Digital Transformation

Why This Matters to Me

I have been in too many product discussions where accountability was fuzzy. Everyone agreed something mattered, but no one owned it. Work stalled, deadlines slipped, and frustration grew. I have also seen the opposite, projects where one person stepped up, claimed ownership, and pushed it forward.

That is why the Directly Responsible Individual (DRI) matters. It is more than a process borrowed from Apple or GitLab. It is a mindset shift toward empowerment and clarity.

What Is a DRI?

A DRI is the single person accountable for a project, decision, or outcome. They may not do all the work, but they ensure it gets done. Steve Jobs made the practice famous at Apple, where every important task had a DRI so ownership was never in doubt. (handbook.gitlab.com, bitesizelearning.co.uk)

In my experience, this clarity is often the difference between projects that deliver and those that linger.

Strengths and Weaknesses

The DRI model works because it removes ambiguity. With a clear owner, decisions move faster, resources are coordinated, and teams feel empowered. Assigning someone as a DRI is a signal of trust: we believe you can make this happen. (tettra.com)

The risks are real too. A DRI without proper authority can be set up to fail. Too much weight on one individual can stifle collaboration or lead to burnout. And if organizations treat the role as a label without substance, it quickly collapses. (levelshealth.com, dbmteam.com)

Examples in Practice

GitLab: Embeds DRIs across the organization, with clear documentation and real authority. (GitLab Handbook)
Levels Health: Uses DRIs in its remote-first culture, often as volunteers, supported by “buddies” and documentation. (Levels Blog)
Coda: Assigns DRIs or “drivers” for OKRs and pairs them with sponsors for balance. (Coda Blog)

The lesson is clear. DRIs succeed when paired with support and clear scope. They fail when given responsibility without authority.

Rolling Out DRIs

Adopting DRIs is a cultural shift, not just a process tweak. Some organizations roll them out gradually, starting with a few high-visibility initiatives. Others go all in at once. I lean toward gradual adoption. It builds confidence and proves impact before scaling.

Expect the early days to feel uncomfortable. Accountability brings clarity but also pressure. Some thrive, others resist. Over time, the culture shifts and momentum builds.

Change management matters. Leaders must explain why DRIs exist, provide support structures like sponsors, and create psychological safety. If failure leads to punishment, no one will volunteer.

The Clash with Command-and-Control IT

The DRI model often collides with the command-and-control style of traditional enterprise IT. Command-and-control relies on centralized approvals and shared accountability. The DRI approach decentralizes decisions and concentrates accountability.

I believe organizations that cling to command-and-control will fall behind. The only path forward is to create space for DRIs in product teams while still meeting enterprise compliance needs.

How AI Is Shaping DRIs

AI is becoming a force multiplier for DRIs. It can track progress, surface risks, and summarize input, giving individuals more time to focus on outcomes. But accountability cannot be outsourced to an algorithm. AI should make the DRI role easier, not weaker.

Empowerment and Conclusion

At its core, the DRI model is about empowerment. When someone is trusted with ownership, they rise to the challenge. They move faster, make decisions with confidence, and inspire their teams. I have seen people flourish under this model once they are given the chance.

For senior leaders, the next steps are clear. Identify accountability gaps, assign DRIs to a few strategic initiatives, and make those assignments visible. Pair them with sponsors, support them with AI, and commit publicly to backing them.

If you want empowered teams, faster results, and less ambiguity, DRIs are one of the most effective levers available. Those that embrace them will build stronger cultures of ownership. Those that resist will remain stuck in command and control. I know which side I want to be on.

