The Big Idea
Companies are under pressure to develop an AI strategy. In response, many are moving quickly to add AI to existing work:
- Summarize this report.
- Extract information from this document.
- Draft this analysis.
- Answer questions about this data.
- Automate this checklist.
These applications can save time. But speed is not the same as improvement.
AI amplifies the process surrounding it. If that process is sound, AI can make it faster, more consistent, and more responsive. If the process is fragmented, poorly controlled, or based on incomplete information, AI can magnify those weaknesses.
Imagine a monthly forecast assembled from spreadsheets maintained by several departments. The numbers arrive at different times, assumptions are inconsistently documented, and important operational changes are communicated through email or meetings.
Adding AI might help finance consolidate the spreadsheets or write the variance commentary more quickly. But it does not ensure that the forecast reflects a delayed customer order, a staffing shortage, an expiring supplier agreement, or a sales opportunity that slipped into the next quarter.
The company has accelerated the production of the forecast without improving the process that determines whether the forecast is reliable.
This distinction matters because AI can make flawed processes appear more capable. The output arrives faster. It is well written. It may include confident explanations and attractive charts. That polish can obscure the fact that the underlying workflow still lacks complete information, clear ownership, defined controls, or a reliable way to handle exceptions.
Before applying AI, companies should ask a more fundamental question:
The strongest AI initiatives do more than make individual tasks easier. They reconsider how information enters the process, how decisions are made, which rules should be deterministic, where human judgment is necessary, and how exceptions are identified and resolved.
That is where AI begins to create lasting business value.
What This Looks Like in Practice
Consider the monthly financial forecast.
Finance collects spreadsheets from department leaders, exports actual results from the ERP, reviews the sales pipeline, and asks managers to explain significant changes. Assumptions arrive at different times and in different formats. Some changes are buried in emails or meeting notes. Finance spends days assembling the forecast before it can begin analyzing it.
AI can consolidate spreadsheets, categorize expenses, and draft variance commentary. That saves time — but it does not fix the underlying process.
A redesigned process connects actual results, contracts, sales opportunities, hiring plans, operational drivers, and departmental assumptions. Defined rules handle calculations and reconciliations. AI identifies changes, tests explanations, and highlights assumptions that conflict with the available evidence.
If a major customer delays an order, a supplier increases prices, or hiring falls behind plan, the effect is traced through revenue, margins, cash flow, and the forecast. Material exceptions are routed to the appropriate person for review.
Finance is no longer producing the same forecast faster. It is operating a more continuous, controlled process that detects changing assumptions earlier and gives management more time to respond.
Three Things to Look For
Ask your team these questions before approving the next AI initiative:
Where does the process get its information?
If employees must still search across spreadsheets, emails, documents, and disconnected systems, AI may be working from an incomplete picture.
Which decisions should follow defined rules?
Calculations, thresholds, approvals, reconciliations, and required controls should not depend entirely on a probabilistic AI response.
What happens when something is unusual?
A trustworthy process needs a way to detect exceptions, present the supporting evidence, assign responsibility, and document the resolution.
The goal is not to eliminate people from the process. It is to stop using people as the integration layer between disconnected information and incomplete workflows.
How We're Approaching It
Farsight DS is built around three complementary layers.
The first is a connected data foundation that brings together structured information from business systems and unstructured information from documents, files, communications, and company knowledge.
The second is a process layer where calculations, controls, checklists, approvals, and business rules can be executed consistently.
The third is an AI support layer that helps interpret information, identify patterns, explain changes, investigate exceptions, and support faster decisions.
This separation is deliberate. AI should be used where interpretation and reasoning add value. Deterministic logic should be used where the answer must follow a defined rule. People should remain involved where accountability, expertise, and judgment matter.
The result is not AI placed on top of the way work has always been done. It is a better way to organize the work itself.
One Thing Worth Reading
McKinsey's global AI research found that, among 25 organizational attributes studied, workflow redesign had the greatest effect on whether companies saw an EBIT impact from generative AI. Yet only 21% of respondents using generative AI said their organizations had fundamentally redesigned at least some workflows.
That gap captures the opportunity: most companies are adopting AI, but relatively few are redesigning the work around it.
Read "The State of AI: How Organizations Are Rewiring to Capture Value"
A Question for Your Team
Where are you using AI to accelerate a process that probably should not exist in its current form?
If you have an interesting example, email us. We read the responses.
Ready to look at your own processes?
We'll walk through where AI, deterministic rules, and human judgment each belong in your workflow. 30 minutes — bring one process you're considering for AI.
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