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.

The Business Case for Generative AI Investment

When it comes to investing in Generative AI, the fundamental business case mirrors any strategic investment—it ultimately falls into two core categories:

Revenue Growth – Driving business expansion through new or enhanced revenue streams.
Cost Optimization – Enhancing operational efficiency to reduce expenses.

Despite the overwhelming hype surrounding AI, these remain the true metrics of success. If there were an "AI Hype Index" similar to the VIX (which measures stock market volatility), 2024 would have seen record levels, with 2025 likely following suit. While social media influencers and industry voices continue to amplify expectations, businesses must evaluate AI investments through their specific lens—ensuring decisions are made strategically, rather than out of FOMO (fear of missing out).

Key AI Investment Questions for Business Leaders

For CEOs, board members, private equity and venture capital partners, and executive leaders, critical questions demand attention:

What is your AI investment strategy today, and how will it evolve over the next 3-5 years?
How are you leveraging Generative AI to enhance products, educate customers, and optimize go-to-market strategies for revenue growth?
How is Generative AI being integrated into your internal operations—from sales and marketing to product development, engineering, customer support, and general administration?

Emerging AI Adoption & Operational Trends

Several key trends are shaping how organizations approach Generative AI:

Understanding AI’s Value Proposition
Over the past 18 months, enterprises have primarily focused on defining where AI fits within their business model. Many organizations—especially those without deep AI or data technology expertise—are starting with smaller, ROI-driven investments to validate impact.
Measuring ROI on AI Investments (2025-2026)
The next two years will be a defining period, as early investors and adopters of Generative AI begin assessing measurable returns. The broader ecosystem—including Limited Partners (LPs), General Partners (GPs), and operating leadership in many industries —will shift toward establishing concrete ROI metrics to track progress and justify further investment.

What’s Next?

In upcoming posts, we’ll take a deeper dive into these topics, recognizing that AI strategies, use cases, and priorities differ across organizations and leadership roles.

As you navigate AI adoption, we bring real-world expertise in developing and executing AI strategies. Let’s explore how we can support your organization in making AI a true business enabler.

Learn More

Business, FinanceBlackPepper LabsJanuary 18, 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

The Business Case for Generative AI Investment

Key AI Investment Questions for Business Leaders

Emerging AI Adoption & Operational Trends

What’s Next?

Learn More

Learn More