There is a fundamental difference between using AI and being an authentic AI practitioner. As enterprise AI moves beyond basic experimentation and enters core operational processes, this distinction is becoming critical to commercial success.
A solution may be technically impressive, but it must also be repeatable, auditable, and commercially viable. The ultimate objective is not to embed a Large Language Model (LLM) into every business process. It is to identify the most effective, reliable way to achieve a business outcome.
The Forecast Challenge
We recently addressed this challenge while refining a complex forecasting requirement. This process brings together data across multiple operational areas. While parts of this workflow had been automated previously, consolidating the data and executing the underlying forecasting logic still required significant manual effort from multiple stakeholders.
The Initial assessment suggested an ideal use case for a generative model:
- The Attempt: An LLM was tasked with ingesting the raw data, executing the analysis, and generating the final forecast.
- The Result: Functionally, the output was exceptional. However, a single run consumed an entire user’s daily token allocation.
- It was an impressive POC, but an unsustainable operational model.
Shifting Focus
This challenge prompted a critical shift in perspective. Rather than asking whether an LLM could produce the forecast, we asked what technology should run the process day-to-day.
The true requirement was a deterministic, auditable spreadsheet featuring explicit calculations that could be reliably re-run and reviewed by stakeholders.
Instead of deploying the LLM as the recurring runtime engine, we utilised it as an accelerator to write optimised Python code. Python and its standard data libraries were then deployed to handle the automated execution.
[Raw Data] ──> [LLM: Used once to write code] ──> [Python Script: Runs daily] ──> [Deterministic Output]
This required an iterative process – reviewing, testing, and refining the generated code until the logic was validated. Once established, the final output matched the quality of the direct LLM output, but with key operational advantages:
- Zero Runtime Token Costs: The code executes locally or via standard cloud compute without incurring model API fees.
- Determinism & Auditability: Calculations are explicit, reproducible, and immune to model hallucinations or unexpected variances.
- Targeted Maintenance: The LLM is re-engaged only when the core business logic or code requires updating.
- AI as a Capability Enabler
This case highlights a vital principle in enterprise AI adoption: AI does not always need to perform the final task itself.
Often, the greatest ROI comes from using AI as an accelerator to interpret complex requirements, explore architectural options, generate code, or design reusable pipelines. Once designed, conventional software automation is frequently better suited for daily execution.
Deciding to execute a process using code rather than an active LLM prompt does not make an architecture “less AI-enabled.” It demonstrates that the role of AI was chosen deliberately based on efficiency, latency, and cost governance.
Moving from AI Consumption to AI Practice
As AI adoption scales across an enterprise, financial and operational sustainability must be evaluated early:
- Unit Economics: Solutions that appear affordable during small-scale pilots can become cost-prohibitive when scaled across larger data volumes or wider user bases.
- Governance & Maintainability: Systems must remain explainable, testable, and decoupled from single-vendor model dependencies.
True AI practitioners look beyond the immediate capabilities of the latest model. They evaluate the full system architecture, balancing capabilities against cost, predictability, and long-term maintenance. Sometimes the most effective AI solution isn’t the one that relies on AI at every step—it is the one that applies it precisely where it creates the highest value.
Optimising Your Enterprise AI Strategy
If your organisation is looking to build scalable, cost-effective AI solutions that balance performance with governance, Coeo can guide your technical roadmap – Contact the team to get started.