AI as Force Multiplier: How AI Helped Our Dev Team 3X Results

Post by Elad Nachmias, BridgeWise CTO

Around the world, companies of all categories are asking themselves how AI is going to impact their business. Whether that’s due to competitors leveraging the technology or due to AI disrupting legacy industries, we are currently living through one of the most dynamic periods of business disruption and innovation.

At BridgeWise, our position as a rapidly growing startup company means one of the primary challenges we face is the issue of scale. This is always a challenge for growing companies, but as an AI-native company, it is even more pointed. AI is one of the most active areas of development; with fierce competition for customers, employees, resources, and more. In my role as CTO, I could have chosen the well-trodden path and hired more engineers to keep up with the demands of growth and scale, but instead we went a different way.

We determined that our best solution to thrive in the AI space is AI itself. For us, AI solutions allowed our team to accelerate development, driving rapid scale, and ultimately multiplied the effectiveness of our engineers and developers by a factor of three. We saw our team of 40 engineers achieve what in the past would have taken a team of 120.

Here’s a review of our experience in building an AI enhanced operation.

AI by the Numbers

Here are some of the key numbers that factor into our analysis. At BridgeWise today, we currently manage:

• Approximately 40 engineers around the world
• Nine products in production
• Over 100 global customers
• Millions of end investor users across our partners and customers

And yet, with our current engineering team and challenging current workload, we estimate that we have been able to deliver the results of a team of approximately 80-120 engineers.

Once the decision was made to make AI a core part of our development process, we conducted an in-depth review of our development teams to understand how they were already leveraging AI. We asked our team leaders to detail the real use cases they have found for AI solutions, estimate the time saved from these AI implementations, and provide measurable KPIs that have been achieved.

Through this process we were able to validate what the industry has been hoping for: AI is not a simple tool, it’s a force multiplier.

Real World Use Cases of AI-Augmented-Development

Let’s examine some of the real-world use cases we found for AI assisted development at BridgeWise.

DevOps and Infrastructure

Key use cases: 

• AI-generated Terraform, Kubernetes, Jenkins pipelines
• Automated Python scripting
• AI-powered log analysis and debugging

Measured Impact: We saw our debugging time decrease dramatically from approximately 40 minutes to 10, a 75% reduction; log investigation times fell from hours to just 10-15 minutes, and we were able to achieve a massive reduction in boilerplate code.

Estimated AI multiplier: 1.5x per engineer

Developer Team

Key use cases:

• AI-assisted TDD, tests + code generation
• Automated PR reviews with prioritized feedback
• API, scripts, and endpoint generation
• Data engineering pipeline cost and time optimization”
• Feature estimation and requirement analysis
• Full SDLC automation: Jira → Code → Tests → PR
• Technical design auto-generation
• Context-aware coding with MCPs (Serena, Context7)
• LLM-based financial text generation (replacing old rules engine base texts generator)
• Automated translation
• Dataset validation & anomaly detection
• Complex analytics scripting
• Code generation

Measured Impact: Our PR review time decreased by 50% and our feature delivery speed increased significantly by at least 60%; we saw up to 80-90% of our code written with AI assistance; delivery cycles increased by 60%; and many more increases and optimizations.

Estimated AI multiplier: 2x – 2.5x per developer

Quality Engineering

Key use cases: 

• LLM as judge for financial text validation
• Automated test plan and test case generation
• Migration to structured test repositories (Xray)
• Backfilling thousands of test cases automatically

Measured Impact: Test creation decreased from eight to three days, validation cycles fell from hours to around 20 minutes, and our documentation efforts saw a 90% decrease in time requirements.

Estimated AI multiplier: 1.8x – 2x per engineer

Data Scientist and Frontend Teams

Key use cases:

• Synthetic training and testing data generation
• Prompt engineering and optimization
• AI-based code understanding and review
• Figma → UI component generation

Measured Impact: Dataset creation was accelerated by 75-80%; dev tasks decreased from 3-4 hours to 30 minutes; UI development saw multiple hours saved per developer.

Estimated AI multiplier: 2x per engineer

Architects

Key use cases:

• Codebase analysis and pattern detection
• Architecture validation and documentation
• Technical research acceleration

Measured Impact: We identified a 3x productivity boost

Key Lessons from AI-Augmented-Development

The most important lesson that we learned is that AI does not “replace” developers or engineers; thinking about it in that way is a critical mistake that misses the true power that AI solutions can enable.

Rather, for all of the use cases we identified, having a human in-the-loop is mandatory. Judgement and focus are powerful drivers where humans excel, but they can still take advantage of the acceleration and breadth that AI can provide.

This is what it looks like:

• AI generates → Engineers validate
• AI suggests → Engineers decide
• AI accelerates → Engineers own

The bottom line, the power we discovered is not in automation, but in augmentation. Humans leveraging AI are able to achieve far better results than either working on their own or trying to replace them with fully automated solutions.

AI-Augmented-Development – The Key Mindset Shift

Far more important than the individual tools we leveraged is the mindset shift we had to execute. It required embracing AI tools for much of the legwork of our development and allowing our team to focus on the vision and validation of our solutions.

As I look to the future of AI assisted development, the industry needs to let go of the old mindset that more engineers translates to more output. Now, we need to focus on the quality and skill of our engineers and how we can multiply their output with the right AI tools.

This mirrors what we are seeing in other industries, and specifically in the finance space that BridgeWise is operating in. Human advisors are learning how to integrate AI to broaden the scope and scale of the insights they can provide. AI solutions allow them to cover far more investment vehicles and access real-time analysis that they can provide to their clients alongside the unique perspectives and personal understanding they bring to the table.

While we are still in the early stages of the AI transformation, the path we are on has become far more clear. It is one in which humans will leverage AI solutions to multiply the effectiveness and efficiency of the efforts, rather than be replaced by AI entirely.