Step 2: Design the right partnership model (build vs. buy)

Finding the balance between control, scalability, and speed.

Why this decision is strategic, not just technical

For technical leaders, the build-vs-buy question is really a decision about focus and risk.

Can you innovate fast while maintaining reliability? Can your internal teams own the overhead of ongoing maintenance, governance, and integration complexity?

AI infrastructure is moving too quickly and the operational burden is too high for most organizations to build everything themselves. The smartest enterprises focus internal resources on differentiated innovation, and partner on capabilities that are critical but non-core.

The question isn’t “Can we build it?” but “Does building this ourselves give us a competitive advantage?”

A real-world example: Love’s Travel Stops chose a build-together partnership model instead of constructing and maintaining their own AI infrastructure. The result was a 98% decrease in speed to answer, dropping from 9 minutes to 9 seconds, along with an 85% reduction in abandonment and $1.7M in cost savings, outcomes that would have required years of internal engineering investment to replicate.

Start with what is core to your business

A simple guiding question should anchor your decision: “If we build this ourselves, does it materially differentiate our business?”

If the answer is no, ownership becomes a distraction.

A product-led company might innovate in personalization or recommendations. A fintech company might innovate in underwriting models. A retail brand might innovate in shopper experience and merchandising science.

But if  the organizations want to gain a competitive edge by engineering on their own, they need to make sure they have the resources to build, maintain, implement, troubleshoot and keep improving constantly. Besides, they need to create a redundant, secure, compliant and reliable architecture:

• carrier redundancy
• telephony failover
• LLM orchestration layers
• observability pipelines
• prompt governance workflows
  

Those are infrastructure, not identity.

Your teams should be building the systems that move your business forward, not rebuilding commoditized plumbing simply because it’s technically possible.

The true cost of building AI automation in-house

Building AI internally may offer a sense of control, but it introduces significant long-term maintenance responsibilities, operational overhead, and hidden costs that most enterprises underestimate. Beyond the technical lift, the greatest cost is the ongoing distraction of engineering resources from the work that actually moves the business forward.

Below, we break down the four major areas where DIY AI becomes a permanent drag on teams and systems.

Operational burden

DIY means you own every layer of the AI lifecycle  and each layer requires ongoing engineering investment.

• Continuous model retraining: Your team must update, tune, and validate models as data shifts, escalating technical debt over time.
• Extensive regression testing: Every model change requires full workflow testing to ensure nothing breaks downstream.
• Break/fix incident management: When something fails in production, AI becomes another mission-critical system your engineers must monitor and repair 24/7.
• Monitoring and alerting infrastructure: You’ll need dedicated observability tools and dashboards to track latency, accuracy, uptime, and drift.
• Multi-vendor failover logic: Ensuring reliability requires redundancy across ASR, LLM, TTS, and telephony providers — all of which your team must orchestrate and maintain.
• Compliance and audit preparation: Every model iteration must meet regulatory, privacy, and audit requirements, which adds ongoing overhead your team must sustain.
  

DIY doesn’t just mean building AI, it means permanently staffing and maintaining an AI reliability organization.

Integration complexity

Modern contact centers depend on deeply interconnected systems. DIY AI introduces significant integration surface area and risk.

• Legacy telephony complexity: Integrating directly into carrier networks, SIP trunks, and CCaaS platforms requires deep telephony expertise many teams no longer have.
• CRM + ticketing interoperability: Ensuring AI agents write back cleanly into CRM, case, and ticket systems requires custom logic and brittle middleware that must be maintained forever.
• Multi-system authentication: Your team must handle identity, SSO, permissions, secure tokens, and privacy controls across multiple back-end systems.
• Data flow stability: Every API response, timeout, schema change, or system upgrade introduces risk of breaking AI flows in production.
• Cross-channel consistency: Maintaining consistent logic, tone, and compliance across voice, chat, and SMS requires duplicate builds unless you engineer a unified layer internally.
  

Building AI means building every integration, every connector, and every safeguard, then owning the risk of every break.

Velocity loss

Engineering teams often underestimate how much DIY AI slows innovation.

• Roadmap disruption: AI infrastructure work pulls engineers away from revenue-driving product initiatives.
• Upgrade overhead: Every model, vendor, dependency, or infrastructure upgrade introduces new rounds of testing and refactoring.
• Operational drag: As the AI footprint grows, so do code paths, monitoring dashboards, and internal review cycles. All of which dilute engineering focus.
• Specialist hiring: Maintaining AI internally requires MLOps, ASR/LLM tuning, observability engineering, and telephony ops expertise. Roles that are costly and difficult to hire for.
  

What begins as a quick internal build becomes a permanent tax on engineering capacity, diluting focus on the initiatives that actually differentiate your business.

Risk exposure

When you build AI yourself, your team becomes the reliability, security, and compliance organization.

• Uptime responsibility: If the AI system goes down, the contact center goes down and IT is fully accountable.
• Security ownership: Your team must embed zero-trust principles, PII redaction, encryption, RBAC, and secure logging across all components.
• Compliance risk: Every conversation must adhere to regulatory frameworks, and your team becomes responsible for maintaining deterministic guardrails.
• Incident response burden: If hallucinations occur, if a workflow fails silently, or if a misrouting event impacts customers, internal engineering must absorb and resolve the issue.
• Model drift: Without continuous validation pipelines, accuracy decays silently creating risk of customer-impacting errors or compliance violations.
  

DIY turns your technical team into the SRE, SecOps, MLOps, and governance arm for AI. Whether they intended to or not.

The hidden cost of DIY isn’t the build, it’s the endless maintenance, oversight, and engineering attention that shifts focus away from the core innovations that actually move your business forward.

Why the build-together model wins

A build-together partner model solves the challenges of DIY while preserving control where it matters.

Your teams retain ownership of:

• data governance
• system integrations
• security approval
• business logic and policy

Your partner accelerates:

• deployment
• optimization
• reliability and redundancy
• observability
• versioning and rollback discipline
• post-launch performance improvements

This approach allows your engineers to focus on core innovation, not infrastructure upkeep.

It’s not outsourcing, it’s multiplying your internal capabilities.

Evaluate partners across five technical dimensions

When assessing potential partners, use a criteria framework that aligns directly to IT, Data, and engineering priorities:

Ensure speed to value and minimal engineering disruption

A strong partner should offer fast time to value. This means measurable results within weeks, not quarters with minimal disruption to your roadmap.

Ask:

• How quickly can we reach production?
• What is required from our engineering team?
• How do you ensure no disruption to our existing systems?
• How will we measure results in the first 90 days?

Velocity matters  but not at the expense of reliability, compliance, or governance.

Validate partnership fit through collaboration

Before committing, run a short discovery sprint or technical workshop.

You’ll quickly see:

• how well they understand your architecture
• how they handle complexity
• whether they can move at enterprise speed
• how they collaborate under ambiguity

You learn more from five days of co-design than from 50 pages of proposals.

In AI, partnership is not an afterthought, it is infrastructure. Choose a partner who strengthens your stack, not one who becomes part of the problem.

Let's move on to Step 3 - Assess risk, security, and integrations.