The Psychology of Enterprise Adoption

Why Your Change Capability Will Determine Survival

78 million new jobs cannot save organisations that have not built change-literate teams.

The Uncomfortable Truth About What Is Coming

What separates organisations that thrive during massive tech disruption from those that collapse?

Not technology investment. Not strategic foresight. Not even leadership quality.

The answer is something most executives have never explicitly built: CHANGE LITERACY.

Consider the transformation ahead: 170 million new jobs will be created by 2030, whilst 92 million existing roles will be displaced (World Economic Forum, 2025). The quantum-AI convergence is driving a complete reshaping of how work gets done across every industry.

Your technology strategy matters. Your AI partnerships matter. Your quantum computing access matters.

But none of it matters if your people cannot adapt fast enough to capture the value.

The convergence of AI and quantum computing marks something unprecedented in business history. Quantum computing has shifted from theoretical exploration to practical experimentation, and in 2025, its influence on artificial intelligence is beginning to crystallise (Software House, 2025). We are witnessing quantum-enhanced AI unlocking possibilities that classical computing could never achieve, from faster optimisation algorithms to breakthroughs in materials science and drug discovery.

Most organisations are approaching this convergence like any other technological shift. Assess the technology, create an implementation plan, train people on features, communicate benefits, expect adoption.

But quantum-AI is different. And organisations that treat it like a standard technology rollout will find themselves among the 70% of transformations that fail to deliver promised benefits.

Why Your People Are Not Prepared for This

Whilst leadership teams are exploring quantum computing capabilities and AI workflows, the people who will actually determine success or failure are grappling with something much deeper than learning new tools.

They are asking fundamental questions:

• Will my expertise become obsolete overnight?
• How do I build skills for jobs that do not exist yet?
• Can I possibly keep pace with this rate of change?
• What happens when the technology knows more than I do?

Here is the reality that most executives are missing: these concerns are not irrational. Goldman Sachs research indicates that AI could disrupt 6-7% of the US workforce, though displacement is expected to be transitory as new opportunities emerge (Goldman Sachs, 2025). But that transition period creates significant friction.

The research also reveals something crucial: approximately 60% of US workers today are in occupations that did not exist in 1940, implying that more than 85% of employment growth has been driven by technology-driven job creation. The pattern is clear: technology creates more jobs than it destroys. But the transition is never smooth, and organisations without change capability suffer massive disruption whilst competitors thrive.

Where Traditional Approaches to Technological Change Fall Short

Most organisations are treating quantum-AI adoption like previous technology rollouts. Which typically means:

• Announce the strategic importance of quantum-AI capabilities
• Invest in infrastructure and partnerships
• Provide technical training on new tools and systems
• Communicate the business benefits
• Expect people to adapt quickly

But quantum-AI convergence challenges something much more fundamental than previous technologies.

Unlike earlier digital transformations that augmented human capabilities, think spreadsheets replacing calculators or email replacing memos, quantum-AI directly challenges human cognitive superiority. When quantum computing accelerates machine learning training from weeks to days (Software House, 2025), and generative AI handles 70% of customer interactions without human intervention (Nice, 2025), people experience something beyond workflow disruption. They experience identity disruption.

The Psychological Barriers Standard Training Cannot Address

Skills Obsolescence Anxiety: The World Economic Forum projects that 39% of key skills required in the job market will change by 2030 (World Economic Forum, 2025). This is not about learning a new software package. This is about fundamental expertise becoming less valuable whilst the definition of valuable skills shifts faster than most organisations can retrain people.

Pace Overwhelm: Quantum-AI development is accelerating exponentially. People who have built careers mastering specific domains are watching those domains transform monthly, not yearly. Traditional professional development cycles cannot keep pace, creating persistent anxiety about falling behind.

Purpose Displacement: When AI systems can perform analysis, generate solutions, and make recommendations faster and more accurately than humans, people struggle to articulate their unique value. What do humans do when machines do thinking work better?

Control Loss: Previous technological changes allowed humans to maintain oversight and intervention. Quantum-enhanced AI systems operate at speeds and complexity levels that make meaningful human oversight increasingly theoretical. This creates fundamental discomfort about accountability and agency.

Traditional change management approaches were not designed for these challenges. You cannot train away existential concerns about professional relevance. You cannot communicate away the reality that entire job categories will disappear whilst new ones emerge faster than people can retrain.

Some Organisations Are Getting This Right

Despite the complexity of quantum-AI convergence, some organisations are achieving remarkable adoption success rates. Not through better technology or bigger training budgets, but by acknowledging that quantum-AI adoption is fundamentally a change capability challenge, not a technology challenge.

