Artificial intelligence has quickly moved from innovation labs into boardroom discussions. 

According to McKinsey's The State of AI, more than 70% of organizations report using AI in at least one business function, nearly doubling adoption in just a few years. Yet despite this rapid growth, many executives still struggle with one of the most important strategic decisions they will face:

Should we build our own AI, buy an existing solution, or partner with an experienced technology provider?

Every organization wants to improve operational efficiency, strengthen compliance, reduce costs, and position itself for future growth. But selecting the wrong AI strategy can create years of unnecessary complexity, increased expense, and slower innovation.

The conversation should no longer focus solely on whether artificial intelligence is worth the investment. The more important question is how organizations should adopt AI in a way that creates measurable business value while supporting long-term competitiveness.

Recent executive discussions on artificial intelligence have highlighted an important shift in thinking. AI is no longer viewed simply as a technology purchase; it is increasingly becoming a strategic business capability that influences scalability, operational performance, and even future M&A opportunities. 

That shift requires leaders to think differently about technology investment decisions.

The Build vs. Buy Debate Misses the Bigger Picture

Viewing AI adoption through only two choices often leads executives to compare software features instead of evaluating broader business outcomes. Technology decisions should begin with organizational strategy rather than technical capability.

For example, a regional collection agency with 200 employees has very different needs than a national creditor or a debt buyer managing millions of accounts. Likewise, a collection law firm may prioritize compliance workflows while a fintech organization focuses on consumer engagement and predictive analytics.

The right decision depends less on the technology itself and more on the organization's objectives, resources, internal expertise, and competitive strategy.

Instead of asking "Can we build AI?", executives should ask:

  • What competitive advantage are we trying to create?
  • How quickly do we need measurable ROI?
  • What resources are required to maintain this solution over time?
  • Will this improve operational performance or simply add technical complexity?

These questions form the foundation of a more strategic approach.

The AI Adoption Decision Matrix

Rather than thinking in terms of build vs. buy, organizations should evaluate three distinct pathways.

Build

Building an AI solution makes the most sense when technology itself becomes a competitive differentiator.

Organizations pursuing this strategy typically possess mature engineering teams, proprietary datasets, long investment horizons, and the financial resources necessary to maintain complex AI infrastructure.

Potential advantages include:

  • Complete control over product development
  • Custom workflows tailored to business operations
  • Ownership of intellectual property
  • Greater differentiation from competitors

However, building introduces significant challenges.

Development timelines often extend well beyond initial expectations. AI models require continuous retraining, security updates, governance oversight, and regulatory monitoring. The investment rarely ends after implementation.

For most organizations, maintaining AI becomes a permanent operational commitment rather than a one-time project.

Buy

Commercial AI platforms allow organizations to deploy capabilities much faster.

Many AI vendors have already invested heavily in compliance, infrastructure, integrations, and product development. Rather than reinventing foundational capabilities, agencies can focus on improving operations.

Benefits typically include:

  • Faster implementation
  • Lower upfront investment
  • Vendor-supported updates
  • Reduced technical risk
  • Proven deployment models

Buying AI works particularly well when organizations need to solve common operational challenges such as call summarization, workflow automation, predictive analytics, quality assurance, or AI copilots.

The tradeoff, however, is flexibility.

Commercial platforms may not perfectly align with every workflow, and organizations often become dependent on vendor roadmaps for future innovation.

Partner

The third option receives far less attention but may ultimately become the most valuable.

Strategic partnerships combine the speed of commercial platforms with collaborative innovation.

Rather than building everything internally or accepting a standard product, organizations work alongside technology providers to shape future capabilities while focusing internal resources on business execution.

This model offers several advantages:

  • Faster innovation cycles
  • Shared implementation expertise
  • Lower development costs
  • Reduced operational risk
  • Greater flexibility than traditional software purchases

In many ways, partnerships allow organizations to remain focused on their core competencies while still influencing technology direction.

