This article explains why Axum invests in AI, what it will (and won’t) do, and the guardrails that keep our work accurate, secure, and human-centered.
📍 Step 1: Understand Our “Why”
- Improve outcomes and speed. AI helps us draft, analyze, and reason faster so we can focus on judgment, relationships, and impact. We see value when AI helps us critically explore common workflows and puts senior leaders (PMs, SPMs and Partners) over AI governance, not just by adding tools.
- Work with the grain of our teams. Top consulting guidance favors central governance (Our AI Team) + project team innovation .
- Meet our teammates where they already are. Enterprise adoption is widespread not just at Axum but with reports show most knowledge workers are using generative AI and many employers now provide approved tools; we’re enabling safe, standardized options at Axum and we must lead the way.
✅ Tip: AI is a force multiplier for good process. We get compounding benefits when we pair AI with clear roles, clean data, and measurable goals.
📍 Step 2: What AI Will — and Won’t — Do
- AI will:
- Accelerate drafting, summarization, data exploration, and knowledge search (e.g., first draft research, data checks, policy lookups).
- Surface patterns and options for you to decide, with human-in-the-loop review.
- Embed into our consulting workflows (requests, checklists, QA) so gains show up in team metrics (cycle time, error rate, satisfaction)
- AI won’t:
- Make final commitments, set policy, or override contracts.
- Use restricted data sources without explicit approval.
- Replace accountability, humans own outcomes.
🚧 Note: We will first realize project-level benefits before enterprise-wide P&L impact appears. That’s expected as foundations and change management mature. Lets remember this will be iterative with everyone chipping in.
📍 Step 3: Where You’ll Use AI at Axum
- Project & partner workflows: intake summaries, risk flagging, meeting notes → action items.
- Sustainability & data QA: anomaly spotting, documentation drafts, evidence linking (with source trails).
- Client ops: first-draft responses, knowledge lookup, multilingual support (human review before send).
✅ Tip: We will start with low-risk, high-volume tasks; escalate to higher-stakes work once quality metrics are consistently met
📍 Step 4: Data, Privacy & Security
- Use approved Axum AI surfaces (Axum AI Help Center and Axum Portal). Don’t paste secrets or third‑party confidential data into non‑approved tools.
- Handle data according to Axum’s classification (e.g., public / internal / confidential / restricted). When in doubt, use synthetic or masked data and ask your PM, SPM or Partner
- We align to recognized Trustworthy AI dimensions (transparent, fair, robust, privacy‑respecting, safe, accountable) and maintain role‑based access and retention controls.
🚧 Note: No personal data in screenshots. (Axum policy)
📍 Step 5: Roles, Access & Support
- Baseline access: All staff get the Axum AI Help Center and core copilots.
- Additional connectors (e.g., data lakes, CRM, code repos): request via the contact the AI team please check our Support and Feedback Section on how to do so
- Who’s accountable: You own your outputs; PM, SPMs and Partners will then approve anything before publishing externally.
✅ Tip: If an AI output feels off, stop and escalate with your PM, SPM or Partner. Don’t just proceed if your gut feels like things are off.
✨ Quick Summary
- ✅ AI helps us work faster and better when embedded in real workflows.
- ✅ Guardrails follow Trustworthy/Responsible AI standards (privacy, fairness, reliability, accountability).
- ✅ Start with low‑risk, high‑volume tasks; measure quality and iterate.
- ✅ Use approved tools only; handle data with care; escalate issues quickly.
🔗 What’s Next?
- Read our Responsible AI Guidelines
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