ChatGPT is about to evolve once again

A year ago, “using AI” often meant informal experimentation.

Today, many organisations are already seeing value from role-based assistants:

• Finance assistants preparing management and board packs
• HR assistants standardising onboarding and policy responses
• Sales assistants summarising accounts and drafting updates

That evolution matters because it turns AI from a novelty into infrastructure.

Frontier takes the next step. It’s designed for AI agents — systems that can reason over context, use tools, complete multi-step tasks, and operate across applications.

That leap feels exciting. It can also feel unsettling.

And that reaction makes sense.

Because agents change the question from “is this useful?” to “are we ready for this?”

Frontier is a signal, not just a product

Frontier is positioned as a way to build and manage “AI coworkers”. The language is deliberate.

Rather than focusing only on intelligence, Frontier focuses on the environment around AI:

• Shared business context across systems
• Onboarding and institutional knowledge
• Learning through feedback on real work
• Clear identity, permissions, and guardrails

In other words, the things humans need to be effective at work.

The implication is clear.

The biggest blocker to AI value is no longer what the models can do.

It’s how organisations deploy, govern, and scale them.

This is what many teams are starting to feel — the opportunity gap between capability and execution.

The opportunity gap — and the adoption gap beneath it

AI models are improving at extraordinary speed.

What isn’t improving at the same pace is most organisations’ ability to operationalise them.

We see the same pattern repeatedly:

• AI use is scattered across teams
• Tools are disconnected
• Data access is inconsistent
• Governance is unclear
• Output quality varies widely

Each new assistant can add value — but also complexity.

This creates an opportunity gap between what AI could do and what teams can reliably put into production.

Underneath that sits a deeper problem: the adoption gap.

Why waiting makes the transition harder

When leadership teams hesitate, it’s rarely because they don’t see the potential.

It’s usually because of sensible concerns:

• Data security and compliance
• Quality, accountability, and risk
• Staff confidence and capability
• Change fatigue

Those concerns are valid.

But delaying action has a cost.

Agentic work is not a single tool you turn on later.

It’s a way of working.

If an organisation is still debating whether to use a licensed AI platform, the issue isn’t AI maturity.

It’s missing foundations.

And those foundations are exactly what agents depend on:

• Clear permissions and access controls
• Trusted, shared data sources
• Defined workflows and standards
• People who know how to work with AI, not just prompt it

The longer these foundations are delayed, the harder the eventual shift becomes.

How organisations actually succeed with AI

Despite the noise, successful adoption follows a surprisingly consistent path.

It’s not dramatic. It’s disciplined.

1. Start with a work-licensed AI platform

This is table stakes.

A licensed platform provides:

• Data protection and privacy controls
• Administrative oversight
• Central governance
• A single environment to build capability

Relying on consumer tools for business work creates fragmentation — and fragmentation kills scale, especially when agents enter the picture.

2. Treat AI as a core skill and train accordingly

AI adoption is behaviour change.

You don’t get behaviour change from policies alone.

You get it from:

• Hands-on, practical training
• Examples drawn from real day-to-day work
• Clear guardrails and expectations
• Ongoing reinforcement

We’ve trained hundreds of staff across hundreds of hours, and the pattern is consistent.

Most people don’t need theory.

They need confidence.

As AI moves from assistance to action, that confidence becomes critical.

Supervising agentic systems is a skill — and skills must be learned.

3. Operationalise value with custom assistants

The biggest jump in ROI happens when organisations standardise their best use cases.

Custom assistants:

• Reduce variation
• Embed policy, tone, and standards
• Make best practice repeatable
• Turn “helpful” into “reliable”

This is where AI stops being optional and starts becoming part of how work gets done.

4. Progress deliberately toward agent workflows

Only once the foundations are in place does agentic work make sense.

That progression usually starts small:

• Drafting outputs from system data
• Suggesting next actions
• Preparing summaries or files

Over time, it expands to:

• Multi-step workflows
• Tool use across systems
• Approvals, escalation, and auditability

This is the space platforms like Frontier are designed for.

A practical 30–60–90 adoption plan

For organisations looking for a grounded starting point, this is a proven approach.

First 30 days: foundations

• Adopt a licensed AI platform
• Define clear usage rules and guardrails
• Identify three high-impact workflows
• Train a pilot group

Days 31–60: operationalise

• Build role-based custom assistants
• Add internal documents, language, and processes
• Define what “good” output looks like
• Measure time saved and quality improvements

Days 61–90: prepare for agentic work

• Connect assistants to key systems
• Introduce permissions and approval steps
• Review what worked and what didn’t
• Expand training to managers and process owners

The objective isn’t to deploy an agent for the sake of it.

It’s to become an organisation that can adopt agents safely and confidently.

The honest message to professionals

Yes, this moment is exciting, and yes, it can feel a little uncomfortable. But the real risk isn’t acting too early; it’s acting too late. If you’re not using a work-licensed AI platform, now is the time to move. If you’re not actively training your people, start, because adoption is behaviour change, not a software rollout. And if you’re not building custom tools inside those platforms, you’re leaving repeatable value on the table. Not out of fear, but out of responsibility, because work is becoming more agentic and readiness compounds.

Book a call: build your AI adoption plan

gecco helps UK organisations adopt AI safely, train teams effectively, and build assistants that fit real workflows — so they’re ready for what comes next.

If you want to move from experiments to impact, book a call to talk through your AI adoption plan.

Source: https://openai.com/index/introducing-openai-frontier/