Standardising Robot Programming for Faster AI Automation

Robot adoption keeps climbing, but robot deployment is still painfully slow. The bottleneck isn’t hardware—it’s software. In too many factories and warehouses, getting a robot from “delivered” to “productive” means weeks (sometimes months) of integration work, vendor-specific coding, and fragile workarounds.

That’s why the conversation in Robot Talk Episode 123 with Nick Thompson (CEO of BOW) lands so well for anyone building or buying AI-driven automation. The core idea is simple: if we standardise how robots are programmed, we make it dramatically easier to add intelligence—AI perception, planning, and optimisation—on top of them.

And yes, I’m going to take a stance: without better standardisation, AI in robotics will keep overpromising and underdelivering in the places that matter most—manufacturing, logistics, healthcare, and field service. Not because AI is weak, but because robot software is still too bespoke.

Why standardised robot programming matters (especially for AI)

Standardised robot programming matters because it reduces integration time, lowers risk, and makes AI features portable across robot brands. If every robot requires a different mental model, toolchain, and interface, teams spend their budget re-writing glue code instead of improving performance.

In practice, standardisation changes the economics of automation:

• From one-off projects to repeatable rollouts. When code patterns and interfaces repeat, you can scale from one cell to ten sites.
• From specialist-only programming to broader adoption. More approachable tooling means process engineers and operations teams can contribute.
• From brittle integrations to maintainable systems. Standards push you toward tested interfaces rather than vendor-specific hacks.

AI integration needs consistency more than novelty

AI robotics doesn’t fail because models can’t predict; it fails because systems can’t integrate reliably. When you add AI perception (vision), AI planning (task sequencing), or learning-based control, you introduce additional moving parts—data pipelines, latency, fallback logic, model monitoring.

A stable, standardised programming layer becomes the “socket” that AI plugs into.

A useful rule: AI is easiest to deploy where the robot interface is boring, predictable, and well-documented.

The real cost of vendor-specific robot programming

Vendor-specific robot languages and interfaces create hidden costs: training overhead, code duplication, and lock-in. Many teams only discover this after the second or third automation project—when they realise the work doesn’t transfer.

Here’s what it looks like on the ground:

• You buy Robot Brand A for a packaging line and build a full stack around it.
• Six months later, supply constraints or pricing push you to Robot Brand B.
• Suddenly, your entire programming approach changes: motion primitives, safety logic, IO mapping, error handling, even the deployment pipeline.

That’s not an engineering problem; it’s an operational tax.

Standardisation reduces downtime risk

When every robot behaves differently, your recovery procedures become tribal knowledge. Standardising programming and operational interfaces (alarms, state machines, logging) means:

• faster troubleshooting,
• fewer “only Dave knows this” incidents,
• cleaner handoffs between integrators, OEMs, and internal teams.

This becomes especially relevant in December planning cycles, when many operations teams are budgeting for next year’s automation projects and want predictable time-to-value.

What “standardising robot programming” actually means

Standardisation isn’t one thing—it’s a stack. If you’re evaluating platforms and approaches like the ones Nick Thompson’s company focuses on, it helps to separate the layers.

1) Standard task abstractions

Robots should be programmed in terms of tasks and outcomes, not vendor-specific motion scripts. Examples of standard task abstractions:

• pick / place with constraints
• inspect with pass/fail criteria
• palletize with pattern definitions
• navigate with goal locations and zones

When tasks are standardised, AI can optimise them—like dynamically adjusting pick points based on vision or re-planning sequences when upstream delays happen.

2) Standard data and event models

AI needs clean data: consistent state, events, and logs. A standard event model answers questions like:

• What does “paused” vs “faulted” mean across robots?
• Where do errors get recorded?
• How do we trace a bad pick back to sensor input?

If you can’t answer those consistently, you can’t do reliable analytics or model monitoring.

3) Standard deployment and versioning

Robots are becoming software products, and they need software discipline. Standardisation here means:

• versioned robot “apps” or workflows
• environment promotion (test → staging → production)
• rollback plans
• configuration management per site

This is where many AI robotics pilots fall apart: the model is fine, but the release process isn’t.

