[By Prasad Prabhakaran]
The change is already here
Software teams everywhere are using AI now.
The 2025 DORA Report shows that 89% of companies are adding AI into their development work, and three out of four developers already use it every day.
At esynergy, we have seen this change up close.
We help banks, insurers, and public services bring Generative AI (GenAI), AI agents, and Code LLMs safely into their Software Development Life Cycle (SDLC).
The results are real – faster testing, better code, and quicker releases.
But there are lessons to learn.
The DORA findings match what we have seen. AI helps speed up development, but if it is not used carefully, it can make systems less stable and teams more stressed.
Let us look at what we learned.
- AI makes coding faster, but not always delivery
When AI tools write or check code, they help developers move fast.
DORA found that a 25 percent increase in AI use can make code quality 3.4% better and documentation 7.5% better.
But the same study found that delivery speed and stability can drop by 7%.
Why? Because AI helps teams write more code, but they often release it in bigger chunks. Bigger releases break more often.
Takeaway:
AI helps speed up work, but the best results come when teams still follow strong DevOps habits – small commits, good testing, and fast feedback.
- AI removes boring work, not meaningful work
DORA found something interesting.
AI helps developers finish important work faster, but it does not reduce the boring tasks like meetings or paperwork.
At esynergy, we saw the same thing.
For example, a bank used GenAI to create test data and automate regression tests. The time for testing went down by half, but developers still had to decide what really mattered next.
Takeaway:
AI is great for removing repetitive work. But humans still need to decide what makes the work valuable and creative.
- Trust makes all the difference
Developers who trust AI tools use them more and get better results.
But many do not yet trust them fully.
We built trust by setting clear rules on:
- What kind of data AI can use
- When AI-generated code must be reviewed
- How we check for security and privacy
When our clients did this, AI adoption increased a lot.
DORA saw the same trend. Companies with clear AI policies had four times more adoption than those without.
Takeaway:
Clear rules and open communication make AI safer and more trusted. Trust is what turns experiments into success.
- Learning time is not wasted time
DORA found that when companies give developers dedicated time to learn AI, adoption goes up by 131%.
If learning is forced or added as extra work, people lose interest.
In our projects, we gave teams space to explore AI tools every week and share what they learned. This made them confident and creative.
Takeaway:
Give people time to play, learn, and fail safely. That is how they become experts.
- The value of a developer is changing
AI changes what developers do, but not their importance.
DORA says there are five ways people find value in their work – usefulness, recognition, pay, pride, and fun.
AI affects each of these. It helps people do more, but it can also make them wonder who gets the credit.
We tell our clients to celebrate outcomes, not hours spent. Developers now guide and teach AI, not just type code.
Takeaway:
Reward people for results, ideas, and teamwork, not just speed. The best developers are AI guides, not robots.
- Measure, learn, and improve
You cannot manage what you do not measure.
DORA recommends using the same four metrics that define good DevOps:
- Time to release a change
- How often you release
- How many releases fail
- How fast you recover
In our client projects, when we tracked these before and after AI, we saw real improvements; 35% faster releases, 25% better code, and 50% less time on testing.
Takeaway:
Collect data, review progress, and keep improving. AI success grows from feedback and measurement.
- The biggest lesson: AI and DevOps belong together
The DORA report reminds us that AI will not fix a weak process.
It makes good teams better and poor practices worse.
High-performing teams keep three habits alive:
- Fast flow — ideas move smoothly from code to release
- Fast feedback — mistakes are caught early and fixed fast
- Learning culture — people share, experiment, and grow
AI fits best when these habits already exist.
Takeaway:
AI is not a replacement for people or process. It is a tool that multiplies what you already have — good or bad.
Simple summary
| Area | What AI changes | What good teams do |
| Productivity | Slight increase | Use AI for coding, testing, docs |
| Code Quality | Gets better | Always review AI suggestions |
| Delivery | Can drop if unmanaged | Keep releases small and tested |
| Documentation | Improves | Let AI draft, humans edit |
| Adoption | Rises with clear rules | Publish policies everyone understands |
| Learning | Works best with time | Let teams explore without pressure |
| Developer Happiness | Improves with trust | Support choice and transparency |
Final thoughts
AI in software development is not about replacing people.
It is about helping them focus on what really matters.
Teams that mix AI with strong engineering habits will go further than those chasing speed alone.
AI works best when humans stay in control and culture stays healthy.
At esynergy, we help regulated industries use AI safely inside their SDLC – with clear rules, trusted tools, and measurable results.
Want to see how AI can improve your software delivery?
Book a free discovery session and learn how to make AI work for your teams, not against them.
