The three infrastructure decisions that determine AI delivery speed in 2026

Delivery is at risk of halting if infrastructure decisions aren’t made, even if you have budget, use cases, leadership support and the team. 

Teams can wait weeks for development environments. Deployment takes longer than building the actual model. Costs spiral because nobody planned for GPU usage properly. And six months in, everyone's frustrated because the infrastructure that was supposed to accelerate delivery has become the bottleneck.

The problem is that infrastructure decisions were made based on what seemed sensible at the time, what vendors were selling, or what other organisations were doing, rather than what would actually enable fast delivery for your specific context.

If you're finalising your 2026 AI plans right now, getting these infrastructure decisions right is critical for your delivery needs, your capability and your budget reality.

Here are the three decisions that matter most.

Decision 1: When to invest in sophisticated infrastructure (and when lightweight is enough)

The biggest mistake I see organisations make is building infrastructure for a future state they haven't reached yet.

Someone decides the organisation needs MLOps, so they spend 6-12 months and significant budget building a sophisticated platform with all the bells and whistles.

Then they step back and realise they've got three models in production. The platform can handle hundreds, but there aren't hundreds to handle. Meanwhile, delivery has stalled because teams have been waiting for infrastructure instead of shipping use cases.

Here's what actually works: match your infrastructure investment to your current scale, not your aspirational scale.

If you're early in your AI journey with a handful of models, you don't need a full MLOps platform yet. You need good hygiene, including version control for code and models, documentation, basic testing, monitoring dashboards. You can achieve this with lightweight tooling and solid processes, which lets you move fast.

As you scale (models, teams, complexity), you'll hit a point where manual processes become unsustainable. That's when investing in sophisticated infrastructure makes sense. You'll know you've hit that point when:

• Tracking model versions manually is error-prone and time-consuming.

• Deployment requires significant manual work and takes too long.

• You can't easily reproduce results or roll back problematic changes.

• You've got multiple teams working independently and you need consistency and visibility.

The key is letting your actual delivery volume and pain points drive infrastructure investment, not building for theoretical future scale. You can always add sophistication later when you need it, but you can't get back the six months you spent building infrastructure you didn't need yet.

Decision 2: What to standardise and where to allow flexibility

This decision causes endless debate in every organisation I work with: should we have one unified AI platform that everyone uses, or let different teams use different tools?

The unified platform camp argues for standardisation because it reduces complexity, improves governance, makes it easier to share knowledge and gives you economies of scale on licensing.

The federated camp argues for flexibility because different AI use cases need different tools, teams work better with tools they know and enforcing a single platform creates bottlenecks and kills innovation.

Both arguments have merit. Here's what I've learned actually works in practice:

Standardise where inconsistency creates real problems. Your core infrastructure (compute, storage, networking, security, identity management) should be consistent. Model deployment, monitoring and governance should be standardised because you need visibility into what's running in production and audit trails for compliance. This is where fragmentation genuinely hurts you.

Allow flexibility where it doesn't matter. Let teams use the development tools, frameworks and languages that work best for their specific use cases. As long as they can deploy to your standard infrastructure and meet governance requirements, tool choice in development doesn't create meaningful problems.

Organisations can waste months debating whether data scientists should use Python or R, TensorFlow or PyTorch, Jupyter notebooks or VS Code. These debates consume energy and create friction without materially impacting delivery speed or quality.

The hybrid approach, where you standardise the foundation and allow flexibility in development, gives you governance and efficiency where it matters while avoiding the innovation-killing effects of forcing everyone to use identical tools for everything.

Decision 3: How to control infrastructure costs before they control you

Here's the infrastructure reality that catches everyone by surprise: AI compute costs will be significantly higher than you budgeted for, unless you actively manage them from day one.

Development teams start experimenting with AI agents and use cases. They need GPUs for model training. Someone spins up a large instance for experiments and forgets to shut it down. Another team runs inference requests through an expensive model when a cheaper one would work fine. Finance starts asking uncomfortable questions when the cloud bill hits six figures.

Organisations can spend 3-5x their planned infrastructure budget because nobody was tracking compute usage or optimising for cost. And here's the thing: it's not because teams are careless. It's because AI infrastructure costs work differently from traditional software infrastructure, and most organisations haven't adapted their cost management practices.

What actually works:

Set explicit budget allocations for different activities, split into experimentation, development, production. Teams need to know what they can spend and they need visibility into current usage against those budgets.

Implement automated monitoring and alerts for compute usage. If someone leaves a GPU instance running overnight or over the weekend, you should know immediately. These aren't small costs, a high-end GPU instance can cost hundreds of pounds per day, so this is really important.

Require cost-benefit justification for expensive infrastructure decisions. If a team wants to train a massive model, they should articulate why it's worth the compute cost versus lighter alternatives. Sometimes the expensive approach is justified. Often it's not.

Build cost optimisation into your development practices. Use smaller models where they'll work. Batch inference requests efficiently. Use spot instances for training when possible. Shut down non-production environments outside working hours. These practices should be standard, not optional extras.

The organisations that control AI costs are the ones who spend intentionally and can articulate what value they're getting for the money.

How these decisions connect to everything else

These infrastructure decisions don't exist in isolation. They're deeply connected to your broader AI strategy, your use case priorities and your delivery approach.

If you decided in your 2026 roadmap that you're primarily buying vendor AI solutions rather than building custom ones, your infrastructure needs look different. You need robust integration capabilities and vendor management processes rather than full model development infrastructure.

If your AI roadmap is ambitious with dozens of use cases planned, you'll hit infrastructure constraints faster and need more sophisticated platforms sooner. If you're starting with a focused set of high-value use cases, lightweight infrastructure might serve you well into 2026.

If your organisation is building AI capability and hiring data science teams, they'll need proper tooling and infrastructure from day one. If you're working with partners who bring their own infrastructure and hand over finished solutions, your infrastructure requirements are different.

The mistake is making infrastructure decisions in isolation and then trying to fit your delivery approach to whatever infrastructure you've built. Do it the other way around: understand your delivery needs first, then build infrastructure that actually enables those needs.

What to do next

This article covers the four highest impact changes you could make to your SDLC to adapt it to be AI-enabled. If you’d like more details, including a “micro-assessment” set of questions to bring into planning meetings, sign up for our micro-assessment email series below - you’ll receive 5 weekly emails from me (Mark), or my Co-Founder, Ben, to support with your 2026 AI planning in its entirety.

If you want to talk through your specific situation, my DMs are always open on LinkedIn. I read and respond to every message.