77% of organizations are adopting AI to streamline operations—but here’s the catch: AI can schedule your meetings, draft reports, and automate tasks, but it won’t fix your broken processes or make bad data magically useful. If your operational framework is duct-taped together, AI will only speed up the chaos. So, where does AI actually help? Let’s break it down. ⬇️
The Reality Check: AI and Its Flawed Foundations
- Bad Data + AI = Bad Outcomes Garbage in, garbage out. AI thrives on training data, but if your organizational data is disorganized or incomplete, the outcomes will reflect those flaws. For instance, relying on outdated or inconsistent data will hinder AI’s ability to deliver meaningful insights. For example, ChatGPT writing your quarterly report is great… until you realize it’s referencing sales figures from 2015 because nobody bothered to clean up the database.
- AI Won’t Fix a Broken Process Automating chaos just creates faster chaos. DeepSeek and ChatGPT can schedule a thousand meetings in an hour, but if your operational framework is held together with duct tape and vibes, those meetings will only amplify the dysfunction. Case in point: automating your daily standup doesn’t magically make your team understand Agile principles.
- AI Can’t Ensure Compliance Sure, AI tools can remind you about deadlines and assign tasks, but they can’t sit in on execution sessions, identify compliance gaps, or ensure deliverables are up to snuff. Imagine having ChatGPT as your Scrum Master: you’d get great motivational quotes, but good luck managing scope creep.
- AI Doesn’t Do Escalations Your favorite AI tool isn’t going to pick up the phone and explain to the VP why the Q3 release is delayed. Humans—preferably competent ones—still need to unblock impediments and escalate issues effectively.
So, What’s the Actual Value?
Let’s break it down into a practical matrix to see where DeepSeek, ChatGPT, and a good old-fashioned TPM (Technical Program Manager) shine—and more importantly, how you can use this comparison to make smarter operational decisions in your own context.
Responsibility | DeepSeek/ChatGPT | TPM |
---|---|---|
Scheduling Meetings | ✅ Automates Well | ❌ Manual Effort |
Drafting Initial Documents | âś… Highly Effective | âś… Tailored Output |
Process Improvement | ❌ Needs Context | ✅ Strategic Insight |
Compliance Monitoring | ❌ Lacks Judgment | ✅ Human-Driven |
Escalations & Unblocking | ❌ Can’t Escalate | ✅ Proactive Effort |
Stakeholder Engagement | ❌ Generic Output | ✅ Builds Trust |
5 Best Practices for Harmonizing AI and Operational Excellence
- Audit Your Processes Before Automating Don’t automate inefficiency. Conduct a thorough review of your workflows before letting AI loose.
- Pair AI with Human Judgment Use tools like ChatGPT to draft reports or suggest ideas, but let a human refine the outputs. For example, have ChatGPT write a project update, then review it for tone and accuracy.
- Set Clear Boundaries for AI Clearly define what tasks AI will handle versus what needs human oversight. Use DeepSeek for data gathering but rely on your TPM for stakeholder alignment.
- Train Teams to Work with AI Provide basic training on how to use these tools effectively. A real-world example: a team trained to use ChatGPT for sprint retrospectives can save hours of manual prep.
- Use Metrics to Measure Effectiveness Implement KPIs to track how AI is improving (or not) your processes. For example, measure ‘time saved per task’ to quantify efficiency gains or track ‘accuracy of automated reports’ to ensure output quality. For instance, measure the reduction in scheduling time or the accuracy of AI-generated reports.
Real-Life Example: AI in Action
Scenario: Your engineering team struggles to meet deadlines because of unclear requirements, ambiguous priorities, and poor stakeholder communication. Requirements are often incomplete or arrive last-minute, leading to frequent rework and missed timelines. Stakeholders rarely align on expectations, creating confusion and friction within the team.
Solution:
- Use DeepSeek/ChatGPT to automatically gather and categorize requirement changes from email threads.
- Use DeepSeek/ChatGPT to draft initial project briefs, which the TPM refines.
- Let the TPM drive execution sessions and manage escalations.
Result? A 30% reduction in missed deadlines and fewer passive-aggressive emails from stakeholders.
What’s your take? Are you using AI to fix what’s broken or to amplify what’s already working? Feel free to reach out if you want to find out more 💡