Different kinds of intelligent systems have emerged as artificial intelligence capabilities have grown. Understanding these categories helps clarify what each type does well, where it falls short, and how they relate to one another.
Creation and Generation
AI has demonstrated the ability to create things that humans previously created — text, images, code, presentations, marketing copy, and other artifacts. Much of the excitement following the release of ChatGPT and DALL-E 2 in 2022 came from novelty and accessibility, though quality, consistency, and accountability remain uneven.
Automation
AI has been applied to automate tasks and jobs, particularly when work is repetitive, rules-based, or narrowly scoped. This has produced measurable productivity gains, alongside understandable concern about job displacement.
Discovery
AI has enabled new forms of discovery — from drug and materials development to optimization of complex systems to improved modeling of customer behavior to advances in mathematics and science. These applications are powerful, but largely removed from most employees' day-to-day work.
Two Less-Discussed Systems
Two additional kinds of intelligent systems have received less media coverage. Concierge Computing emerged a few months ago. It is gaining greater mainstream exposure and has the potential to become the most discussed workplace AI topic in 2026. An early type of system, SkillStream Performance Engines, emerged in 2023 and, despite its effectiveness, hasn't garnered much attention. Concierge Computing and SkillStream Performance Engines serve different, complementary purposes.
Concierge Computing
Concierge Computing interprets a person's intent, plans and executes work, synthesizes information, coordinates across tools and workflows, and escalates key decisions to the person. It works well where the specific method isn't important, creativity and exploration are acceptable, and the person can evaluate quality.
SkillStream Performance Engines
SkillStream Performance Engines are designed for situations where there is a strong, preferred method for performing a task, quality matters deeply, and mistakes are subtle, costly, or compounding. They are especially useful when the person lacks the expertise to express intent clearly and evaluate quality reliably.
Real-world examples include a new manager giving performance feedback, senior executives generating solutions using organizational models, and employees increasing empathy in communications.
Complementary, Not Exclusive
These different types of intelligent systems aren't mutually exclusive, and in many cases, a particular work activity will use more than one. For example, a marketer might use Concierge Computing to gather competitive intelligence, automating periodic updates and generating reports, which are then run through a SkillStream Performance Engine to ensure regulatory compliance.