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AI-Human Partnering Options
Strategic workforce restructuring based on AI automation continues to gather pace. Even though organisations leveraging AI for augmentation achieve 3x higher revenue growth per employee, it’s the redundancy stories that make news and frame perceptions.
- Microsoft eliminated approximately 9,000 roles (4% of its global workforce) in July 2025 as part of a broader shift toward AI and cloud infrastructure. CEO Satya Nadella confirmed that around 30% of the company’s code is now written by AI.
- Salesforce reduced its customer support headcount from 9,000 to 5,000 employees through the implementation of agentic AI agents, according to CEO Marc Benioff
- IBM’s CEO announced 200 HR employees were eliminated and replaced with AI chatbots. The company plans to gradually replace around 30% of its back-office roles with AI within five years, equating to approximately 7,800 positions.
- UPS laid off 20,000 workers, with CEO Carol Tomé explaining that machine learning technologies enabled these cuts.
Without being part of the decision-making process, it’s hard to say whether these examples were driven by a traditional cost cutting mindset or were the result of structured assessment about where their use cases sit in an AI-Human partnership model.
However, one thing is clear. These high-profile announcements from CEOs are intentional. The messaging is designed to set executive expectations around what it means to run organisations in an AI first world.
Maybe they believe this helps sell their own AI solutions. But there may be other motivators as well.
Tech CEOs witness disruption before the rest of us. In this respect, something that’s impressed and frightened them is that AI native start-ups have radically different revenue per employee profiles to their own.
As a result, CEOs running traditional, people heavy organisations need to show markets that they understand this threat and are responding to it.
Just how long it would actually take an army of AI start-ups to eat Microsoft’s lunch is not the point. Every CEO needs a credible ‘moat’ story to explain why their organisation’s ability to grow and be profitable remains safe and is still worth investing in.
Slimming down the workforce and boosting productivity is about modelling how these new AI start-ups work.
It’s also why SaaS companies had a heart murmur as they digested the implications of agentic AI. And why LinkedIn is full of stories about the demise of big consultancies and analyst firms: a story that is highly relevant to the focus of this article.
LinkedIn couch critics believe having access to an LLM in deep research mode means anyone is now able to replicate market intelligence reports and strategic recommendations. Therefore they conclude those firms have lost their moat. Not so. They confuse this with the accrued tacit knowledge of the people in these firms: the source of where value sits. LLMs offer next to nothing in this respect.
This is a costly mistake.
Sack someone and their tacit knowledge is lost. Because tacit knowledge right now is too hard to codify, capture and use in AI form. Whereas workflow can be codifed and automated . It’s why agentic AI exists.
It’s contextual awareness that distinguishes human intelligence and creates a moat for humans in a world of encroaching AI.
On this point, customer contact and contact centres in particular are once again caught in the crosshairs of conflicting perceptions.
They are frequently quoted as being one of the most vulnerable to AI replacement.
For instance, the latest data from Metrigy’s global AI study of 697 companies reports 37% of companies implementing AI in their contact centres have laid off an average of 24% of their employees.
Within that cohort, early career employees are especially vulnerable according to detailed analysis of recruitment data by Stanford University’s Digital Economy Lab. I did a deep dive on the topic in a recent article exploring how to rebuild the first rung in career ladders.
But guess what?
Those in operational command of contact centres have a different view on replacement. Presumably based on lived experience. According to Contact Babel, less than 20% see the impact of AI as replacing people. The overwhelming majority favour augmentation.
Even so, politics may still trump experience.
Vendors know they need to have a convincing, short-term ROI to secure deals.
It’s how they sold omni-channel. Chat was three times cheaper than voice. This was followed with the news that messaging was now even better! Being seven times cheaper than voice.
Preposterous headlines that deliberately ignored any wider context. But it worked in terms of sales and set the scene for the current dysfunctional generation of omni-channel implementations.
The same logic could be resurrected for selling AI. Especially once LLM inference starts being fully priced into deals rather than subsidised to kickstart demand. At that point, purchasing managers will start to scrutinise the numbers much more closely.
In terms of pushing through a business case, it is so much easier to reference the immediate impact of one less salary than trying to price the evolution needed to augment front line roles with AI. One needs change management, the other just a P45.
In the short term, this might work. But it sets a certain culture in play which is hard to reverse out of later on. The cultural drumbeat is that people are needed until AI proves otherwise.
This is how it manifests.
A directive path is where the AI takes over routine tasks completely and outputs final decisions or responses with little human input. When this is the design priority, there is a higher chance that entry-level roles can be replaced or made redundant by the technology rather than being supported or developed.
Organisations using directive AI for contact centre work and other high-exposure occupations face a shrinking entry-level talent pipeline.
A better way to future-proof talent strategies is to adopt a framework of human-AI partnering and accelerate development of tacit knowledge rather than automate away the roles where new talent learns and grows.
For sure, it is a more complex business case. And the strategy requires alignment across teams.
For instance, have augmentative paths been made explicit in the needs analysis of your current AI workflow initiative? This remains rare.