What they are doing differently:

Building Change Capability Before Technology Arrives

Rather than waiting until AI implementation to address adoption, successful organisations are building change literacy as a core competency now.

A global pharmaceutical company facing post-merger integration challenges used structured change capability development to prepare teams for coming AI enhancements in drug discovery. By focusing on building people’s confidence in navigating uncertainty and learning new approaches, they created resilience that made subsequent technology adoption significantly smoother (Prosci, 2023).

The message was not “AI will revolutionise our R&D process.” The message was “we are building a culture where continuous adaptation is normal, supported, and celebrated.”

When the technology arrived, people were already comfortable with rapid learning cycles and collaborative problem-solving. Adoption happened organically rather than through mandated compliance.

Creating Peer Learning Networks That Scale Knowledge

Organisations succeeding with change adoption recognise that top-down training cannot match the pace of technological change. Instead, they establish networks where early adopters naturally share insights, challenges, and solutions with peers.

A major hospital system implementing AI-enhanced diagnostic tools across 11 hospitals created change champion networks that connected medical professionals experiencing similar adoption challenges. Rather than waiting for formal training updates, doctors and nurses shared practical workarounds, emotional support, and innovative use cases in real-time (Prosci, 2023).

Research from Prosci shows that organisations with excellent change management are seven times more likely to meet their objectives and 88% more likely to achieve their goals (Prosci, 2023).

The impact was remarkable: 75% of professionals who participated in peer learning networks became active advocates for AI adoption, compared to only 40% who received only standard training. People trust colleagues more than executives when evaluating whether new technology genuinely improves their work.

Reframing the Human-AI Relationship

The organisations thriving with AI do not position it as replacement technology. They position it as amplification technology that frees humans for higher-value work.

A global logistics company implementing AI-enhanced route optimisation did not announce “AI will handle route planning so you can focus on customer relationships.” They involved route planners in defining what “higher-value work” meant to them, co-creating new role definitions that emphasised strategic problem-solving, customer intimacy, and innovation rather than routine optimisation (Software House, 2025).

The result: route planners who had initially resisted AI adoption became its strongest advocates, achieving a 15% reduction in fuel costs whilst simultaneously improving customer satisfaction through more personalised service. When people help define their future roles, they own the change rather than resist it.

The Change Literacy Advantage

Organisations that successfully navigate AI share one critical characteristic: they have built change-literate teams.

Change literacy is not just about accepting change or being “flexible.” It is about developing the organisational capability to thrive in an environment of continuous, accelerating transformation.

Key Capabilities of Change-Literate Teams

1. Comfortable with Continuous Learning

Change-literate teams treat learning as ongoing work, not occasional training. They build time and systems for continuous skill development, peer teaching, and experimentation. When 39% of required skills change every five years (World Economic Forum, 2025), organisations cannot rely on annual training cycles.

2. Psychologically Safe to Experiment

Teams that thrive during change adoption create environments where people can test new approaches, admit confusion, and share failures without fear. When technology evolves faster than expertise can solidify, psychological safety becomes the foundation for rapid adaptation.

3. Collaborative Problem-Solvers

Change-literate teams instinctively collaborate across functions and hierarchies to solve emerging challenges. They recognise that no single person understands the full implications of new disruptive technologies, so collective intelligence becomes essential.

4. Resilient Through Uncertainty

Rather than needing complete clarity before moving forward, change-literate teams operate effectively in ambiguity. They make decisions with incomplete information, adjust quickly when assumptions prove wrong, and maintain momentum despite uncertainty.

5. Focused on Value Creation

Change-literate teams stay anchored in customer and business outcomes rather than process compliance. They continually ask “what value does this create?” rather than “what are we supposed to do?”

The Practical Steps That Make the Difference

Building change literacy is not abstract. Here are the specific actions that successful organisations take:

1. Create Dedicated Learning Time

Designate 10-15% of work time specifically for learning, experimentation, and peer teaching. Make this protected time, not something that disappears during busy periods. When learning is optional, it never happens. When it is structured into workflows, it becomes cultural.

2. Establish Cross-Functional Learning Cohorts

Form small groups (8-12 people) from different functions who meet regularly to share what they are learning about new technologies, challenges they are facing, and solutions they are discovering. These cohorts create accountability, support, and knowledge sharing that formal training cannot match.

3. Reward Productive Failure

Publicly celebrate experiments that failed but generated valuable learning. When people see that intelligent failure is rewarded rather than punished, they take the risks necessary for innovation. AI adoption requires experimentation, and experimentation requires safety to fail.

