Defining HumanAI

A HumanAI approach recognises that artificial intelligence is a catalyst for human growth, not a replacement for human judgment or creativity. It positions AI as a tool that augments human capabilities while keeping people at the centre of the transformation. Key principles include:

1. Experiential journey: AI adoption is more than a technological upgrade; it is a learning journey that reshapes how organisations work and how individuals think.

2. People first: Prioritising people ahead of AI facilitates adoption and change management. When organisations address employee needs and concerns, they adapt faster and unlock productivity.

3. Learning through success and failure: AI projects require experimentation. Organisations need a culture that learns from both breakthroughs and missteps.

4. Supportive ecosystem: People need appropriate models, services, resources and support—from training and onboarding to guidelines and ethical frameworks—to make the transition.

5. Community and collaboration: AI adoption is not a solitary endeavour. Communities of practice, cross‑disciplinary teams and peer networks foster encouragement and validation.

6. Vision and innovation: A bold vision inspires teams to see AI as a vehicle for innovation and to push beyond incremental improvements.

7. Human agency: Humans retain agency in AI‑enabled workplaces. They should be empowered to make decisions, shape their work and define how AI is used.

8. Growth opportunities: AI creates new roles and paths for career progression. Showing employees these opportunities increases engagement and openness to change.

9. Expanded capabilities and productivity: AI augments human capabilities, enabling people to perform more tasks, often more efficiently.

10. Trust, autonomy and empowerment: Empowering people with autonomy and establishing trust are critical to productive HumanAI systems.

11. Environmental evolution: The environments in which people work shape their responses to technology. Environmental changes—down to epigenetic influences—affect how individuals adapt, learn and thrive.

Learning through success and failure

AI’s “jagged technological frontier” means there are tasks AI performs well and tasks where it failsmitsloan.mit.edu. Research from MIT Sloan examined more than 700 consultants and found that using generative AI within its capabilities improved performance by 38 %–42.5 % compared with a control group, but misapplying AI outside its competency resulted in performance declines of 13 %–24 %mitsloan.mit.edu. The lesson is clear: AI projects require experimentation, learning from successes and failures and adjusting processes accordingly. Employees must develop “cognitive effort and expert judgement” when working with AI, rather than blindly trusting outputsmitsloan.mit.edu.

Creating a culture that embraces experimentation requires psychological safety. Organisations should encourage teams to pilot new workflows, analyze results and share what did and did not work. The Connecteam guide on employee empowerment notes that workers will inevitably make mistakes as they take on new responsibilities and that the best way forward is “learning and offering continuous feedback”connecteam.com. Providing space to learn from failure fosters resilience and innovation.

Models, services, resources and support

A successful HumanAI transition demands a supportive ecosystem of training, models and resources. The MIT study recommends an onboarding phase so workers can explore where AI excels and where it does not; it also suggests recognizing and rewarding peer trainers who help colleagues learn mitsloan.mit.edu. Effective onboarding teaches workers to verify AI outputs, understand limitations and integrate AI into their workflow.

HumanAI also requires multidisciplinary teams. At Stanford’s conference, Jodi Forlizzi emphasised that teams should comprise workers, managers, software designers and experts from domains such as medicine, law and environmental science hai.stanford.edu. Such diversity ensures that AI solutions consider ethical, legal and social dimensions. Experts must be “true partners from the start, rather than added near the end” hai.stanford.edu, preventing technologists from overlooking human factors.

Resources should also include ethical guidelines and human‑centred metrics. Saleema Amershi of Microsoft Research explained that current AI evaluations focus on accuracy, but HumanAI requires metrics that reflect what people need and valuehai.stanford.edu. Organizations need tools to measure user satisfaction, fairness, autonomy and societal impact, not just technical accuracy.

Community and collaboration

AI adoption is not a solitary journey. Workers learn best when they share insights and validate each other’s experiences. The Stanford panel encouraged embracing productive discomfort—inviting disagreements and conflicting perspectives to surface and resolving them collaborativelyhai.stanford.edu. Communities of practice help employees navigate new tools, share best practices and provide mutual encouragement.

Peer networks also mitigate the loneliness that can accompany rapid technological change. For example, the MIT study notes that some consultants acted like “centaurs”, dividing tasks between AI and human effort, while others behaved like “cyborgs”, integrating AI deeply into their workflowmitsloan.mit.edu. Discussing these experiences in a community setting helps workers reflect on their approach, learn from peers and avoid over‑reliance on AI.

Vision, innovation and human agency

A HumanAI strategy starts with a bold vision. Instead of applying AI to marginally improve existing processes, leaders should imagine how AI can unlock new possibilities. Yet the vision must respect the unique attributes that make us human.

