Artificial intelligence (AI) and generative AI (GenAI) are becoming increasingly essential for organizations seeking to build applications that are fast, reliable, interactive in real time, and context-aware. Yet, some organizations are falling behind as they are approaching adoption too cautiously or encountering challenges with implementation.
최근 MIT report found that 95% of companies’ generative AI implementation is falling short. The research found that it was based on the learning gap for tools and organizations, not the quality of the AI models themselves. AI tools like ChatGPT work well for individual employees, but stall in enterprise use since they don’t learn from or adapt to workflows.
The human role in guiding AI
Some of the underlying hesitancy for AI adoption is the fear that it will replace human jobs. While this may be true for some roles, the current state of knowledge work is more of a dynamic shift vs an outright replacement of jobs. Humans used to be solely focused on producing, and now AI has taken on that role. Humans are finding themselves playing more of a supportive role: feeding data to AI engines, reviewing their work, and then providing additional context or adjusting prompts to improve the outputs.
While humans are needing to adapt to this dynamic shift, they are still playing a crucial role in AI workflows. Humans take on the role of an “AI Conductor,” a professional who orchestrates AI agents with intention and creativity. Instead of cleaning up for the AI agents, they are guiding it through the lens of industry expertise and personal experience. Instead of fearing AI as a competitor, it should be viewed as a tool waiting to be directed.
-
- Related post: 카우치베이스가 구현하는 AI 기반 애플리케이션의 미래
Humans in the middle of AI workflows
Companies are hesitant to rely on AI for critical decisions due to concerns about the accuracy, transparency, and accountability of AI-driven outcomes. “Human in the middle” is one way to alleviate these potential concerns. The concept of “human in the middle” refers to workflows where AI systems and humans collaborate in a structured way, with humans actively involved in key points of decision-making, oversight, or refinement rather than letting the AI operate fully autonomously.
While the AI is producing results, humans intervene to evaluate and correct before the final output. This human input helps improve the AI agent over time. This “decision gate” mechanism helps ensure that AI workflows remain safe, ethical, and adaptive by weaving human judgment directly into the process. Humans retain control and accountability, while AI boosts efficiency by providing outputs quickly.
There are three primary ways for humans to engage in AI workflows:
-
- Humans write prompts or give instructions for AI to act on. This is most common, as chatbots assist humans with daily tasks. AI is not replacing the human’s work, it is being used for collaboration and to enhance it.
- AI turns over the workflow to humans at key moments. Checkpoints are built in where humans become involved
- Humans review AI’s final work to verify accuracy, especially for sensitive workflows
Benefits vs challenges
There are multiple benefits of humans remaining involved in AI workflows. First, catching and correcting any errors before they can have a negative impact. This is crucial in high-stakes fields such as healthcare, finance, or law. Humans can also help spot biases, ensure outputs are transparent and trustworthy, and provide feedback to help fine-tune models as AI systems continuously learn. By having humans guardrail AI agents, it ensures they stay in their lane of expertise rather than blurring lines into other areas.
However, there are some challenges to humans remaining involved. Having humans in the middle of workflows can be a barrier to AI acceleration. Scalability and efficiency are decreased, rather than utilizing fully autonomous agents. There are also increased costs for training and staffing, and balancing the complexity of figuring out when the humans should be involved.
-
- Related post: 에이전트 AI 워크플로 평가
Roles that aren’t ready for AI automation
Another consideration when it comes to AI is not just whether a job can be automated, but whether humans want them to be. Where humans clearly see the value in AI taking on logistics-heavy or technical roles, they are more resistant to roles where trust, empathy, accountability, and moral judgment are deeply valued.
Some examples include:
-
- Healthcare/relational roles – Doctors, nurses, therapists, psychologists, hospice aides
- Educational roles – Teachers, childcare workers, coaches, mentors
- Justice/leadership roles – Judges, lawyers, police officers, religious leaders, politicians
- Creative roles – Artists, journalists
Workflows with a “human in the middle”
While some roles still aren’t ready to be fully reliant on AI, there are many areas today where AI can do the heavy lifting before a human steps in at the opportune time.
Content creation, moderation, document review: AI can filter spam and harmful posts/comments, write outlines, summarize long text documents, transcribe videos, and create images. Then humans can check for accuracy, refine tone, and ensure brand consistency.
고객 지원: This is one of the most common AI use cases we are seeing today. Chatbots can provide customer service for routine questions and provide links to further materials. Humans step in for more complicated and unique issues, or if a customer becomes unruly.
Recruiting and hiring: AI can scan resumes and quickly review applications, then rank candidates based on certain hiring criteria. Then a human hiring manager verifies the list, checks for any bias, and determines the final interview decisions.
Nutrition and fitness: AI can provide a workout and nutrition plan based on goals and body composition. A personal trainer ensures that exercises are being performed with correct form and provide motivation. As the client’s fitness journey progresses, the trainer can make modifications to the original plan as needed.
Financial trading: AI models can suggest certain stock trades based on underlying market conditions. Human financial advisors take a customer’s whole financial picture into consideration, and execute the final trades.
Medical imaging: AI can scan X-rays and MRIs to check for tumors or other potential red flags. A human radiologist confirms the findings and writes the official report.
-
- Related post: Building a Path to Edge AI for Vector Search
Workflow elements of “human in the middle” roles
These “human in the middle” scenarios consist of the following workflow elements:
-
- Raw Input (Data/Task) → The source material (scans, resumes, transactions, documents, etc.).
- AI Processing (Analysis/Prediction) → AI performs classification, ranking, detection, or generation.
- Human Review (Oversight/Correction) → A human expert checks AI outputs for quality, bias, or context.
- Decision Point (Approval or Override) → A formal “gate” where the human decides whether the process continues or stops.
- Final Output (Action/Decision) → The outcome (diagnosis, hire, trade, report, moderation, etc.).
- Feedback Loop (Model Improvement) → Human corrections are fed back into the AI system to retrain and improve performance.
Couchbase provides the platform for an AI-driven world
Data is the lifeblood for AI model training. The more current, clean, and reliable data that can be provided to LLMs, the more trustworthy the responses will be. And the more data that you give AI models, the more precise they become. Interactions with LLMs are text-based, thus the best way to exchange data with LLMs is JSON. Couchbase delivers flexible data modeling with a JSON-based architecture that adapts as application needs evolve.
By uniting transactional, analytical, mobile and AI workloads into a seamless, fully managed solution, Couchbase empowers developers and enterprises to build and scale applications and AI agents with confidence – delivering exceptional performance, scalability and cost-efficiency from cloud to edge and everything in between.
Humans still provide unique value to workflows that AI can’t fully replace
At this point, humans won’t be fully replaced by autonomous AI agents anytime soon. There are still invaluable qualities that humans bring that AI is unable to replicate. Humans are needed for ethical judgement, creativity, empathy, authenticity, and life experience. When content is all AI generated, everything sounds the same and your brand’s voice gets lost in the echo chamber. People connect with other people – through experience, relationships, and building trust. Hearing directly in the customer’s voice provides proof and validation that AI can’t replace. Overall it’s evident that AI has a crucial role in workflows, but having a human in the middle is most often the right decision.
For more, explore the new AI Services in Capella.