4) Standard safety and compliance interfaces

Safety can’t be an afterthought, and it can’t be re-invented per vendor. A standardised safety interface clarifies:

• interlocks and safety-rated IO
• speed and separation monitoring behaviours
• safe stop categories and recovery steps

For regulated environments (healthcare, pharma, food), this layer determines whether AI-enabled automation is deployable at all.

Where standardisation pays off fastest: manufacturing and logistics

Manufacturing and logistics benefit first because they run many similar processes across many sites. That’s also where AI has obvious upside: better picking, smarter scheduling, higher utilisation.

Manufacturing: faster cell replication

In discrete manufacturing, a common pattern is: build one successful robot cell, then replicate it.

Standardised programming turns replication into an engineering exercise instead of a re-integration project. You can keep the process logic the same while swapping:

• robot arm models,
• grippers,
• vision cameras,
• conveyors and PLC brands.

AI fits naturally here. Once the programming layer is stable, AI improvements (like better pose estimation or adaptive grasping) can be rolled out across many cells without rewriting everything.

Logistics: interoperability across fleets

Warehouses increasingly operate mixed fleets: AMRs for transport, arms for depalletising, sortation systems, and vision stations.

Fleet-level AI—traffic optimisation, task allocation, congestion prediction—depends on consistent robot interfaces. If each machine speaks a different operational language, your “fleet intelligence” becomes a set of custom adapters that constantly break.

Standardisation doesn’t remove complexity. It moves complexity into shared infrastructure where it can be tested once and reused.

Standardisation is also about collaboration, not just tooling

Nick Thompson’s emphasis on making robots easier to program points to a bigger truth: robotics scales when vendors, integrators, and end users agree on shared conventions.

If you’ve worked on automation projects, you’ve seen the friction:

• OEM wants to protect their ecosystem.
• Integrator wants to minimise risk and stick to what they know.
• End user wants portability and faster changeovers.

Standardisation is the compromise that still lets everyone win:

• OEMs compete on reliability and performance.
• Integrators deliver faster with fewer surprises.
• End users avoid being trapped and can adopt AI features sooner.

My strong opinion: the next wave of industrial AI won’t be won by the fanciest model—it’ll be won by the teams that standardise integration first.

Practical checklist: how to evaluate a “standardised robot programming” approach

You can evaluate standardisation with a few blunt questions. Use this checklist when you’re choosing a platform, integrator, or internal architecture.

1. Can we run the same workflow on two robot brands with minimal changes?
2. Is there a clear separation between process logic and hardware drivers?
3. Do we get a consistent state machine (idle/running/paused/faulted) across deployments?
4. Are logs and events structured, searchable, and comparable across sites?
5. Is there a versioned release process with rollbacks?
6. How are safety behaviours represented and validated?
7. Can AI components be added as modules (vision, planning, optimisation) without rewriting the workflow?

If you can’t get solid answers, you’re buying future integration pain.

Common questions teams ask before standardising

“Will standardisation limit what our robots can do?”

Good standardisation exposes the common 80% while still allowing escape hatches for the hard 20%. You want a framework that supports vendor-specific extensions without forcing your entire codebase to become vendor-specific.

“Does standardisation make sense for small deployments?”

If you plan to stay at one robot forever, maybe not. But most organisations don’t. They expand, swap vendors, add sites, or broaden use cases. Standardisation is insurance against your own growth.

“How does this connect to AI-enabled robotics?”

Standardisation is what makes AI portable. It lets you take an AI improvement—say, a better defect detection model—and deploy it across different cells without rebuilding the integration each time.

The next step for AI in Robotics & Automation: boring interfaces, smarter behaviour

Standardising robot programming isn’t flashy, but it’s the work that makes everything else possible. It’s how you get from pilots to production. It’s how AI stops being a demo and starts being an operational capability.

If you’re planning 2026 automation initiatives right now, focus less on which AI model to pick and more on how your robot software will stay maintainable across vendors, sites, and use cases. Once that foundation is in place, AI features—vision, planning, predictive maintenance, adaptive motion—become upgrades instead of reinventions.

If you’re investing in AI-driven robotics, what’s your bigger risk: the model’s accuracy, or the integration debt you’re quietly accumulating?