A Tier List for Company AI Strategies.

Source: Dev.to

Often these days, conversations inevitably turn to the topic of AI. While we should expect this as it has and will continue to transform our world, I can’t help but note the rather disparate takes, understandings, and discourse on the matter—even amongst IT professionals. Most of us don’t possess deep academic training on the subject; many are hobbyists who have spent most of their careers in a world where AI was just a curiosity. Consequently, discussions can feel juvenile, as if the masses were thrust into an informed conversation on a matter that takes years to master.

To help make sense of where organizations stand, I’ve created a tier list of company AI strategies. It frames AI adoption as a natural progression, from early experimentation to full AI‑native transformation. The five tiers are presented with a culinary spice metaphor.

I: Sprinkle

Light opportunistic exploitation of AI solutions

• AI is “sprinkled” onto existing solutions and processes.
• Strategists use the term “AI” vaguely, with little understanding of specific techniques, costs, or value.
• Many so‑called AI initiatives are merely traditional IT solutions mislabeled as AI.
• Adoption often consists of curating AI‑labeled vendors, tooling, basic plugins, and integrations.
• Efforts are broad yet shallow, lacking measurement or KPIs.
• No formal strategy, training, or organizational mandates are in place.
• Leadership typically has very little AI fluency and relies on instinctual assumptions about AI’s value proposition.

II: Stir

Deliberate integration of AI into specific workflows and tools

• Strategists possess at least a basic understanding of AI as a field.
• Vendors and tooling undergo informed analysis before adoption; integrations feature custom configurations.
• Organizational adoption includes basic guidelines and training.
• The focus is on targeted efficiency gains.
• Changes remain additive rather than transformative.

III: Simmer

Deep embedding of AI across multiple functions, with custom solutions and data feedback loops

• Leaders either have AI fluency or delegate to informed specialists.
• Use of fine‑tuned models, internal AI platforms, agentic workflows (AI agents that plan and execute multi‑step tasks), and Retrieval‑Augmented Generation (RAG) systems.
• Cross‑functional governance, data‑infrastructure investments, and measurable ROI tracking are established.
• AI influences decisions, optimizes operations, and begins reshaping how work is done.
• Legacy roles are augmented or replaced by AI‑oriented skill sets.
• The organization is “letting AI simmer”—changes are gradual but pervasive.

IV: Bake

AI is baked into core business processes and products; the company redesigns operations around AI capabilities

• Enterprise‑wide platforms, autonomous agents/swarm systems, and predictive analytics at scale become standard.
• AI‑driven automation handles complex workflows.
• Significant talent hiring, ethical frameworks, and a cultural shift toward AI fluency occur.
• New revenue streams or cost structures emerge from AI‑powered products or services.
• AI is no longer a layer—it is fundamental to how value is created.

V: Feast

AI‑native transformation: the entire organization is built or rebuilt around AI as the primary driver

• The organization becomes an industry leader, realizing exponential advantage through AI.
• Continuous AI‑human collaboration drives the evolution of proprietary AI models.
• AI is the core around which strategy, operations, and culture revolve.