Generic AI is becoming a baseline capability
If you’ve been following the AI industry over the last few years you would have certainly noticed the fierce competition between the major AI model providers. OpenAI, Google, Mistral and Grok have all produced fantastic models and all have attempted to outdo eachother with models that are faster, smarter and “better”.
However, the disadvantage of these models is they’re considered “general purpose”. In this sense, a user will employ a model to fulfill a wide range of tasks whereby the model will often respond with base-line answers to help satisfy a users query.
General purpose models are great, but as we enter 2026 it’s clear Executives and business leaders are wanting more.
In 2025, many large enterprises purchased and implemented powerful, general-purpose AI models. So far, these models have proven to be good for drafting, summarising, and accelerating many forms of knowledge work.
But as organisations move from pilots to production—especially in regulated or operationally complex environments—leaders are discovering a predictable gap.
The challenge is that general models are broad by design, while enterprise problems are narrow, constrained, and highly contextual.
That gap is a key reason 2026 is likely to be the year “industry AI models” become mainstream. If you’re unfamiliar with the concept, AI industry models are designed specifically for a sector—banking, retail, transport and logistics, mining—so they can perform specialised tasks more reliably than a generic model can.
As an AI company, we at Kodora have spoken with many business and government leaders who are looking for models built with an explicit industry focus—systems that understand their operating context, terminology, constraints, and risk settings, rather than relying on generic assumptions.
That demand is a key reason we’ve started to pioneer development in this space, designing industry-aligned models that can deliver more consistent, evidence-based analysis and more usable outputs for real enterprise workflows.
What leaders mean by “industry AI model” in practice
In boardrooms and architecture reviews, “industry AI model” doesn’t need to mean training a foundation model from scratch. More commonly, it means building an AI capability that behaves like an industry expert. An industry AI model is one that understands the language of the domain, reasons within industry constraints, and produces outputs in the formats that decision-makers and operators can actually use.
An industry AI model is usually a combination of a strong base model (such as ChatGPT) plus domain adaptation. That adaptation can include curated knowledge, training or grounding with enterprise content, domain-specific evaluation, workflow integration, or governance.
Why general-purpose models fall short in enterprise settings
General models like ChatGPT and Grok are impressive at breadth. However, the challenge is that enterprise work is often involves highly complex processes, combined with low risk appetite and almost always has a requirement for high accuracy.
Let’s take the banking industry for example. If an employee uses a general model that’s widely available today, its response may sound plausible but will likely miss a policy nuance, a product rule, or a regulatory constraint could create downstream risk.
In retail, generic recommendations provided by general AI models tend to ignore real-world inventory, fulfilment constraints, promotions, or supplier lead times. In transport and logistics, AI advice that doesn’t reflect chain-of-responsibility obligations, safety requirements, or operational realities mean the model becomes unusable.
Even when a general model is connected to internal documents, leaders still run into issues of inconsistent retrieval, version confusion, missing context across systems, and output that lacks evidence or traceability.
If you’ve used these models yourself in an enterprise setting, you would know the model may be “right” in a general sense but wrong in the organisational sense. This is because mainstream models don’t know which rules matter most, which exceptions are acceptable, or how decisions are actually made.
The benefits of industry models: fewer surprises, more dependable outcomes
The reason why enterprises are beginning to use AI industry models is because these new technologies are far better at reducing ambiguity and significantly better at providing employees highly specific responses that are tailored to their working environment.
One benefit leaders notice when using an industry model is improved response accuracy. Industry language tends to be dense with acronyms, overloaded terms, and assumptions. When an AI model is tuned—technically and operationally—to a specific industry or company, it stops guessing what words might mean and starts interpreting them the way your organisation does.
A second benefit is decision usefulness. Leaders rarely need long-form “chat” responses; they need structured outputs that align to how the organisation makes decisions. In banking, that might be a risk and control view, mapped to relevant policy sections and evidence. In retail, it might be a set of supply chain trade-offs explained in terms of service level, margin, and customer experience. In logistics, it might be an operational exception analysis that links the event to obligations, incident categories, and next actions.
A third benefit is governance. In large enterprises, AI systems must be deployable under clear controls: data access, auditability, escalation pathways, and defensible reasoning. Industry models tend to be built with these constraints in mind from the start, rather than added as an afterthought.
Why 2026 is the likely inflection point
In terms of the evolution of AI models, it is clear we are seeing three forces are pushing in the same direction.
First, enterprises across the world are starting to move beyond experimentation with general models. The question that’s being posed by leaders appears to be shifting from “Can AI do this?” to “Can we trust it in production, at scale, under scrutiny?” As a result, business and government leaders are starting to have higher expectations regarding AI reliability, traceability, security, and measurable outcomes.
Second, base models are rapidly becoming commoditised which means most organisations will have access to broadly similar underlying capabilities. As that happens, competitive advantage shifts away from simply “having AI” and toward domain fit and how well an AI system performs in your specific operating environment, using your data, reflecting your constraints, and producing outputs that align with your workflows and risk settings.
In other words, differentiation moves up the stack, away from the foundation model itself to industry-specific knowledge, governance, evaluation, and integration that make it reliable in practice. That shift naturally favours industry models.
Third, operational and regulatory pressure is rising. In sectors like finance and critical infrastructure supply chains, leaders increasingly need AI outputs that can be explained, defended, and audited. If a system can’t show its working, such as what it relied on and why, then it won’t be trusted in core workflows.
What “better than generic” actually means
For the sake of clarity, using an industry model means achieving competitive advantage. Industry models will help organisations win when the work depends on domain constraints, data structures, and organisational rules. In those settings, “better” also results in fewer hallucinations, fewer misinterpretations of terminology, more consistent outputs across teams, higher relevance to the actual decision being made, and stronger traceability.
When leaders ask whether the organisation should invest here, a useful framing is: where do we have repeated decisions or high-cost exceptions that currently depend on internal or scarce expertise?
Those are the natural candidates for industry models, because the model can be shaped around the patterns experts look for, then deployed to scale that expertise more consistently.
A measured way to approach industry models
For large enterprises, the sensible path is to start narrow and prove value in one or two workflows where outcomes are measurable and governance requirements are clear. From there, an industry model can be extended as the organisation’s confidence grows. This can be achieved using domain evaluation, user feedback loops, and controls that match the risk profile.
Kodora’s approach aligns with this philosophy. We build specialised models where the work is complex and the demand for reliable, grounded outputs is high. A model for banking for example can be found here, and our Motorsport AI model page provides a concrete example of how specialisation can be applied in a high-performance domain.
The practical takeaway for decision makers
By 2026, general-purpose AI may start to feel stale to many people. Therefore, the organisations that get disproportionate value will be those that use AI industry models that are designed for the constraints, language, and risk expectations of their environment.
For leaders, the goal is not to chase novelty. It’s to choose a small number of high-value workflows and deploy AI that is dependable enough to become part of how the enterprise runs.
Industry AI models are one of the cheapest and clearest paths to that outcome.
