AI Evolution blog explores a wide range of topics tailored for operational leaders and innovators, including:

AI Market Analysis: Periodic reviews of market trends and the evolving AI landscape.
AI Research Insights: Relevant topics around traditional machine learning, models, LLMs, GenAI, agents, algorithms, deep learning, neural networks, agentic software and much more.
AI Product Innovation and Strategy: Relevant topics around product development, economics, and go-to-market (GTM) strategies.
AI Software Engineering Lifecycle: Best practices for developing, testing, and managing AI-enabled and AI-native products.
Adopting AI Across Enterprise: Strategies to boost operational efficiency through AI adoption.
And much more, offering relevant insights to keep you ahead in the AI-driven world.

Building Bootstrapped Software Products in the Age of AI

Thirty years ago, I graduated with a degree in computer science. Last fall, I spoke with a college senior—also studying computer science—just beginning their journey. It reminded me how much has changed since those early days: from the internet boom to mobile, to cloud, and now AI.My own career moved from writing code to leading teams and shaping product portfolios. I even spent four years as a founder, learning the hard lessons of building from scratch. Later, I had the chance to lead data science and ML teams, which gave me a front-row seat to the promise—and the limits—of earlier AI adoption. Over time, my focus shifted toward leadership, strategy, architecture and engineering execution. But that conversation last fall, combined with the rise of generative AI, sparked further curiosity that put me on the builder path again.This wave feels unlike the others. AI tools aren’t just exciting—they’re accessible, practical, and production-ready. Suddenly, the climb back into hands-on building felt lighter. I didn’t just want to use the tools—I wanted to understand the models, the architecture, and the systems behind them. That reignited an old dream: trying another startup, at 50+.

This post isn't about our startup —we'll discuss that later. It's about : how modern AI tooling is changing the way we approach end-end software product engineering, especially as founders where speed to market and exploring multiple launch paths are crucial. I'm not suggesting that traditional roles are disappearing or that AI will solve everything, even in established companies. Far from it—computer science fundamentals, domain expertise, ability to build/operate complex scalable systems, creativity, and human judgment are more important than ever. This is simply a founder's perspective on how AI toolings can assist in product engineering, getting to MVP and iterate quickly.

How AI tools helped us build faster as a bootstrap founder

Here’s how AI tools played a role at different stages of the journey:

1. AI Tooling for Product Management

Translating product ideas into JIRA epics, user stories, and tasks took hours/days—not weeks. AI obviously didn’t replace product judgment, but it sped up the grunt work.

2. AI Tooling for Design

AI-generated wireframes and UI concepts gave us strong starting points. Expert designers still needed to refine the UX, but we skipped the blank canvas phase.

3. AI Tooling for Coding

Among other things, a very short while ago, building polished React(or other JS based) front end app and integrating them with backend used to feel cumbersome and expensive. Also lot of time was spent putting things to gather and debugging broken JS/CSS/API/Basic Infra etc. Now, AI tools offer solid scaffolding fast—though debugging and optimization remain essential and shifting focus to building complex production grade systems, building better business logic, models and user experiences making customers happy.

4. AI Tooling for Testing & Reviews

AI-assisted test generation and code reviews helped us catch issues early. Human oversight ensured quality and relevance.

5. AI Tooling for Cloud & Monitoring

Deployment scripts, infra templates, and basic monitoring setups came together quickly with AI help—freeing time for deeper architecture work.

6. AI Tooling for Security

Early tools scanned code for vulnerabilities—even HIPAA-relevant ones. They’re not a replacement for formal audits yet, but they’re useful early indicators.

Along with these uses, we also realized you can do market research and create initial GTM strategies.

7. AI for Market Research

LLM-powered tools helped us research competitors, trends, and whitespace opportunities with surprising speed and depth.

8. AI for Startup Marketing

From creating landing pages to drafting early campaigns, GPT-based tools gave us a fast, cost-effective starting point. Enabling us to allocate more capital, time towards customer acquisition campaigns and maximizing ROI.

9. And More

Every month, new AI tooling capabilities open new possibilities. The pace of change is very encouraging helping minimize grunt work.

Full Circle

For those of us who grew up learning the fundamentals of computer science, this is another crucial moment to adapt. AI isn’t replacing fundamentals or technical depth (or product expertise)—it’s built on computer science fundamentals helping accelerating us like advancements before this, reducing grunt work, reducing friction, and freeing us to focus on what truly matters: innovation and customers. It’s inspiring to see a new wave of tools emerging, enabling the creation of even better products.

AI Research, Engineering, Business, Go-to-Market, Product, TechnologyBlackPepper LabsAugust 13, 2025

One of the early use cases of Generative AI has been around, how to create, manage and operate Agents for supporting existing customers. As you might have noticed, Salesforce has been promoting AI agents in their platforms, so do other providers like Servicenow, Zendesk. Naturally it is the extension of their business model to protect existing business from over AI optimization, while making sure a their version of AI narrative is promoted. However there are plenty of AI-First agent products have been coming to market in 2024. We have seen good adoption of these agents in enterprise context.

As these technologies mature, a crucial conversation has emerged around leveraging AI for end-to-end customer support and success. This includes new customer onboarding to providing product support(including tech support in most cases).

So, if you are a Chief Customer Officer or Leader in this organization, naturally you have to be able to create Ai strategy to adopt agentic software capabilities. In most organizations, these capabilities are provided by IT function. However it is crucial to be able to understand and create your own strategy around AI for your department beyond toolings. Following are crucial points that keeps coming up in conversations between Exec Leadership and Chief Customer Success office.

"How can we leverage AI Agents to best support to customers to make them successful?"

"What's the fastest path to optimize existing customer support systems and data?"

"How can we increase key KPIs such as NPS and CSAT while optimizing customer success/suport investments?"

"How do we maximize our existing talent's efficiency?"

"Which current Gen AI/Agent tools can deliver immediate productivity gains?"

While these questions are critical, the answers vary significantly based on organizational role and AI expertise. This has created three distinct stakeholder groups:

1. Investors, particularly in PE and VC spaces, seeking efficiency gains in customer success and support budgets beyond offshoring.

2. Organizational leaders driving acceleration and digital transformation initiatives need to show ROI backed by key metrics including efficiency gain metrics

3. Product Support, Success and onboarding leaders tasked with adoption, and implementation

For those in the third category, the responsibility is significant. Success requires crafting both short and long-term strategies through careful discovery, stakeholder education, and adaptive road mapping - all while navigating this rapidly evolving technological landscape collaborating with technology function.

The challenge isn't just about adopting new tools - it's about understanding their true potential and limitations in your specific customer success, product and operational context. As we move forward, the winners won't be those who simply embrace AI, but those who strategically integrate it into their customer lifecycle managment processes while maintaining focus on business outcomes.

We invite you to learn from our real-world experience in developing and executing AI strategies, and discover how we can support your organization's AI journey.

AI Evolution - Blog