When Enterprises Adopt AI, the First Hurdle Isn't the Model—It's the Data Scattered Across Excel Files

Where AotterClam AI fits: make the data usable first, then choose the AI you want to use

AotterClam AI's starting point is that enterprises don't necessarily need to rebuild their entire ERP in a short period of time, nor do they need to first move all their data to an unfamiliar new system before starting data applications. Based on our current product design, AI helps understand sources like Excel and CSV and generates reviewable organization definitions; data is then validated, organized, and loaded into the warehouse according to fixed rules, after which the enterprise can use permission management, audit logging, BI dashboards, and AI Q&A features. Deployment and model options are then discussed according to the enterprise's cloud, VPC, or on-premises requirements. 5

The difference between this approach and an AI-ERP shouldn't be oversimplified into "which one is definitely better." An AI-ERP is suitable for enterprises that happen to be ready to replace their core operating system and want to redesign both process and data standards together; but if a legacy ERP still supports a large amount of daily operations, a full replacement might involve data migration, process overhaul, staff training, external system integration, and downtime risk. A data governance platform like AotterClam AI can instead first process the data already being output by different sources, allowing an enterprise to gain cross-system analytical capability while keeping its core systems intact. This is one path to reducing part of the migration cost — it doesn't mean every company should never replace its ERP.

In fact, not long ago, Aotter itself, during interviews at a traditional enterprise, discovered that directly rebuilding the ERP might disrupt processes that already deeply depended on the legacy system (since these were human-machine collaborative processes tightly bound to specific employees), and so began evaluating an alternative approach: building a data warehouse using existing reports. This case demonstrates the value of "first integrating the data already being output by the systems"; if a legacy system has already stopped being maintained, poses major security risks, or can no longer support core operations, rebuilding may still be the correct choice. What really should be compared is the overall transformation cost, operational risk, future maintainability, and expected benefits — not simply whether the product name contains the word "AI." 9

Aotter and Formosa Digital Intelligence Technology announced a partnership in 2026, which includes jointly developing enterprise-grade AI data governance products and services, with the hope of turning the digital transformation experience of traditional industries into solutions that can be offered to other enterprises. 14 Our view is straightforward: enterprises don't need to chase the AI wave by sending every piece of data to a model, nor do they need to wrap every question in a chat interface from the start. What matters more is first managing data into an understandable, traceable, updatable organizational asset, establishing clear permissions and use cases, and then choosing models, connecting tools, and adjusting processes according to real work needs. Models will change, APIs will change, prices will change — but an enterprise's understanding of its own products, customers, orders, and way of operating should remain within a data foundation the enterprise itself can manage.

AotterClam AI currently has very fast implementation examples among manufacturing, traditional industries, and any enterprise using similar systems to Digiwin ERP, traditional CRM, or CDP management platforms. Perhaps after talking with us, you'll find that you don't actually need to go through the trouble of digging everything up and rebuilding your systems to conveniently leapfrog ahead. Anyone with similar needs is welcome to fill out the form and get in touch with us. Implementation doesn't have to start with writing up a grand AI vision — it can start with a report that someone has to manually organize every month, a question that often goes unanswered, or a reconciliation process that's constantly repeated. Clearly defining the problem first, taking inventory of the data, and then jointly deciding which part is worth automating will get you much closer to real transformation than just buying a pile of tools first.

Lastly, let us introduce ourselves again: we are Aotter, the Aotter behind AotterGirls. Many people may first know us through our content brand, but Aotter has continuously built data and technology products all along — from app tools, content, and ad tech, to first-party data, data governance, and enterprise AI. For more than a decade, what we've cared about has always been how to turn knowledge, content, and data into usable value faster; now we're applying that experience to AotterClam AI, hoping to help enterprises gradually build data into their own capability, without needing an entire data engineering team from day one.

AI is hot, but data can't just chase the hype. Data needs someone to define it, someone to be responsible for it, and it needs to be able to be rerun, validated, and traced whenever rules change. When enterprises are willing to put in this seemingly unglamorous foundational work, AI has a chance to evolve from a one-off answer into a capability that can be built up over the long term.

Original by: Dr. Porkchop (Peng-han Su) (Aotter Co-founder and Deputy CEO) | Adapted by: Konois (Lun Hsieh) (Aotter Co-founder and CEO)

References

1. Salesforce Marketing Statistics: First-Party Data and Data Unification
2. PwC 2026 Digital Trends in Operations Survey
3. EMARKETER: 2025 Trend, B2B Firms Are Laser-Focused on First-Party Data
4. 2025 Manufacturing AI Adoption Survey Report
5. AotterClam AI Official Product Introduction
6. AI Foundation: Good data is what lets AI take root — the 1234 principles of data governance
7. McKinsey: The State of AI in 2026
8. McKinsey: The State of AI, How Organizations Are Rewiring to Capture Value
9. BusinessNext: More than just AotterGirls! Aotter builds AotterClam AI, targeting the B2B enterprise market with two core strengths
10. Aotter Official Company Introduction
11. AotterTrek Official Introduction: History of First-Party Data Development
12. Ministry of Digital Affairs: Legislative Yuan Passes Third Reading of the Artificial Intelligence Basic Act
13. European Commission: AI Act Regulatory Framework
14. Aotter: Announcement of Partnership Between Formosa Digital Intelligence Technology and Aotter