The AI Shift: The Next Great Business Transformation

First, there was the internet—it redefined commerce. Then came SaaS—it reshaped business models. Now, AI is fundamentally shifting how businesses operate.

A Lesson from History: Why Some Companies Win While Others Fade

Consider how technology evolved:

• On-Premise Era: Businesses bolted cloud capabilities onto outdated systems, accumulating tech debt and eventually getting disrupted.
• Cloud/SaaS Era: Businesses that re-architected for the cloud thrived, while those that merely “added” cloud features struggled.
• The Blockbuster vs. Netflix Mindset: Blockbuster tried to extend its existing model (DVD rentals + mail). Netflix fundamentally reimagined the business around streaming.

AI is the next inflection point. The winners won’t be those who simply “add AI” to what they already do—they’ll be the ones who rethink their models from the ground up.

How AI Will Reshape SaaS and Traditional Businesses

If You’re a SaaS Company:

SaaS as we know it is dead. AI-native startups are already redefining the game. Simply adding AI as a feature won’t cut it—your platform needs to be reimagined.

For example, traditional CRM platforms rely on users manually inputting customer data. An AI-first CRM will analyze multiple data sources, predict customer needs, and act autonomously—without requiring human input.

Side Note: Tanvir’s Prediction on The Future of SaaS & AI-Driven Agents

Imagine this from a customer’s perspective: They can analyze multiple sources of content across their network, scaling numerous AI agents to test hundreds of iterations of a marketing campaign or social media post. These agents are trained on models tailored to their specific customer and vertical, factoring in competitor activity, past successes, and what’s currently working. The result? Rapid scenario analysis at a fraction of the time, enabling decision-making that truly matters.

Now, instead of manually analyzing data, the customer focuses solely on selecting the best output from the options presented by AI agents. The CRM is no longer just a system—it’s an ally, a strategic partner. SaaS businesses that embrace this shift will thrive, while others will be disrupted.

What This Means at a Technical Level:

Here’s my prediction: UI, UX, and traditional interfaces will become obsolete. As AI agents consolidate, navigate, and make decisions across multiple tools and tech stacks, the need for a surface layer disappears. This will fundamentally change how tech companies build products, structure teams, and approach hiring.

Apps and systems will be reduced to APIs. A tech company’s success will depend on the strength of its APIs, the robustness of its ecosystem, and how well it supports consumer-facing applications. Their core value will lie in how efficiently their agents operate in the background.

For tech leaders, the key question becomes: How do we embed ourselves in the right ecosystems, make our AI agents the best in the industry, and ensure seamless integration with customer-facing apps?

This shift demands a new business model—moving from a product-focused approach to an agent-first strategy. The most successful SaaS companies will prioritize:

Best-in-class APIs to power AI-driven automation.

Deep integrations & partnerships across ecosystems.

Pricing based on AI agents & outputs instead of traditional user licenses or recurring payments.

The future belongs to those who adapt. The question is: Will you lead the shift or be left behind?

If You’re a Traditional Business:

AI is your biggest opportunity—and your biggest risk. This is the digital disruption of the 2020s.

For example, a restaurant chain today drives revenue through apps, delivery, and drive-thru. In an AI-first world, your ranking on AI-driven platforms and seamless AI-powered ordering will dictate your success. The consumer doesn’t care which system is used—they just want their food delivered on time, with zero friction.

What this means for you:

AI will redefine how customers find and interact with you—miss the shift, and someone else will capture your market.

AI-driven automation will reshape cost structures, allowing competitors to operate at higher margins and scale faster.

The time to act is now—before AI-native competitors redefine your industry.

So, What Does This Really Mean?

With AI – there are two paths, in terms of simplifying it for this purpose:

1- Incremental improvement (path of disruption)

AI is viewed as a tool for improvement – These businesses, whether tech companies, consumer brands, service businesses, see AI as a tool that can help them drive more revenue, become more efficient, and drive better customer satisfaction.

• Example: Adding an AI agent chat bot to reduce headcount in the customer support team, or implementing an AI-first loyalty program to drive higher repeat business, but keeping your marketing/digital team structure as is.

2- Redefining the business model from the ground up (path of opportunistic growth)

AI is viewed as an inflection point – These same businesses see AI as an opportunity that requires them to re-frame their entire business model and how they do business, preparing themselves to capitalize on what’s to come next.

• Example: A SaaS business with the ability to scale with no incremental human headcount. Or an AI as a service platform where a large retailer buys pre-trained loyalty agents, redefining their entire marketing strategy and cost structure.

Which path are you on? More importantly—which path are your competitors on?

Where to start?  – The AI Opportunity equation.

AI Opportunity =  (Business Model & Revenue Impact + Operational Efficiency Gains) × Execution Readiness

Equation Definition:

• AI opportunity is the incremental revenue or efficiencies by leveraging AI.
• Business Model/Revenue Impact: This is the business’s value chain – how it generates value.

» Key Question: How can AI create new revenue streams, increase customer lifetime value, or drive competitive advantage?

➡ Examples include Incremental revenue from existing business growth, user growth or incremental usage, new business or verticals like AI-driven product features, new data monetization strategies, or AI-first business models.

• Operational Efficiency Gains: This is the internal business operations – how the business is run to generate the intended value and achieve the vision.

» Key Question: How can AI reduce costs, automate processes, or improve decision-making speed/accuracy?

➡ Examples include higher profitability through process improvement or efficiencies, such as AI for workflow automation, predictive maintenance, or hyper-personalization in marketing. It also includes how to scale a team with minimal bottlenecks with Agentic AI, for example

• Execution Capability: This is key—it measures how well an organization can actually execute on AI initiatives.

» Key Question: Does the company have the leadership, talent, infrastructure, and speed to execute?

➡ Example: A company with a strong AI strategy but weak execution will struggle to see real impact.

Let’s break this down further:

Note: This equation will look different for each business, given there are no similarities between businesses, given vertical, market share, internal resources, timing, etc.

1. Business model/revenue opportunity:

• Look at your current market, current customers, and key competitors
• How can AI help delight customer experiences better than those of our competitors (example: voice-enabled ordering for drive-thrus)?

» This is a good place to brainstorm potential revenue streams, how will today’s pricing model shift, where is the consumer going, and how are your competitors reacting?

» What opportunities exist beyond the surface?

➡ Example: New AI-powered product lines, AI-driven personalization that increases sales conversions, or AI-based pricing and revenue optimization.

➡ Example: For a B2B restaurant tech company, building an AI layer that learns, analyses, and predicts business outcomes, makes decisions in real time, while breaking down silos across platforms.

» How can you leverage current assets, customer base, data, competitive positioning to offer a new AI-first business (example agents as a service)?

2. Operational Efficiencies:

• Look at your business today. How are you structured? What are your Opex and Capex like? What is your return on capital and return on headcount?

» This is an opportunity to reimagine your organizational structure, and if there are current bottlenecks, and how you can reduce silos, increase speed, while maximizing customer value.

➡ Example:  AI-powered workflow automation with minimal bottlenecks, AI-driven demand forecasting, cost savings through AI-first supply chain optimization.

» What does this unblock for you 10 years from now?

» Where will you reinvest any savings – will it be short term (buy back stock), or long term (reinvest to build an AI first business).

3. Execution Readiness:

• Even the best AI strategy fails without execution. Assess your organizational readiness. Does your organization have the talent, infrastructure, and leadership buy-in to act?

» Do you have a leadership team aligned on AI priorities?

» Do you have the data and tech stack to support AI adoption?

» Does your team have the skills to implement AI successfully?