Humans and Machines: Building Learning Cultures in the Age of AI
11 | 2025 Jari Huilla, CTO & partner Kipinä
As we’re heading into 2026, businesses must prepare for a future where humans and machines collaborate rather than compete. In the rapidly evolving landscape of technology, we are forced to ask ourselves a difficult question: with AI becoming exponentially better, why should humans keep learning at all?
“The answer lies not in competing with machines on computation, but in doubling down on what makes us human.”
The Shift from "Know-it-all" to "Learn-it-all"
For decades, professional value was often determined by how much you knew. However, leading tech giants have already recognized that static knowledge is becoming less relevant. Laszlo Bock, the former SVP of People Operations at Google, has noted that role-related knowledge is often the least important attribute they screen for. Instead, success is predicted by a combination of raw intelligence and - crucially - learning ability.
The mindset must shift from being a "know-it-all" to a "learn-it-all". As NVIDIA CEO Jensen Huang famously stated, "You’re not gonna lose your job to AI. You’re gonna lose your job to somebody who uses AI". Therefore, "learning to learn" is the most important skill for the next generation.
Overcoming the "Hidden Penalty" of AI
Integrating AI isn't just a technical challenge; it is a social one. Research indicates there is a "hidden penalty" to using AI at work: perceived competence is rated 9% lower on average for those who use AI assistance, with a higher penalty for females (13%) than males (6%).
If employees feel they will be judged as "lazy" or "incompetent" for using these tools, adoption will stall. To make the "human + machine" collaboration socially acceptable, leaders must actively dismantle these barriers.
To make the "human + machine" collaboration socially acceptable and effective, organizations must:
Increase AI literacy
Redesign evaluation systems so employees aren't penalized for efficiency
Encourage psychological safety and openness
Pay attention to inequalities that arise from AI adoption
Keep building on human strengths
Identifying AI-Proof Skills
So, which skills should we build? We can identify "AI-proof" skills using two main tools:
Tool #1: Things That Can’t be Added After the Fact
There are critical tasks where we simply cannot rely on AI because the cost of error is too high - situations where things cannot be fixed "after the s**t has hit the fan". These areas include:
Cybersecurity and data protection
Governance, work safety, and decision auditability
Ethical judgment
Tool #2: The Human Touch
We must cultivate skills where the bottleneck has never been the production of text or code, but rather the human connection. These include:
Social & people skills: empathy, emotional intelligence, communication, networking…
Taste & a unique point of view: Having a unique point of view and the creativity to guide the machine
Combinations of skills, such as mixing emotional intelligence with domain expertise and adaptability.
Steering development: in software, for example, writing code is rarely the bottleneck - figuring out what to build is
How Leaders Can Drive the Transformation
In the AI age, it is the leadership’s responsibility to foster adaptability, learning, ideation and change. To design environments where humans and machines learn together, leaders should follow three steps:
Enablement: Focus heavily on increasing AI literacy across the organization.
Communicate Effectively but Humanely: Avoid the extremes of "everything will stay the same" or "we are firing everyone." The middle ground is the truth: "We’re going on a journey. We don’t know where this is going to take us and we need your help. We want you to have an active role in this journey".
Let People Plan Their Own Transition: Help employees identify their skill gaps, align those gaps with AI tools, and involve them in the process.
How Do These Skills Materialize at Kipinä?
At Kipinä, we have a Senior Tech Leadership model where each of our senior-level experts serves a both a coach and a top team player, acting as a catalyst for more effective digital development. This way of serving our clients was created already before the current AI boom, but AI has only increased the necessity for people skills and ”taste” in digital development.
The way we see it, this way of mentoring and amplifying others while protecting deep skills will continue to be our ”unfair advantage”.
The Bottom Line
As we integrate these powerful tools into our workflow, we must remember the limitations of the machine. AI cannot care. AI cannot mentor. AI cannot grow wiser.
That is still on us.
Looking for an inspiring keynote on AI & How to amplify human learning?
Jari Huilla’s celebrated and thought-provoking CTO point of view on competative advantage from human learning cultures is great for audiences from digital and business development to HR and culture
Jari Huilla, CTO & partner Kipinä
Jari Huilla on Kipinän tuore CTO, jolla on poikkeuksellisen pitkä ja monipuolinen tausta teknologian parissa; ensimmäinen työpaikka löytyi jo 15-vuotiaana Nokia Research Centeriltä. Vuosien varrella hän on ehtinyt toimia kehittäjänä, johtajana ja kasvuyritysten rakentajana. Kipinällä Jari tuo yhteen teknisen syväosaamisen ja liiketoimintalähtöisen ajattelun. Häntä kiinnostaa erityisesti, miten tekoälyratkaisuista tehdään paitsi teknisesti toimivia myös aidosti tarkoituksenmukaisia – ja miten niiden rajoitteet ymmärretään, ei vain ohiteta.