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.

Adopting Generative AI in Customer Facing Products

If you are a Go-to-Market professional (e.g., Product Manager/Marketer), chances are you’ve been asked a simple yet critical question over the past 18 months by various stakeholders including customers: “What is your AI roadmap, How is your AI strategy compares with competition, What is your plan to educate customers or answer their questions on AI roadmap and how are you leveraging Generative AI in your product suite?”

It’s a fair question, given the immense promise of Generative AI in recent times. The depth and range of questions depends on which industry vertical you are operating. However, we have observed many Product Managers struggle to provide a clear answer. Here’s why:

Lack of AI Training: Crafting an AI roadmap requires a certain level of technical expertise, and many Product Managers haven’t been trained in AI layers.
Lack of AI Expertise in Technology teams: Just there aren’t enough technology experts to partner with to build AI features in their products.
Lack of various functions within organizations: In many market verticals outside deep tech, People are still figuring out how to sell, market. develop and support AI products and features.
Complexity of Third-Party Solutions: Navigating the array of third-party tech solutions and aligning them with a build-vs-integrate strategy demands strategic thinking, which adds another layer of difficulty.

Despite the widespread adoption of Generative AI, the fundamental principles of product management remain unchanged when it comes to delivering value to your customers and other stakeholders.Customers invest in products that that are intuitive, help to drive revenue growth, enhance marketing efforts, save time on repetitive tasks, optimize operations, and strengthen security, compliance, and other critical business functions. Meanwhile, investors, executives, and boards stay focused on driving revenue growth, boosting profit margins, and reducing operational expenses.As a Product Manager, your core responsibilities still include enhancing product usability, driving adoption, and accelerating go-to-market timelines. While these fundamentals stay the same, the rise of AI has introduced new questions from customers, leadership, and boards that require clear, strategic answers.

Here's how you can confidently engage in AI-related discussions while staying rooted in core product management principles—focusing on customer needs, monitoring the competitive landscape, and considering other key factors.

Gain a Deep Understanding of the AI Ecosystem including Generative AI
View AI as a multi-layered ecosystem consisting of technology products(data layer to foundation models to LLMs/Services), AI service providers, and other key players relevant to your industry. While consuming AI-related content on platforms like LinkedIn, critically assess the motivations behind each player—whether investors, bloggers, or companies promoting their services.
Collaborate with AI Experts
Partner with professionals in data engineering, data science, machine learning, and AI. Their expertise can help you distinguish between hype and reality, ensuring your product roadmap is both realistic and forward-thinking. It also help making build vs partner decisions helping you meet your GTM goals and timelines.
Balance Short-Term and Long-Term AI Strategies
Develop an AI strategy that includes both short-term, iterative goals (such as quarterly milestones) and long-term initiatives that can be confidently communicated to customers and stakeholders. Given AI's rapid evolution, regularly showcase progress and reinforce thought leadership through your roadmap.
Prioritize Quick Wins
Leverage widely adopted AI solutions to demonstrate early success, aligning with customer expectations. As with any product decision, apply a build-vs-buy strategy to determine the most effective path forward.
Embrace the AI-Driven Future
AI adoption isn’t optional—it’s a necessity. Strengthen your understanding of enterprise data and explore opportunities to monetize it through AI-powered features to stay competitive in this new era. Depending on industry vertical, you may have to create ML teams and roadmaps to develop core AI services yourself. That helps you answer questions around if you are AI-Native vs AI-Enabled along with addressing, further AI strategy questions including compliance, security and IP protection around data.
Establish key AI success metrics relevant to your context to demonstrate progress in your AI roadmap investments. This should include tracking customer and stakeholder education alongside core product performance metrics.
Finally, Keep up and adopt the User experience expected by the customers. Market is educating them quickly on how to use AI products(starting AI Assistants on their mobile and product they use on daily basis at work!).

By applying these strategies, you can confidently address the AI roadmap question and position your products to thrive in the new AI era.

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.

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Engineering, Product, User ExperienceBlackPepper LabsJanuary 27, 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

Adopting Generative AI in Customer Facing Products

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