AI is reshaping how businesses operate, but making the shift isn’t as simple as flipping a switch. While AI promises efficiency and innovation, companies need a structured approach to integrating it into their workflows without risking revenue streams.

This blog explores a two-step approach to AI migration that ensures your ability to sell and service products remains intact. You’ll discover:

• The four types of workflows in an AI-driven world.
• Three business model profiles that impact AI adoption.
• How to migrate workflows strategically while minimizing disruption.

Understanding these factors will help you confidently navigate AI adoption, leveraging automation where it makes sense and protecting your bottom line. Let’s dive into the AI Two-Step.

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The AI Math Behind Tech Business Models

For many technology companies, the AI conversation in boardrooms boils down to a simple equation: How can we leverage AI to reduce the cost of building, selling, and servicing our products?

The logic seems straightforward: reduce labor costs enough to offset AI investment, ideally saving more than you spend. But in reality, AI-driven cost savings aren’t so cut and dry. Companies must grapple with some key questions, such as:

• Which workflows can (and should) shift from knowledge workers to AI?
• How quickly can these workflows be transitioned?
• What will the actual cost of AI adoption be? (Spoiler: Technologies like DeepSeek have already disrupted initial cost assumptions.)
• How do you migrate without disrupting sales and service in the short term?

Despite these complexities, many leadership teams are slashing budgets, assuming AI will allow them to “do more with less.” This is a high-risk strategy. AI adoption isn’t instant, and without a structured approach, businesses could jeopardize their ability to sell and deliver products.

Related: The AI Adoption Paradox

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The AI Two-Step: A Smarter Approach to AI Migration

To avoid these pitfalls, TSIA recommends a methodical AI Two-Step approach:

• Step one: Identify and begin migrating a set of workflows to AI.
• Step two: Confirm your ability to sell and service products.
• Step one: Identify the next wave of workflows to migrate.
• Step two: Reconfirm your ability to sell and service products.

What’s next? Repeat. This iterative approach ensures you’re not mindlessly cutting costs, but strategically adopting AI while safeguarding revenue streams.

So, where does this process ultimately lead? Let’s explore what happens when companies follow the AI Two-Step to its final destination.

The Great Workflow Migration: How AI Is Reshaping Workflows

The first step in the AI Two-Step is identifying which workflows can realistically migrate to AI. Historically, enterprise technology companies have operated in silos, with each department relying on its software systems. This often resulted in clunky handoffs, data bottlenecks, and inefficiencies.

AI is changing that. By breaking down data silos, AI enables knowledge workers across departments to leverage the same data, insights, and predictive models. This shift is one reason legacy software providers are on high alert—because AI isn’t just streamlining workflows; it’s redefining them.

Related: Deploying AI in Revenue Generation Workflows

The Four Types of AI-Enabled Workflows

In an AI-driven world, workflows will evolve into four distinct categories.

Knowledge Workers With Software (Traditional Approach)

The familiar model is where employees input data, generate reports, and use software to support their work. Some tasks are automated, but knowledge workers still drive the workflow.

Knowledge Workers Augmented by AI

AI enhances efficiency by automating parts of the workflow, reducing manual effort.

Examples:

• AI-assisted content creation (marketing materials, RFP responses).
• Automated competitive analysis.
• AI-driven pricing recommendations and revenue forecasting.
• AI assists in analytics-driven decision-making, where multiple decision-makers typically review large volumes of data.

Digital Agents Taking Over Customer Interactions

Instead of interacting with humans, customers engage with AI-powered digital agents that go beyond traditional chatbots.

What makes them different?

• Autonomous decision-making: AI processes data and acts without human input.
• Natural language processing (NLP): AI understands and responds conversationally.
• Contextual awareness: AI adapts to user needs and learns from interactions.
• Complex task management: AI can support financial advising, data analysis, and multi-department sales operations.

Fully Automated Workflows

AI anticipates customer needs and executes tasks without human or digital agent intervention.

Examples: 

• AI detects that a customer would benefit from a new product feature, proactively recommends training, and monitors adoption progress—all without human intervention.
• AI Agents assist a customer in diagnosing and resolving a problem.
• AI agents converse with sales prospects to understand their business challenges and desired outcomes to prescribe the right product or service offer.

The goal for every tech company is to migrate as many workflows as possible from human-led to AI-driven—without jeopardizing revenue or the ability to sell and service customers effectively.

Understanding AI Migration Profiles: Where Does Your Company Fit?

Not every workflow can—or should—be fully automated by AI, and not every company will migrate the same number of workflows. That’s why defining AI migration profiles is helpful in understanding where your organization stands today, and where it’s headed.

At a high level, TSIA has identified three distinct business model profiles based on AI adoption.

AI Light (Less than 20% AI adoption)

• AI enhances workflows but doesn’t replace human decision-making.
• Most workflows involve knowledge workers augmented by AI.
• AI is primarily used for efficiency—think AI-assisted writing, competitive analysis, or customer insights.

AI Augmented (More than 50% AI adoption)

• AI plays a significant role in business operations.
• More than 50% of business decisions rely on analytics and AI models.
• AI helps optimize pricing, offer configuration, lead scoring, and customer health assessments.

AI Automated (More than 80% AI adoption)

• The majority of workflows are fully automated.
• AI not only assists but executes tasks, reducing human involvement across operations.
• Sophisticated AI models primarily handle decision-making.

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It’s not just about how many workflows AI influences—other key variables define AI-intensive companies, such as:

• Labor vs. technology investment: AI-automated companies rely far less on human labor and significantly more on AI-driven processes.
• Software system consolidation: As AI automates workflows, companies reduce the number of disparate systems supporting business processes.
• AI-driven decision-making: In AI-intensive companies, models influence everything—from feature prioritization, to pricing strategy and customer engagement.

