AI-First vs. AI-Enhanced: Which Path a Business Should Take
Companies looking at baking AI into their products need to make a critical decision on whether to rebuild around AI or use AI to augment existing capabilities and structures.
In the early 2000s, Blockbuster was untouchable. It had thousands of stores, millions of loyal customers, and a brand synonymous with home entertainment. Going to Blockbuster was something that many millions of people did every weekend. Few would ever have imagined that a small DVD-by-mail service could bankrupt Blockbuster. Netflix burst onto the scene and quickly became an existential threat to the incumbent. But behind the scenes, this challenger was quietly laying the groundwork for a new kind of entertainment. Netflix wasn’t just focused on DVDs; it was building something bigger—a fully digital, AI-enhanced streaming platform that would eventually make physical DVDs irrelevant.
Even as Netflix became a serious competitor, Blockbuster’s response was cautious and incremental. It introduced a DVD-by-mail service to compete with Netflix, but it kept its store network, resulting in a confusing online experience that lagged behind Netflix’s streamlined model. By the time Blockbuster began experimenting with streaming, it was too late. The store-driven model that had once made Blockbuster untouchable was built for a different era and simply couldn’t adapt. Netflix, on the other hand, had reimagined itself for a digital future, while Blockbuster was layering technology on top of old foundations. Today, Netflix is a household name, and Blockbuster is a distant memory.
Today, companies face a tipping point with AI: deciding whether to adapt incrementally or make AI a foundational part of their strategy.
This story foretells a future challenge now facing many companies. Today, businesses face a similar crossroads with artificial intelligence. As AI technologies grow more powerful, the pressure mounts to integrate AI into products and services. CEOs and boards are aware of the promise and peril of AI. Since the launch of ChatGPT in 2022, I’ve received countless requests from investors, executives, and technology leaders to review their plans to transform their business into an “AI business.” This has led me to a critical observation.
As I review the architecture of emerging AI-powered applications, I see an essential distinction between two approaches to AI: “AI-First” and “AI-Enhanced.” This isn’t just a technical decision—it’s a strategic one that could shape a company’s future. Understanding this distinction could have given Blockbuster a fighting chance. Not understanding this distinction means a company has a diminished likelihood of surviving and thriving in the future where AI competency is essential for success.
AI-First: Reinventing from the Ground Up
Imagine building a digital platform with AI as its core, not just as an enhancement — which is what Netflix did when it envisioned its streaming service. That’s the AI-first approach: designing products where AI is essential to every interaction. Think of ChatGPT or Perplexity—these tools would not exist without AI. Their value and user experience depend entirely on advanced algorithms, continuous learning, and adaptability.
AI-First companies build with AI as a foundation, almost like an operating system. This approach demands iteration not only on product performance but also on AI quality. To realize the capabilities unlocked by AI-First architectures, companies must invest in infrastructure, data, and expertise, fundamentally changing the way products work and how users interact with them. For those that succeed, AI-First becomes a competitive advantage, setting a new standard in the market.
AI-Enhanced: Adding Intelligence to the Familiar
Then there are companies that take an AI-Enhanced approach, like adding a digital layer to an analog process. GitHub Copilot is a great example. AI improves developer experience by providing suggestions and automating repetitive tasks. But AI isn’t the core of GitHub; it’s an upgrade, an enhancement. If the AI were removed tomorrow, GitHub would continue to serve its users and would likely remain the preferred choice for version control for some years to come.
AI-Enhanced products can integrate AI without a complete overhaul, which makes them more practical for companies with legacy systems. But there’s a trade-off: these companies might eventually struggle to keep up with AI-First competitors who’ve designed their products with AI woven into every part of the experience.
Where Companies Go Wrong with AI
Both AI-First and AI-Enhanced come with challenges. Sometimes, an AI-Enhanced model is the practical choice. For legacy businesses with established systems and processes, starting with AI-Enhanced can be a strategic, lower-risk path. However, the success of this approach hinges on whether the organization has a clear plan to evolve as technology advances. AI-Enhanced can provide immediate gains, but companies must actively keep pace, adapting and refining their systems to stay competitive—much like Netflix did by transitioning from DVD-by-mail to digital streaming and beyond.
The risk for AI-Enhanced companies lies in viewing it as the final destination rather than a step in a longer journey. Without a plan to evolve, they may find that their retrofitted systems accumulate technical debt, create integration headaches, or even reach a point where they can’t compete with the seamless, adaptable experiences offered by AI-First companies. AI-Enhanced companies should also expect challengers to quickly emerge with similar capabilities. GitHub Copilot faces challenges from multiple coding assistants in many different interfaces — the integrated development environment, the version control system, and even the browser (where developers can cut and paste code into web interfaces for AI-powered coding assistants). Ultimately, companies choosing the AI-Enhanced path need a clear vision for the future — one that evolves with technology and lays the groundwork for AI-First capabilities when the time is right.
