Build vs Buy: The Hybrid AI Strategy Reshaping CX

Written By Faraaz Khan

Since 2023, AI has reshaped the CX market faster than anything the industry has seen since the adoption of SaaS and the composability wave. Customer expectations are sharper, release cycles are tighter, and many companies that built their foundations before the current AI wave are finding themselves at the same crossroads:

Do we build AI capabilities internally or buy them?

In practice, neither path works well on its own. The companies progressing the fastest are using a hybrid strategy that blends internal capability with targeted M&A. For many pre-AI CX and SaaS platforms, this hybrid model is becoming one of the few reliable ways to escape 3–5x SaaS multiples and move closer to AI-driven valuation ranges.

Why building is harder than it sounds

Most operators underestimate the organizational momentum that is required to build meaningful AI. It's not just about hiring smart engineers. Teams that are succeeding tend to share a similar checklist:

  • a clean, unified data foundation

  • a product culture that ships quickly

  • leadership that moves with conviction

  • funding to resource AI ahead of revenue

  • the ability to recruit and retain scarce AI talent

The constraints are real. Across early and growth stage companies, demand for ML and AI engineers continues to outstrip supply, and multiple founder surveys from a16z and Air Street Capital show the same thing: AI engineering is one of the hardest functions to scale. Carta's latest compensation data showed AI and ML engineering salaries rose 5.4% to 9.1% between January 2024 and June 2025, with the fastest increases at younger companies. Even companies with strong engineering teams hit bottlenecks with data readiness, governance, and prioritization.

One of the clearest counterexamples has been Intercom. They aligned around AI early, had a unified data model, resourced the shift properly, and had the brand strength to attract talent. They moved quickly enough to build real capability in-house. It was an impressive demonstration of strength of conviction and speed of execution. Most businesses don’t have that combination in place.

Why buying isn’t the magic shortcut

Acquisitions can collapse time-to-market, especially when the bottleneck is talent.

The momentum in AI M&A continues to accelerate. Q1 2025 saw AI and ML startups raise a record $73.6 billion across 1,603 deals. This was the highest quarterly total on record by deal value. Globally, AI and ML startup investment increased more than 50% in 2024 to $131.5 billion, accounting for 35.7% of all VC global deal value. In Q1 2025 alone, 57.9% of global VC dollars went to AI and ML startups, with 70.2% of North American deal value flowing to AI/ML companies. Much of this activity is concentrated in smaller, specialized acquisitions under $100M that compress development cycles and focus on acquiring niche teams rather than products or revenue. That tells you where value is flowing: speed and skill, not revenue.

But buying only works when the underlying platform can absorb new capability. For CX vendors, investing in a unified data foundation significantly reduces the cost, risk, and complexity associated with integrating new AI features or teams. Companies with robust internal AI architectures are better positioned to integrate acquired teams and technologies quickly, enabling them to realize value more rapidly after M&A, while organizations with fragmented data face significantly higher risks of integration delays or project failures. That's when teams end up shipping disconnected features that don't add up to a defensible platform.

At Insider, our acquisition of MindBehind played exactly that role. It brought a team with deep conversational AI experience. And because we already had a unified data layer and an internal AI foundation, we were able to integrate their work tightly and fold it into the broader roadmap.

Buying is not a shortcut. It’s an accelerator. And accelerators only work when the engine is already running.

Why a hybrid strategy is becoming the default

For companies that were built pre-AI, two things are becoming clear:

  1. Building alone is too slow.

  2. Buying alone doesn't create differentiation.

A hybrid strategy is emerging because, if done correctly, it blends the strengths of both paths. It positions pre-AI SaaS platforms to re-rate into categories where AI-driven peers command meaningfully higher valuation multiples.

1. Build what’s closest to your moat

Your moat usually lives at the intersection of:

  • your core data model

  • your workflow engine

  • your customer insights

Anything in this zone compounds over time and should be built rather than bought. This is the part of the product that competitors can't simply copy. Even when a feature looks similar on the surface, the underlying data, workflows, and infrastructure are where defensibility lives.

Twilio Segment's 2024 report shows unified data platforms outperform fragmented ones across personalization and customer engagement metrics.

2. Buy for acceleration and specialized talent

This is where M&A plays a real role. The surge in AI investment, particularly in smaller transactions, highlights a noticeable increase in deals aimed at acquiring specialized teams.

The high-impact areas tend to be:

  • conversational AI teams

  • workflow-specific agents

  • vertical AI capabilities

  • model ops and evaluation expertise

These types of acquisitions accelerate 18–24 months of build time. Think of it as compressing the clock, not outsourcing the roadmap.

3. Integrate into one coherent platform

Most of the fragmentation in CX comes from weak integration. ChiefMartec's 2025 marketing technology landscape now includes 15,384 solutions, up 9% from the previous year's 14,106, where only a small minority reach true scale. Fragmentation is the rule, not the exception.

Customers don’t care if something was built or bought. They care whether it:

  • works across their workflows

  • uses their data in a unified way

  • drives outcomes without extra work

This is where internal AI and acquired talent reinforce one another. When the integrations are tight, the whole platform feels more intelligent, not more complex.

A simple framework for build vs buy decisions

Here’s the lens that seems to work across most SaaS teams:

Step 1: Map the impact on your moat

High impact on your core data model or workflow engine → Build
Low impact on the core engine → Buy or Partner

Step 2: Map the time sensitivity

Need it inside 12 months? → Buy
Need it in 12–24 months? → Either
Need it beyond 24 months? → Build

In simplest terms: Build the core. Buy the accelerators.

What seems likely in the next 12–24 months

Across operators, founders, and investors, a few patterns are showing up.

1. The strongest brands will widen the gap

Companies that already had strong execution muscles, like Intercom, are pulling further ahead.

2. Talent-heavy M&A will increase

PitchBook already shows the trend accelerating. These are “team buys,” not revenue or product plays.

3. Pricing power will shift toward AI-native platforms

Gartner notes that AI ROI drops sharply when the underlying data is fragmented.

4. Data architecture will become the constraint

Teams with fragmented data will struggle no matter how good the UI looks. AI ROI is increasingly determined by the quality of the underlying data infrastructure, not model choice.

5. CX suites will reorganize around agents

Not chatbots or templates, but workflow-specific and vertical agents embedded across the lifecycle. The teams that build the right data architecture now will benefit most from this shift. The teams that invest early in clean data architecture and workflow orchestration will benefit most from this shift.

Next: the investor perspective

I’m putting together a follow-up piece that looks at this shift from the investor lens:

  • where enterprise value will actually accrue

  • what separates durable platforms from feature companies

  • how M&A fits into value creation over the next five years

  • and how hybrid AI strategy becomes a lever to re-rate pre-AI SaaS assets

If you’re building, buying, or operating in this space, I’d love to compare notes.

Please reach out at faraaz@inorganicedge.com or on my LinkedIn.

Sources

AI Talent, Hiring & Compensation

AI Adoption & Market Acceleration

AI M&A Trends

Data Fragmentation & Personalization

AI Productivity & Development Cycle Time

AI Integration & Organizational Readiness

Faraaz Khan
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