To help crystalise what this means in practice, have you devoted time to think through the redesign of roles to optimise AI-Human partnering? For reference, I covered the topics that need workshops and workstreams in this post.
If you are alert to this issue because you are at the forefront of using AI to evolve your operating model, please make contact with Brainfood to explore how we can help you structure your own redesign.
Meanwhile, here’s a taster of one of the orientation modules we offer. It’s designed to raise awareness around what’s distinctive about human intelligence in relation to what we attribute to artificial intelligence.
If you think about it, you can’t produce an effective AI-Human partnering strategy without a clear understanding of both. See here for gaining clarity on what makes AI unique.
The Human Sensory “Moat”
Humans possess up to thirty-three different sensory systems beyond the traditional five senses depending how experts categorise them. These create a protective “moat” around uniquely human value that AI cannot breach.
Here’s some foundation understanding and language that will help articulate human value when debating AI-Human partnering
Proprioception & Interoception: The Internal Intelligence
- Proprioception enables spatial awareness and complex motor planning.
- Interoception detects internal signals including emotions, stress, and intuitive insights.
- Business Impact: Enables reading customer emotional states, sensing team dynamics, making gut-level strategic decisions.
Contextual & Emotional Intelligence
- Humans process subtle environmental cues, body language, and emotional undercurrents.
- We integrate multiple sensory inputs to understand what’s not being said.
- Business Impact: Critical for sales negotiations, crisis management, stakeholder relationships
Creative Problem-Solving Through Embodied Experience
- Human creativity emerges from physical, emotional, and sensory experience.
- AI can recombine existing patterns but cannot generate truly novel insights from lived experience.
- Business Impact: Innovation, breakthrough thinking, adaptive solutions to unprecedented challenges.
This rich network of senses working together gives humans a range of abilities that current AI systems simply cannot match.
It is why we can “read between the lines” in conversations, understand context that isn’t explicitly stated, produce truly creative solutions by combining insights from different senses, and navigate complex social situations by picking up on emotional undercurrents that no computer can detect.
Recent research reveals a fundamental disconnect between AI and human understanding. When scientists compared how large language models process sensory concepts versus how humans do, they discovered virtually no correlation across all six sensory experiences: sight, sound, touch, smell, taste, and internal body sensations.
This finding suggests AI systems cannot truly replicate the rich, embodied understanding that comes from actually experiencing the world through our senses. While humans build their knowledge through direct physical experiences and the complex way our nervous system integrates all these sensory inputs, AI systems like ChatGPT learn by identifying statistical patterns in massive amounts of text data.
Essentially, AI systems are learning about concepts second hand through descriptions, while humans learn through first hand sensory experience. This creates a fundamental difference in how each processes and understands the world around us
This means:
- AI cannot genuinely understand context that emerges from embodied experience.
- AI cannot replicate emotional intelligence that comes from interoceptive awareness.
- AI cannot generate breakthrough innovation that requires multisensory integration.
- AI cannot build authentic relationships requiring genuine empathy and social cognition.
In contrast humans excel at:
- Real-time adaptation to unprecedented situations
- Creative problem-solving when established patterns fail.
- Ethical reasoning in ambiguous situations
- Relationship navigation through complex social and emotional terrain
AI is incredibly good at processing information and following patterns, but humans are like sophisticated sensors that can feel, sense, and understand the world in ways that go far beyond just data processing.
We can sense when something “feels wrong” even if we can’t explain why, we can be creative by combining ideas in ways that emerge from our lived, physical experience, and we can connect with other people on emotional and social levels that require genuine understanding, not just clever responses.
Hopefully, this helps refine your own understanding of the sensory foundations on which a human ‘moat’ can be explained and justified.
Even so, how does this translate into boardroom priorities and cause cost conscious CEOs to think twice?
The Strategic Risk of Pure Automation
Automation-first strategies create dangerous vulnerabilities such as:
- Competitive Commoditisation: If everyone automates the same processes, you lose differentiation.
- Innovation Stagnation: Pure automation replicates existing approaches rather than creating breakthrough solutions.
- Customer Relationship Erosion: Automated interactions cannot build the trust and loyalty that drive premium pricing.
- Organisational Fragility: Over-automated systems lack the adaptive resilience that humans provide.
- Talent Flight: Your best people leave for organisations that value human contribution, taking institutional knowledge with them.
Summary and Take-Aways
Pure automation is a race to the bottom. It reduces costs but also eliminates the human capabilities that create sustainable competitive advantage, premium pricing, and genuine innovation.
Augmentation strategies generate higher returns because they leverage both AI efficiency and irreplaceable human capabilities. Organisations that will dominate the AI-first future are those that recognise human sensory intelligence, creativity, and relationship-building as their ultimate competitive moat. Capabilities that become more valuable, not less, as AI handles routine work.
The choice isn’t between human costs and AI efficiency. It’s between commoditised automation that anyone can replicate and differentiated human-AI partnership that creates sustainable competitive advantage. The sensory intelligence moat ensures that humans won’t just remain relevant, they’ll become the defining factor in organisational success providing their value is repositioned and developed.
Brainfood has the facilitation and toolkit to get you there.
