The phrase ‘Artificial Intelligence Software’ conjures up images of high-level technology that operates without the need for human involvement. And although that is true to a large extent, there is undoubtedly a need for an element of human skill to keep the software up to speed.
This article will explore the human skill layer. It will also list specific roles and how these play out in the real-life deployment of AI software.
The Human Skill Layer
This is the part of AI software architecture where human expertise is woven into the system. It is incorporated from the ground up, not added later on as an afterthought or for damage control. It involves the roles, responsibilities, and decision rights that define how human judgment interacts with AI algorithms.
In practice, this means the system becomes well acquainted with human input. It “knows” which human role can validate an outcome, override a decision, define acceptable boundaries, and intervene when uncertainty exceeds predefined levels.
Why AI Software Needs a Human Skill Layer
AI software operates in complex and unpredictable environments. The complexity is further varied across use cases and applications. This is unlike the perfectly defined conditions models, and software is developed and trained in.
The real world may present inputs and scenarios that are unclear and contradictory. In many B2B and B2C applications, this is a constant. There are ethical and dynamic regulatory guidelines to navigate, business risks to counter, and completely unexpected events.
Therefore, the most effective AI software is not the most autonomous but the most well-orchestrated system.
The Role of AI Software Development Teams
Software is typically built and deployed by a mix of “brains.” These professionals engage in systems thinking and cross-disciplinary collaboration to create AI solutions. For instance, at createIT, the team comprises over 130 specialists that go over and beyond to provide teams and solopreneurs with the digital products they need to scale.
Human Skill Profiles in AI Software
1. Domain Experts
The domain expert is responsible for validating the software’s outputs against real-world knowledge. They ensure that decisions and actions make sense beyond statistical correlation. Domain experts define acceptable boundaries and exceptions within the system. They also dictate scenarios that require special handling, manual review, and alternative logic.
2. Business Owner
The role of the business owner is to align the AI software with the intended business outcomes. It involves making clear definitions for how AI supports goals and when and where automation starts and ends.
3. Operations and Risk Manager
After software deployment, the focus shifts from capability to reliability. Operations and risk managers oversee system behavior in action to ensure compliance with expected function(s). They perform something of a quality check by monitoring for anomalies, reduction in performance, unexpected patterns, and external changes that may affect outcomes.
When output and behavior deviate from the expected, the risk manager then steps in to handle incidents and failures. This requires extensive logging and alerting.
4. Compliance Reviewer
Navigating regulations and ethical frameworks can be difficult with AI systems. This is even more taxing in industries that handle sensitive data and high-impact decisions. The reviewer ensures that AI’s decisions are traceable, explainable, and logged for both regulators and internal governance.
5. UX Designer
The UX designer converts the functionalities of an AI software into clear, simple workflows. Features and functions are translated into designs that present recommendations and how confidence and uncertainty are communicated.
Human Skill Layer in B2B and B2C AI Systems
B2B Systems
- Risk tolerance threshold is adjusted to ensure that decisions with significant impact are escalated to human experts before being executed. Typically, this applies to operational, financial, or legal decisions.
- There are stronger audit and control requirements for traceability, accountability, and regulatory compliance.
- AI systems are designed to have multi-role supervision for better efficiency.
B2C Systems
- A strong emphasis on clear user experience to boost confidence and reliability in the software.
- AI software in B2C is designed to have faster feedback loops so human teams can quickly assess system behavior and refine and iterate if needed.
The Bottom Line
Building and deploying AI software solutions succeed when human skill forms a solid part of the architecture. The best AI software solutions are an effective blend of AI autonomy and human supervision. They are more resilient, adaptable, and scalable over time, and will prove to be worth the investment of time and resources.
FAQs
1. What Are the 7 Layers of AI Model Architecture?
These are the data, data processing, feature engineering, modeling, evaluation, deployment, and monitoring layers. Each layer focuses on a key aspect of building and using AI software systems.
2. What Skills Are Required for an AI Architect?
Usually, AI architects need to be knowledgeable and skilled in machine learning & AI algorithms, data analytics, cloud computing, programming, problem solving, project management, team collaboration, and critical thinking.
3. What is the 30% Rule in AI?
The 30% Rule refers to the idea that about 30% of AI project effort should come from human expertise. They should guide, validate, and manage AI outputs to increase accuracy and efficiency, especially when confronted with uncertainty and ethical concerns. The rule emphasizes that successful AI systems combine automation with human judgment.
4. What Are the Three Layers of AI?
- Cognitive Layer: This is where AI simulates human thinking, reasoning, and decision-making.
- Human Skill Layer: Here, professionals provide guidance, judgment, and oversight in morally grey areas and ambiguous scenarios.
- Automation Layer: Human supervisors hand off and allow autonomous task execution, reasoning, and decision making.