How to Choose Your AI Strategy: An Assessment Framework
To help leaders assess which approach best aligns with their goals, here’s a framework to guide the decision-making process:
Core Value Proposition
Is AI fundamental to your product’s value?
Could your product function and still deliver value without AI?
Technical Requirements
Does your application demand real-time AI processing?
Is your data infrastructure mature enough for an AI-First strategy?
Resource Considerations
What level of AI expertise is within reach?
How substantial is your budget for AI implementation?
Do you have the computing resources to sustain this vision?
Risk Assessment Matrix
To illustrate the trade-offs between AI-First and AI-Enhanced, consider the following factors:
Common Implementation Pitfalls
Even with the best intentions, both paths have their risks. Here are some of the most common:
AI-First Challenges:
Infrastructure Gaps: High demands on data, compute, and storage.
Data Quality/Quantity: AI depends on vast and reliable data sources.
Dual Iteration Requirement: Balancing product-market fit with refining AI quality can stretch resources and delay time to market.
Expertise Shortages: Advanced AI requires more than generic data science; it needs deep, AI-specific expertise.
High Development Costs: Making AI-First work often requires a significant upfront investment in specialized infrastructure and talent.
AI-Enhanced Challenges:
Integration with Legacy Systems: Aligning new AI capabilities with existing architectures can add complexity.
Performance Bottlenecks: Without optimized integration, AI-Enhanced models can create friction or latency.
Limited AI Capabilities: Many enhancements don’t fully leverage AI’s transformative power.
Technical Debt: Integrating AI without sufficient planning can add long-term technical debt.
Navigating the Transition
Legacy organizations face a timeless challenge: adapting to the latest paradigm shift. With AI, the stakes are higher because AI-First products and experiences are often structurally different from their predecessors. While an AI-Enhanced model may seem like a reasonable starting point, companies with long-term ambitions need to consider if their systems can support an AI-First future when the time comes.
A gradual evolution from AI-Enhanced to AI-First is possible but requires foresight and investment in data, infrastructure, and processes. For companies that need real-time personalization or constant adaptation to user preferences, investing in AI-First architecture today may avoid a costly overhaul down the road and position them to adapt smoothly and efficiently as technology evolves.
The Bottom Line
The choice between AI-First and AI-Enhanced is especially significant for legacy businesses, where entrenched systems and workflows can make a shift to AI-First feel like navigating an iceberg. For these organizations, an AI-Enhanced approach offers a practical start while planning for gradual architectural changes. However, taking that first step means acknowledging that the expectations for AI will only continue to increase.
Strategic Considerations for Your AI Path
As you choose between AI-Enhanced and AI-First, address these core considerations to align your approach with your company’s capabilities and vision:
Define Your AI Vision: Clarify where AI adds the most value to your product and ensure it aligns with your long-term objectives. Will AI drive the user experience, or will it serve as a supporting feature? This vision will shape priorities, investment, and focus.
Assess the Shift Costs: If starting with an AI-Enhanced approach, realistically assess the costs and challenges of potentially shifting to AI-First later. Legacy systems, technical debt, and integration complexity are factors that can impact your ability to adapt if AI becomes more foundational down the line.
Evaluate Your Data Strategy: AI needs data—both quality and quantity. Determine whether your current data infrastructure can support either path and if investment in data resources or infrastructure will be necessary.
Consider Your Iteration Cycle: For AI-First, success depends on iterating to refine both product-market fit and AI quality. Assess your organization’s ability to manage these parallel iteration cycles and the resources they’ll require.
Plan for Scalability and Flexibility: Whether AI-Enhanced or AI-First, design with scalability in mind. Ensure your AI infrastructure and strategy allow for flexibility as new opportunities or technologies emerge.
The Path Forward
In a world where AI-driven experiences are fast becoming the norm, the distinction between AI-First and AI-Enhanced has never been more crucial. Understanding where AI best fits into your strategy can mean the difference between a product that evolves with the market and one that encounters costly, insurmountable roadblocks.
As your company faces this AI crossroads, consider not only which path to take but how each aligns with your long-term vision. The question remains: will your business adapt and thrive as technology evolves, or risk being left behind?
Andrew Tahvildary is on the leadership team at Techquity.ai. He is the primary author on this post. Andrew is a CTO who has led 7 tech startups to successful exits, exceeding $2 billion in total transaction value.