Kwanchanok - stock.adobe.com
AI without borders: Why the next advantage is in model ownership, not just access
Enterprises have become familiar with AI tools by accessing publicly available models - but to gain true differentiation, it's time to explore ownership of your AI model
For the past five years, much of the enterprise conversation around artificial intelligence (AI) has revolved around access – with access to application programming interfaces (APIs) from hyperscalers, pre-trained models, and plug-and-play integrations promising productivity gains.
This phase made sense. Leaders wanted to move quickly, experimenting with AI without the cost of building models from scratch. “AI-as-a-service” lowered barriers and accelerated adoption.
But as the dust settles, a new reality is apparent - in the long run, access is not enough. The real advantage will come from ownership, treating models as core enterprise assets, not disposable services.
From tools to assets
Today’s AI stack often looks like a patchwork of third-party models. Marketing leans on generative copy tools. Developers use GitHub Copilot. Analysts query ChatGPT-like assistants.
This has enabled rapid experimentation but exposes three structural weaknesses.
The first is intellectual property (IP) risk. Outputs generated by external models can be legally ambiguous, which is a red flag in IP-intensive industries.
Second, feeding proprietary data into external models creates security and regulatory concerns.
And third, governance gaps - as regulations tighten, the lack of explainability or auditability increases compliance risks.
These risks are tolerable when AI is peripheral. But as AI becomes mission-critical - embedded in customer interactions, product design, and supply chains - the stakes rise. Forward-looking enterprises are no longer content to simply use AI. They want to own it.
What model ownership really means
Model ownership does not mean building a large language model (LLM) from scratch - an endeavour requiring billions of dollars. It means treating models as assets to be trained, customised, governed, and controlled in line with enterprise priorities.
This involves bespoke training on proprietary data sets; infrastructure control via secure, enterprise-run environments; governance frameworks embedding transparency, bias mitigation, and ethics; and lifecycle management with versioning, monitoring, updating, and retiring models as needed.
The difference is as much philosophical as technical: AI becomes part of intellectual capital, not an external service.
Lessons from history
The pattern is familiar. In the 1990s, many firms outsourced their web presence, only to bring it back in-house as marketplace conditions demanded. The same shift occurred with cloud - from shared infrastructure to hybrid and private models for control and resilience. AI is on the same curve. Early adoption is about consumption; maturity is about ownership.
Waiting too long risks three traps:
- Vendor dependency - lock-in with little negotiating power.
- Regulatory fragility - struggles to prove governance and compliance.
- Strategic sameness - no differentiation if everyone uses the same tools.
Enterprises that invest in ownership will gain defensible IP that is protectable; data security within controlled environments; regulatory resilience aligned to evolving laws; and sustainable advantage tuned to unique strengths.
Why leaders need to act now
Having led global technology and operations at scale - from FTSE 100 firms to financial services worldwide, I’ve seen how ownership creates resilience.
In the global payments infrastructure, ownership of critical systems ensured not only speed but also compliance across jurisdictions.
In digital transformation, organisations that built and governed their own cloud layers avoided costly lock-in and responded faster to regulatory changes.
In financial services, where data protection and trust are existential, ownership of core digital assets often marks the difference between market leaders and laggards.
The lesson is consistent - leaders who invest in control, governance, and long-term capability consistently outperform those who outsource their core assets. AI is no different.
Organisations that will thrive in the next decade will be those that treat intelligence as a core enterprise asset, not a rented utility.
Building the roadmap
Ownership is a journey, not a switch. Leaders should:
- Audit the AI footprint - map where external models are embedded and assess risks.
- Prioritise critical workflows - focus ownership where it creates the most value.
- Develop internal capability - build MLOps, governance, and ethical AI expertise.
- Experiment with hybrid models - external APIs for non-core, proprietary models for strategic domains.
- Engage the board - elevate AI ownership as a strategic issue.
Beyond these technical steps, cultural readiness matters. Teams must learn to see models not just as utilities but as products requiring ongoing stewardship. Boards must shift from viewing AI spend as “IT cost” to recognising it as capital investment in intellectual property. And leaders should align incentives so that ownership of models, data and outcomes is embedded in decision-making at every level.
Beyond borders
AI promises to transcend borders, but reliance on third parties leaves enterprises bound by someone else’s roadmap. Owning models, not just outputs, unlock freedom - defining guardrails, harnessing unique data, and creating intelligence that reflects enterprise values.
For IT leaders such as CIOs and CTOs, this means shifting the conversation from tools to assets. Ask: “Which models should we own outright?”
For chief risk officers, define AI governance frameworks now, before regulators do it for you.
If you’re a CFO, view ownership as capital investment in defensible IP, not just operational expense.
And for boards and chairs - put AI ownership on the strategic agenda, alongside cyber security and digital infrastructure.
The next wave of AI strength will not be decided by who has access to the best API, but by who owns the intelligence at the heart of the enterprise.
For senior leaders, the choice is stark - build the foundation for ownership now, or risk being locked out of the next frontier.
As leaders, we have a once-in-a-generation opportunity. Just as the internet and cloud reshaped the enterprise landscape, AI will redraw boundaries. The question is not whether to use AI, that debate is over. The question is whether we will own the intelligence that shapes our future or leave it in the hands of others.
History suggests the answer will define the winners and losers of the decade ahead.
Read more about AI strategy
- Inside Prudential’s AI strategy - Prudential is leveraging AI across its operations, from enhancing customer interactions and streamlining internal processes to empowering its workforce through upskilling initiatives.
- Interview: Currys CIO Andy Gamble on AI strategy - The CIO of Currys on the four pillars of the retailer’s artificial intelligence strategy and how generative AI can enable staff to be the best versions of themselves.
- Interview: Amanda Stent, head of AI strategy and research, Bloomberg - Hallucinating AI, which lies through its teeth, keeps Amanda Stent busy at data analytics and intelligence giant Bloomberg.