TL;DR: The Buy vs Build

Challenge	Build (DIY Wrapper)	Buy (Enterprise Solution)
Cost	Tens to hundreds of thousands in build plus ongoing maintenance (applifylab.com, softermii.com, medium.com)	Predictable subscription model with updates and support
Security	Vulnerable to prompt injection, data leaks, and evolving threats (en.wikipedia.org, wired.com, wsj.com)	Enterprise-grade safeguards built in such as encryption, RBAC, and monitoring
Reward	Limited differentiation and fragile ROI	Faster time to value, scalable, and secure

Do not fall for the trap of thinking “we are different” or “we can do this better with our framework.” Building these wrapper experiences has become the core product that multi-billion-dollar model makers are selling. If this is an internal solution, think very carefully before taking that path. Unless your wrapper directly connects to a true market differentiator, it is almost always wasted effort. And even then, ask whether it can simply be implemented through a GPT or an MCP tool that already exists in commercial alternatives like Microsoft Copilot, Google Gemini, or ChatGPT Enterprise.

This is a textbook example of a modern buy vs build decision. On paper, building a ChatGPT wrapper looks straightforward, it’s just an API after all right. In practice, the costs and risks far outweigh the benefits compared to buying a purpose-built enterprise solution.

Don’t fall for the trap that “we are different” or “we can do this better with our framework” as building these experiences have become the core experience these multi-billion dollar model makers are now selling. If this is an internal solution, thing hard before falling for this trap. Unless this is somehow linked to your market differentiator. Even then think can this simply be a GPT or a MCP tool used by a commercial alternative like Co-Pilot, Gemini, or ChatGTP enterprise.

1. High Costs Upfront with Diminishing Returns

Even a seemingly modest AI wrapper quickly escalates into a significant investment. According to ApplifyLab, a basic AI wrapper app often costs $10,000 to $30,000, while a mid-tier solution ranges from $30,000 to $75,000, and a full enterprise-level implementation can exceed $75,000 to $200,000+, excluding ongoing costs like infrastructure, CI/CD, and maintenance (applifylab.com).

Industry-wide estimates suggest that launching complete AI-powered software, particularly in sectors such as fintech, logistics, or healthcare, can cost anywhere from $100,000 to $800,000+, driven by compliance, security, robust pipelines, and integration overhead (softermii.com).

Even just a proof-of-concept (POC) to test value can run $50,000 to $150,000 with no guarantee of ROI (medium.com).

Buy vs Build Takeaway: By the time your wrapper is ready for production, the cost-to-benefit ratio often collapses compared to simply adopting an enterprise-ready platform.

2. Security Risks with Low Visibility and High Stakes

DIY wrappers also tend to fall short on enterprise-grade security.

Prompt Injection Vulnerabilities
LLMs are inherently vulnerable to prompt injection attacks where crafted inputs (even hidden in documents or websites) can manipulate AI behavior or expose sensitive data. OWASP has flagged prompt injection as the top risk in its 2025 LLM Applications report (en.wikipedia.org).
Advanced variations, such as prompt-to-SQL injection, can compromise databases or trigger unauthorized actions via middleware such as LangChain (arxiv.org).
Real-world cases have already shown indirect prompt injection manipulating GPT-powered systems such as Bing chat (arxiv.org).
Custom GPT Leaks
OpenAI’s custom “GPTs” have been shown to leak initialization instructions and uploaded files through basic prompt injection, even by non-experts. Researchers easily extracted core data with “surprisingly straightforward” prompts (wired.com).
Broader LLM Security Risks
Generative AI systems are now a target for malicious actors. Researchers have even demonstrated covert “AI worms” capable of infiltrating systems and exfiltrating data through generative agents (wired.com, wsj.com).
More broadly, the WSJ notes that LLMs’ open-ended nature makes them susceptible to data exposure, manipulation, and reliability problems (wsj.com).

Building your own ChatGPT wrapper may feel like innovation, but it often ends up as a costly distraction that delivers little competitive advantage. Buying enterprise-ready solutions provides scale, security, and speed while allowing your team to focus on higher-value work. In the modern AI landscape, where risks are growing and the pace of change is accelerating, this is one of the clearest examples of why buy often beats build.

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