How to Keep Software Delivery Human-Centered When Adopting AI

Introduction

Imagine you go to a rescue center hoping to find a loyal, understanding dog—a companion that will be there for the ups and downs. Instead, the staff insists you take home a cheetah because it's faster. That's the trap many organizations fall into when they adopt artificial intelligence with a single-minded focus on speed. In software delivery, speed has never been the true goal. The real objective is to get feedback earlier, so you can stop bad ideas quickly and pivot to better ones. This guide will help you avoid the cheetah trap and use AI to enhance your team's ability to deliver what users truly value.

How to Keep Software Delivery Human-Centered When Adopting AI — Source: thenewstack.io

What You Need

Clear understanding of your current software delivery process (value stream map)
Stakeholder alignment on prioritizing user value over raw throughput
Basic knowledge of AI/ML tools that can assist in testing, analysis, and automation
Commitment to iterative learning – measure outcomes, not outputs
A feedback loop mechanism (surveys, usage analytics, user interviews)

Step 1: Define the Real Goal – Faster Feedback, Not Faster Delivery

Before integrating any AI tool, clarify why you want to increase speed. The answer should not be “to ship more features faster.” The primary reason to accelerate throughput is to get feedback sooner. When you discover a feature doesn’t excite users, you can stop working on it immediately. This saves wasted effort and allows you to pivot to better ideas. Use the following checklist to align your team:

Write down the specific feedback you want to gather sooner (e.g., user adoption, satisfaction, behavior changes).
Map your current workflow and identify where delays in feedback occur.
Set a goal to reduce feedback cycle time, not just development velocity.

Step 2: Measure What Matters (Value), Not Just Velocity

Many teams fall into the trap of measuring lines of code, story points completed, or deployment frequency. These metrics can be misleading. Emulate the discipline of outcome-based measurement. For example:

Track feature adoption rates and user retention.
Use A/B testing to compare new features against alternatives.
Monitor support ticket reductions as a sign of true value.

Remember the story of Microsoft Word vs. Google Docs: Microsoft Word had 3.9% market share in 2023 vs. Google Docs’ 9.6% (source: 6sense). Word had more features, but users chose Docs for its ease of collaboration. Speed of adding features didn’t matter; value did. Let that be your north star.

Step 3: Evaluate AI Tools for Feedback-Related Activities

Not all AI is bad – it’s about where you apply it. Instead of using AI to generate code faster, use it to amplify your feedback loop. Good examples:

Automated testing: AI can generate test cases that catch regressions earlier, reducing time to feedback on quality.
User sentiment analysis: Use NLP to parse support tickets or social media for early warning signs.
Predictive analytics: Identify which features are likely to flop before release.

When evaluating a vendor’s claim that AI will “speed up delivery,” ask: “How does this tool help us get feedback faster?” If they can’t answer, walk away.

Step 4: Integrate AI Incrementally with Guardrails

Resist the temptation to overhaul your pipeline overnight. Start with a single workflow where feedback is slow or manual. For instance:

Pilot an AI code reviewer on a non-critical service.
Use an AI assistant to summarize user interviews.
Implement a chatbot to triage support requests.

Set up guardrails: define acceptable error rates, require human oversight for critical decisions, and measure whether the AI actually reduces feedback time. If it doesn’t, stop using it.

Step 5: Iterate Based on Real User Feedback – Not AI Outputs

AI may suggest features or code optimizations, but the ultimate judge is the user. Continue to run small experiments and listen to your users. Consider the lesson from the Agile movement: it started with values like collaboration and responding to change, but many organizations reduced it to “go faster.” Don’t let AI suffer the same fate. Create a ritual:

Every sprint, review one piece of user feedback that surprised you.
Adjust your backlog based on that evidence.
If an AI recommendation contradicts user feedback, trust the user.

Tips for Avoiding the Cheetah Trap

Don't confuse speed with progress. A cheetah can run fast but can’t provide companionship. AI can accelerate irrelevant work.
Watch out for “speed theater.” When a software leader announces AI adoption solely for speed, check their track record. Have they delivered value before? If not, skepticism is warranted.
Remember that fewer features often win. Google Docs beat Word not by being faster, but by being simpler and better at collaboration. Prioritize fewer, high-value features over many mediocre ones.
Keep the human in the loop. The most valuable feedback comes from real conversations with users, not from metrics alone.
Celebrate stopping a bad idea early. This is a win, not a failure. Make it visible in your team’s retrospectives.

By following these steps, you can adopt AI in software delivery while keeping your focus on what truly matters: delivering value through meaningful feedback loops. Speed will follow naturally – but it will never be the goal.