As AI capabilities continue to evolve, these profiles will become more pronounced. However, even companies with the potential to achieve full AI automation must approach migration carefully—as moving too fast can risk revenue and customer experience.

That’s why the AI Two-Step is critical. Companies can unlock AI’s potential without jeopardizing business stability following this measured approach.

Step 2 of the AI Two-Step: Protecting Revenue While Migrating to AI

AI offers incredible potential, but over-rotating too quickly introduces real risks—especially when protecting revenue.

Relying too much on AI without a thoughtful strategy can backfire in several ways:

• Loss of human connection: Over-automation in customer interactions can weaken trust, especially in complex B2B sales where consultative engagement is key.
• AI biases and inaccuracies: Poorly trained AI can generate incorrect recommendations, damaging customer confidence.
• Frustrating high-stakes customer interactions: Enterprise customers expect human expertise in renewal discussions, escalations, and troubleshooting—AI alone won’t always cut it.
• Employee alienation and attrition: Over-reliance on AI without clear workforce integration can demotivate employees and lead to talent loss.

The AI Budget Cut Trap

Some companies rush into AI by slashing operational budgets upfront—often by reducing headcount—before knowing where and how AI will be leveraged.

This reactionary approach creates a nightmare scenario where:

• Leadership assumes AI will instantly create efficiency.
• Departments are forced to cut staff before AI is even implemented.
• The ability to sell and service customers is jeopardized.

Even ChatGPT agrees this is the worst possible way to roll out AI. A hasty, top-down AI rollout without a clear strategy or governance leads to:

• No clear business goals: AI is adopted without specific use cases, resulting in disconnected, low-impact initiatives.
• Siloed implementation: AI is rolled out without input from Sales, Customer Success, IT, Security, or Legal, creating confusion and inefficiencies.
• Poor data quality: AI models trained on biased, outdated, or incomplete data generate inaccurate recommendations and flawed automation.
• Over-reliance on AI: Automated AI-driven sales and support replace human interactions without a fallback plan, frustrating customers.
• Lack of employee training: Employees are expected to adapt to AI tools without guidance, leading to resistance and misuse.
• Security and compliance risks: AI tools are deployed without data privacy and security controls, exposing the company to legal issues.
• Failure to track performance: AI’s impact isn’t measured, leading to wasted resources on ineffective implementations.

The Smarter Way: Deploying AI in Waves

Rather than slashing budgets and forcing AI adoption prematurely, businesses should deploy AI capabilities in structured waves:

1. Identify proven, high-impact AI use cases.
2. Deploy those use cases with oversight.
3. Assess efficiencies gained and measure impact.
4. Verify that sales and service operations remain strong.
5. Adjust budgets based on actual AI-driven efficiencies.

Companies should proceed to the next wave only after completing a wave. This iterative approach ensures that AI enhances business operations rather than disrupting them.

The ultimate goal? To evolve into an AI-augmented or AI-automated business—but at a pace that protects revenue, maintains customer trust, and secures long-term success.

Related: AI’s High Stakes: The Future of Technology Business Models

TSIA’s Role in Guiding AI Migration

Successfully adopting AI isn’t about making a single leap—it’s about navigating the journey strategically, and about smart decision-making. That’s where TSIA comes in. Our research team is dedicated to helping technology companies migrate workflows to AI, while safeguarding revenue and operational stability based on data, evidence, and experience. Here’s how TSIA is supporting businesses through the AI Two-Step.

Identifying Mature AI Use Cases

We separate AI hype from reality by studying real-world implementations. Our research focuses on AI’s impact across:

• Customer Success
• Support Services
• Professional Services
• Education Services
• Managed Services
• Field Services
• Offer Management
• Revenue Management

Best Practices for Deploying AI

Through case studies and industry insights, we identify what works and what doesn’t when implementing AI capabilities. Learning from successful deployments helps companies avoid costly missteps.

AI Capabilities Heatmap

We track where and how fast AI capabilities are advancing within tech organizations. This helps companies prioritize the right AI investments at the right time.

AI’s Impact on Tech Company Operating Models

AI will fundamentally reshape how technology companies operate—altering organizational structures, financial models, and best practices. TSIA continuously analyzes these shifts to help businesses adapt before their existing models become obsolete.

Why the AI Two-Step Is Critical

AI is not just another trend—it’s a fundamental shift in how technology businesses operate. Ignoring AI is a critical mistake, but rushing into it without a structured approach is even more dangerous.

That’s why the AI Two-Step process is essential. By following this iterative approach, companies can harness AI’s potential while maintaining the trust of customers, employees, and stakeholders. AI is the future, but how you get there makes all the difference.

Your Key Takeaways

• AI adoption must be strategic, not reactionary: AI is transforming how businesses operate, but rushing to implement it without a clear migration plan can put revenue at risk. Companies must resist the urge to cut headcount prematurely and adopt an iterative, structured approach—ensuring AI enhances operations without disrupting sales and service.
• Not all AI workflows are the same—or equally adaptable: Tech companies fall into three AI migration profiles: AI Light, AI Augmented, and AI Automated. The number and type of workflows that can be transitioned vary, and companies need to balance AI-driven automation with human oversight to maintain efficiency and trust.‍
• The AI two-step ensures stability while scaling AI: A hasty AI rollout can lead to poor data governance, disconnected implementations, and customer dissatisfaction. The AI Two-Step—migrating workflows in waves and continually validating sales and service effectiveness—helps businesses scale AI without jeopardizing revenue or customer experience.