2026 Is Not the Year to 'Try' AI

2025 was the year AI stopped being optional for print and packaging companies.

Not because it was easy.
Not because it was perfect.
But because ignoring it stopped being a responsible choice.

By the end of 2025, AI was no longer confined to innovative teams or side projects. It showed up in estimating, in customer demands, in procurement conversations, and in boardrooms asking the same question over and over: “Why are we behind?”

For most organizations, AI didn’t enter through a plan. It entered through disruption.

And that’s not a bad thing. Disruption wakes people up.

But 2026 is not the year for curiosity, pilots for the sake of pilots, or another round of “let’s see how this goes.” 2026 is the year to get serious about readiness, strategy, and execution.

And that starts with understanding a hard truth the industry is still dancing around:

Not all AI approaches are created equal, and choosing the wrong one will cost you more time, money, and credibility than doing nothing at all.

The Fork in the Road: Tools Versus Capability

As we head into 2026, print and packaging leaders with real budgets and real intent are facing a clear fork in the road.

On one side:
Off-the-shelf AI platforms

On the other:
Custom AI agents and agentic systems designed around your business

Both have a place.
Only one creates durable advantage.

Off-the-Shelf AI Platforms: Fast, Familiar, Limited

Off-the-shelf AI tools such as Copilot, ChatGPT Enterprise, workflow automation platforms, and AI features embedded inside ERP or MIS exploded in 2024 and 2025 — for good reason.

They’re:

• Easy to buy.
• Easy to pilot.
• Easy to justify.

For many organizations, they were the first real exposure to AI in daily operations.

And that’s fine. I get it.

These tools are excellent for:

• Helping individuals work a little faster.
• Automating the easy stuff.
• Acting as a smart assistant, not a decision maker.
• Experimenting without real risk.

What off-the-shelf AI platforms are not designed to do is:

• Improve how the business operates, not just how people work.
• Automate decisions and workflows, not just tasks.
• Understand your data, your rules, and your constraints.
• Create an advantage, not just avoid risk.

By design, off-the-shelf platforms create shared advantage.
Your competitors are using the same tools.
With the same features.
On the same roadmap.

That doesn’t make them bad tools.
It makes them a starting point.

Tools are not a strategy. I say that often because it’s true. And if your AI approach stops here, your upside isn’t just capped, it’s predefined.

Custom AI Agents: Where Strategy Becomes Capability

Custom AI agents are different — not because they’re flashier, but because they’re owned.

A custom AI agent:

• Operates inside your workflows.
• Understands your data structures.
• Follows your rules and constraints.
• Learns from your historical patterns.

In printing and packaging, that might mean:

• Agents that understand estimating logic.
• Agents that monitor production exceptions.
• Agents that orchestrate data across ERP, MIS, scheduling, and customer systems.
• Agents that automate decisions — not just tasks.

This is where AI stops being a tool and starts becoming infrastructure.
But here’s the catch: Custom AI isn’t a software project; it’s more than just writing code. It’s about intentional design and how systems are built to operate over time.

And this is where many companies will get it wrong in 2026.

Not All 'Custom' Is Equal: Consultancy Versus Code Shop

One of the more common assumptions — and often a point of pride — I see heading into 2026 sounds something like this: “If we need custom AI, we’ll just hire a developer.”

That instinct isn’t wrong. It’s familiar, and for a lot of things, it works.

It works for websites.
It works for integrations.
It works for building features against a clear specification.

Where it starts to break down is with agentic AI systems.

Because once AI is expected to make decisions, move work across systems, and operate inside real business constraints, the challenge stops being “Can someone write the code?” and becomes “Should this system behave this way at all?”

That’s the distinction leaders need to understand going into 2026.

General Custom Software Developers

Traditional custom developers are excellent at:

• Writing code to a spec.
• Building applications.
• Connecting APIs.
• Delivering features.

Their pricing model reflects this:

• Project-based.
• Feature-driven.
• Scope-defined.

What they typically do not provide:

• AI readiness assessments.
• Data governance strategy.
• Model selection and life cycle planning.
• Risk management for AI behavior.
• ROI measurement tied to operations — remember, if AI doesn’t hit the P&L, its just entertainment.

They build what you ask for — even if it shouldn’t exist.

Agentic AI Development Teams

Agentic AI development teams operate differently.

They start with:

• Readiness before design.
• Strategy before code.
• Governance before scale.

Their work includes:

• Designing multi-agent systems.
• Defining decision boundaries.
• Engineering human-in-the-loop workflows.
• Managing data sovereignty and IP ownership.
• Aligning AI capability to financial outcomes.

Their pricing reflects that reality:

• Fewer “features,” more architecture.
• Ongoing advisory and refinement.

You’re not paying for lines of code. You’re paying for judgment. And in AI, judgment is everything.

The Pricing Reality (2026 Outlook)

As companies head into 2026, the conversation around AI investment needs to mature.

Off-the-shelf AI platforms are inexpensive and fast to roll out. They deliver quick wins, but the upside is limited and shared with every competitor using the same tools.

Custom software development sits in the middle ground. It offers flexibility and execution speed, but without a clear AI strategy, it often trades short-term progress for long-term risk.

Agentic AI development requires a higher upfront investment. In return, it creates something fundamentally different: ownership, control, and advantage that compounds over time.

For organizations with the budget and commitment, the real question isn’t “Can we afford this?” It’s “Can we afford to build AI without a plan?”

2026: The Year of Readiness, Strategy, and Execution

2025 was the wake-up call.

2026 is the response.

The companies that win this year will:

• Stop experimenting blindly.
• Get honest about their data and processes.
• Choose ownership over convenience.
• Invest in AI as capability, not a novelty.

This doesn’t mean boiling the ocean. It means starting correctly.

Readiness assessments.
Clear prioritization.
Intentional partner selection.
Disciplined execution.

No more delays.
No more “next quarter.”
No more pretending AI is just another software upgrade.

Because the printers and packaging companies that treat AI like infrastructure in 2026 will not just survive the next cycle, they’ll define it.

And the rest will spend the year catching up.