What’s the ROI timeline?

One to two weeks, with one week being entirely realistic if you prioritize it and I can get time with both management and engineering teams to do this properly. Keep in mind that once developers start working at this new speed, every other part of your pipeline needs to be streamlined to keep up.

The key is having at least one person on the team willing to suspend disbelief and truly try it out, not just treat AI as a fancy autocomplete tool. Once they follow best practices for a few days and get the hang of it, you’ll see results within a week.

For ROI specifics: my consulting fee is minimal, and ongoing monthly costs for coding assistants range from $20 to $200 per developer. While it’s early days for precise percentages, your development team will become more than 10x more efficient. Not just in speed, but in quality and security too. Plus, your developers will be able to work with languages and technologies they couldn’t touch before.

How to ensure code quality?

When you follow best practices, code quality and security actually improve. Part of best practices includes having AI do separate peer reviews for both quality and security. You should also run your standard code security and quality tools alongside AI reviews. Human peer reviews are still fine, but teams tend to need them less over time as they learn to trust the AI’s capabilities.

Which AI tool is best?

You need the best tool, period. Trying to save money on a lesser tool could cost you enormously. The difference between tools might seem small (maybe 10% better performance), but that translates to more trust, fewer mistakes, and better results. Right now, Claude Code is the most proven tool available.

What’s the budget needed?

For full-time developers, budget $100-200 per month. Product managers can usually work fine with the $20 monthly plan.

You’ll need one or more AI champions, either consultants or internal team members. I recommend selecting people whose roles can include being an AI champion. I’ll meet with them weekly at first, and help directly when they hit problems they can’t solve themselves.

The main cost is typically the consultant. I’m affordable and efficient, and you’ll see tangible, significant results within one to two weeks.

Will AI replace developers?

If your goal is just to maintain your current feature delivery rate with a smaller team, you’re thinking about this wrong. You won’t beat your competitors that way. The real power of AI is delivering at 10x speed with your existing team.

Reducing staff means losing institutional knowledge and the ability to spot problems specific to your organization. Keep your team the same size and completely outpace the competition by delivering astronomically faster. Success comes from speed of delivery combined with quality and security, not from cutting corners on people.

How to handle resistant developers?

You don’t need everyone on board immediately. As long as one developer is willing to give it an honest shot and suspend their disbelief, we can demonstrate success quickly. With my guidance on best practices, I can guarantee results. Once that first developer implements a week-long feature in an afternoon (with full quality and security), they become the advocate for the rest of the team.

Yes, the job changes significantly. But adapting to new technology has always been part of working in tech. This shift is bigger than switching from Perl to Python. Your role becomes more about writing specs, ensuring AI did what you wanted, and cleaning up edge cases. Some find it less fun, others more so. Personally, I get more joy from rapidly delivering features and seeing them work than from diving into code details. Your mileage may vary.

Who owns AI-generated code?

Simple: only use coding tools where the terms of service confirm you own the results and the tools are SOC compliant. I’ve implemented SOC compliance systems and run many SOC audits, so I know what to look for.

[Include table showing main companies’ SOC compliance status and terms of service regarding ownership]

How to measure success?

If you’re using Jira, each ticket should already have complexity sizing (story points or similar). Track how many tickets each team member closes per week and month.

I’ve done something some consider controversial: making ticket closure data publicly visible within the team. Data is good. Organizations can decide how visible they want it to be. Yes, this data can be misused (for instance, some developers spend time helping others rather than closing tickets themselves), but sharing data isn’t inherently problematic.

How to maintain quality?

Code quality should increase, not decrease, when following best practices. AI reviews code for quality. A separate AI agent reviews for security. Documentation stays current. You still want human oversight and code reviews of AI-generated code. Over time, teams learn the right level of trust for AI coding.

How to handle large projects?

Traditional Agile development works fine with AI coding, but every part of the pipeline needs to follow best practices. Surprisingly, the product manager might become the bottleneck. You need to constantly watch for bottlenecks, tracking them mathematically while using common sense.

If you’re using manual processes like shared charts that aren’t auto-generated or parsed automatically, those need to change. Any process requiring humans to collate information needs automation, and AI can help fix these processes quickly.

Using Jira with automated roadmaps and Gantt charts is good. The more manual your processes, the more problems you’ll face.