Research by the University of South Australia shows that AI does not possess independent creativity; it follows human prompts and lacks original thoughtearth.com. Professor David Cropley explains that AI will take over routine, algorithmic tasks, freeing people to focus on unpredictable, non‑algorithmic and creative workearth.com. Dr Rebecca Marrone emphasises that AI excels at processing data quickly but lacks independent thought or emotion, and that its outputs depend on what “you” tell it to deliverearth.com. These findings highlight that AI is an amplifier of human creativity rather than a substitute.

Innovation arises when humans use AI as a tool to push the boundaries of their imagination. For example, generative models can produce draft ideas, which human designers refine; AI can summarise research and highlight patterns, allowing researchers to ask deeper questions. Such human‑AI co‑creation demands agency: individuals must decide when to rely on AI, when to override it and how to incorporate its outputs.

Opportunities for growth and expanded capabilities

One of the most compelling reasons to adopt a HumanAI strategy is its potential to expand human capabilities and boost productivity. The Nielsen Norman Group synthesised three empirical studies and found that generative AI tools increased business users’ throughput by 66 % on average during real‑world tasksnngroup.com. Support agents handled 13.8 % more customer inquiries per hour, business professionals wrote 59 % more documents per hour, and programmers completed 126 % more projectsnngroup.com. Importantly, these gains were statistically significantnngroup.com.

The same article notes that tasks requiring higher cognitive effort saw the greatest benefitsnngroup.com, suggesting that AI acts as a force multiplier for skilled work. Additionally, AI can expedite learning: in a longitudinal study of support agents, those using AI reached the productivity level of experienced agents four times faster—achieving the same performance in two months instead of eightnngroup.com. Accelerated learning means employees upskill faster and can take on more complex tasks sooner, expanding career opportunities.

These findings corroborate the MIT study’s evidence that AI can improve performance by nearly 40 % when used correctlymitsloan.mit.edu. Together, the data show that AI is not just a cost‑saving tool but a driver of growth. When integrated thoughtfully, AI allows employees to focus on high‑value work, speeds up idea generation and reduces time spent on mundane tasks.

Trust, autonomy and empowerment

For HumanAI to thrive, employees must be empowered to make decisions and trust must flow both ways. Employee empowerment, according to Connecteam, gives workers authority, autonomy and permission to decide how they complete parts of their jobsconnecteam.com. Such empowerment means trusting employees to make some decisions without managerial approval, allowing them to set goals and giving them a voice in company decisionsconnecteam.com. Empowerment improves engagement and satisfaction, leading to companies that are 21 % more profitable and employees who report 26 % higher satisfactionconnecteam.com.

Empowered employees are more likely to trust leadershipconnecteam.com. Trust fosters a culture where people feel safe to use AI tools, share feedback and admit when AI guidance seems wrong. Conversely, micromanagement undermines autonomy and discourages innovation. The article advises organisations to train both employees and leaders on delegation and decision‑makingconnecteam.com. Starting with small empowerment experiments and gradually expanding decision rights helps teams adjustconnecteam.com.

A culture of learning and feedback is central to empowermentconnecteam.com. Because AI is fallible, employees will sometimes make mistakes when following AI recommendations. Regular feedback loops enable workers to refine their judgment and help leaders understand where more guidance or training is needed. These practices mirror the MIT study’s call for onboarding and peer trainersmitsloan.mit.edu and emphasise that empowerment must be supported by resources.

Environmental evolution and epigenetics

HumanAI also acknowledges that people do not operate in a vacuum. The environment—physical, social and organisational—shapes how individuals respond to AI. Epigenetics research shows that environmental and lifestyle factors such as behaviour, stress, physical activity and working habits can influence epigenetic mechanisms like DNA methylation and histone modificationpmc.ncbi.nlm.nih.gov. These epigenetic changes are flexible genomic parameters that allow gene activity states to change under exogenous influencepmc.ncbi.nlm.nih.gov. In other words, the environment can modulate gene expression and thus behaviour and health.

As workplaces evolve with AI, they become new environments that will, over time, shape our biology. High stress, shift work, sedentary behaviour or toxic work cultures can affect epigenetic patterns, whereas environments that encourage physical activity, autonomy and positive social interactions can promote healthier epigenetic profilespmc.ncbi.nlm.nih.gov. A HumanAI strategy therefore considers not only technological and organisational changes but also well‑being and environmental factors. Designing human‑centric workplaces—spaces that support movement, mental health, and social connection—helps employees adapt to AI without adverse health effects.

Epigenetics also underscores the importance of adaptability. Genes do not rigidly determine outcomes; they interact with environmental signals. Similarly, AI does not dictate a fixed way of working; it offers tools that individuals can adapt. Recognising this interplay encourages organisations to provide flexible environments and to monitor how work practices influence employee health and performance.