.

Numero.io can help you level up your AI pilot implementation and take on bigger financial challenges. It helps you pull the data you need and make sense of it too. 

With Numero’s true AI intelligence, you gain a competitive advantage, speed in decision making, get better accuracy, and uncover strategic insights that can fuel successful business decisions. 

Numero is built from the ground up as a true AI-native platform, designed to handle finance challenges head-on. Numero helps with things like search, summarization, and metadata extraction, with its own proprietary small language model trained on over 70,000 financial documents. Also - When financial data lives across multiple systems - contracts in SharePoint, pricing in Salesforce, billing in NetSuite - Numero agents bridge these disconnected sources, identifying mismatches between systems and automatically updating each platform with consistent, accurate information

What makes Numero stand out is its ability to connect with other software systems and keep data in sync. It can detect, report, and flag any mismatches in repositories and documentation, so you’re always on top of the latest information and updates. 

There’s more. Numero guarantees zero hallucination and is built with enterprise-grade security and multi-tenancy.

Safe to say, it can handle both parts of automation and AI for you, while you focus on what matters to you. 

Ready to experience real AI? Book a demo with Numero today!

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