For financial services organizations where operational execution determines profitability, this approach often creates stronger long-term value than attempting to become a software development company.

Matching Strategy to Business Objectives

One of the most common mistakes organizations make is selecting an AI strategy before clearly defining the business problem they are trying to solve.

AI should never become the objective. Business outcomes should.

Consider several common priorities across the receivables management industry.

Organizations focused on improving collector productivity may benefit from AI copilots that surface account information, summarize previous conversations, and recommend next-best actions.

Debt buyers looking to improve portfolio performance may prioritize predictive analytics and cost-to-collect optimization. Collection law firms may focus on document automation, workflow orchestration, and compliance monitoring.

Each objective leads toward a different technology decision.

Selecting the appropriate adoption model requires leaders to evaluate operational priorities before evaluating software capabilities. The technology should always support the strategy: not define it.

Operational Efficiency vs. Cost Saving: Measuring the Right Outcomes

Operational efficiency and cost savings are not the same thing.

Cost savings measure what an organization spends less. Operational efficiency measures what an organization accomplishes more effectively.

For example, an AI copilot that helps collectors prepare for conversations, summarizes previous interactions, and recommends next-best actions may not reduce headcount. Instead, it allows agents to spend more time engaging with consumers and less time searching for information or completing administrative work.

That improvement can increase collector productivity, improve customer experience, shorten training cycles, and strengthen client performance metrics. Those gains often produce significantly greater long-term value than labor savings alone.

Leaders should evaluate AI investments against business outcomes such as:

  • Revenue per collector
  • Recovery performance
  • Client retention
  • Consumer experience
  • Compliance consistency
  • Time-to-productivity for new employees
  • Operational scalability

Organizations that measure AI through these broader performance indicators are better positioned to create sustainable competitive advantages.

Preparing the Workforce for AI

Another common misconception is that AI adoption is primarily about workforce replacement.

In practice, the organizations seeing the greatest success are using AI to strengthen and not replace their people.

Collectors continue to provide empathy, negotiation, judgment, and relationship management. AI complements those capabilities by reducing repetitive work, organizing information, and providing real-time decision support.

This people-first approach also changes how organizations think about hiring and professional development. Future employees may spend less time learning administrative processes and more time developing communication skills, critical thinking, and problem-solving capabilities.

As AI becomes more integrated into daily operations, leaders should prepare for continuous workforce evolution rather than one-time technology implementation.

Organizations that invest equally in people, process, and technology are likely to achieve stronger long-term results than those focused solely on automation.

Governance Must Grow Alongside Innovation

Every AI initiative should include a governance strategy.

Financial services organizations operate within highly regulated environments where documentation, transparency, privacy, and oversight remain essential. As AI capabilities expand, executive teams must ensure governance evolves at the same pace.

Questions worth asking include:

  • How are AI-generated recommendations reviewed?
  • What data supports model decisions?
  • Who remains accountable for final decisions?
  • How are vendors evaluated?
  • What controls exist for ongoing monitoring?

Strong governance builds trust not only with regulators but also with clients, investors, and employees.

Organizations that proactively establish AI oversight today will likely adapt more effectively as regulatory expectations continue to evolve.

Looking Beyond Technology

Artificial intelligence is changing how financial services organizations compete, but technology itself is only one piece of the equation.

The organizations creating lasting value are those aligning AI with business strategy, operational execution, workforce development, and governance. They recognize that AI adoption is less about purchasing software and more about making disciplined leadership decisions.

For executives navigating this transition, the most important decision may not be choosing between building or buying AI. It may be deciding how technology supports the organization's broader vision for growth.

 

Additional perspectives on AI strategy and innovation can be found on Receivables Info, including resources that explore how artificial intelligence is reshaping the receivables management industry.

Readers interested in the broader discussion that inspired this conversation can also explore episode 3 of the Applying AI podcast with Michael Lamm from Corporate Advisory Solutions.