4. Connect Technology to Customer Value

Help people trace direct lines from new capabilities to improved customer experiences and business outcomes. When a route planner sees how new optimisations create happier customers and more profitable routes, adoption becomes personally meaningful rather than abstractly mandated.

5. Build Change Literacy Into Performance Expectations

Include change capability metrics in performance reviews: willingness to learn, ability to adapt, contribution to peer learning, experimentation mindset. What gets measured gets attention. When change literacy affects advancement, people invest in developing it.

The Competitive Advantage of Change-Literate Organisations

Organisations with change-literate teams do not just handle change better. They handle all transformation better.

When the next major technology shift arrives after AI, they adapt faster. When customer expectations evolve, they pivot more effectively. When regulatory requirements change, they comply more smoothly. When competitive threats emerge, they respond more decisively.

The reality is: AI is not the last major change your organisation will face. It is just the beginning of an era of continuous, accelerating transformation.

The companies investing in building change literacy today are not just preparing for AI. They are building the organisational capability that will determine their long-term survival in an increasingly dynamic world.

Research from Goldman Sachs reveals that generative AI will raise labour productivity in developed markets by around 15% when fully adopted (Goldman Sachs, 2025). But that productivity gain only materialises in organisations where people successfully adopt and optimise the technology. Change capability directly translates to competitive advantage.

Three Immediate Actions You Can Take

The research is clear: organisations that invest in change capability before technology arrives achieve significantly higher adoption success. You do not need to wait for a perfect strategy or comprehensive programme.

1. Conduct a Change Literacy Audit

Assess your organisation’s current change capability honestly. How do people typically respond when significant changes are announced? How quickly do new practices become embedded? What percentage of people actively help others adopt new approaches versus passively complying or actively resisting?

Ask these specific questions across a representative sample of employees:

• How confident do you feel learning new technologies or processes?
• When was the last time you helped a colleague navigate a change?
• Do you have time protected for learning and experimentation?
• Can you explain how recent changes connect to customer value?

The answers will reveal whether you are building change literacy or just managing compliance.

2. Identify and Empower Your Natural Change Champions

Every organisation has people who naturally embrace new approaches and help others adapt. These individuals are invaluable during change adoption but often go unrecognised and unsupported.

Identify 15-20 people across different functions and levels who demonstrate:

• Enthusiasm for learning new approaches
• Ability to explain complex concepts simply
• Credibility with peers
• Willingness to experiment and share results

Give these champions dedicated time (4-6 hours per month) to explore new technologies relevant to their domains, then share insights with peers. Research shows that peer-driven adoption achieves 2-3 times higher success rates than top-down mandates (Prosci, 2023).

3. Start Building Learning Infrastructure Now

Do not wait until the next technology implementation to create learning systems. Establish structures today that will support continuous adaptation:

• Create protected learning time in team schedules
• Set up regular knowledge-sharing sessions where people teach peers
• Build psychological safety by celebrating productive failures
• Connect all changes to customer and business value

The goal is not to become change experts overnight, that is impossible and unnecessary. Your goal should be building teams that can navigate uncertainty confidently, transforming anxiety into excitement about new possibilities.

This Problem Is Accelerating, Not Slowing

Quantum-AI convergence is not the last major change organisations will face. It is one of many. The pace of technological innovation is accelerating, not slowing down.

Quantum computing is moving from the NISQ (Noisy Intermediate-Scale Quantum) era toward practical utility, with early commercial deployments already beginning (Software House, 2025). By the early 2030s, hybrid quantum-classical systems will become mainstream in enterprise AI workflows. Companies will use quantum processors for the most computationally demanding tasks whilst classical systems handle less intensive operations.

Most organisations are still approaching technological change like a series of discrete projects. Implement ERP, then stabilise. Roll out CRM, then stabilise. Deploy AI tools, then stabilise.

But the research shows us something different: the organisations succeeding with AI understand it is fundamentally a change capability challenge, not a technology challenge.

Building change literacy takes time, but the alternative is watching your transformation investments fail whilst your people struggle with each new wave of change. The companies that start building this capability now will find every future transformation easier, faster, and more successful.

This is exactly the challenge M1 is designed to solve. We specialise in change adoption, helping organisations ensure their transformations actually deliver the promised business benefits rather than joining the 70% that fail. We understand that lasting change happens when everyone moves as one, especially when it comes to AI where collaboration and capability determine success.

Ready to Build Teams That Thrive Through Disruption?

The quantum-AI convergence will separate organisations that survive from those that thrive. The difference will not be technology investment. It will be change capability.

Book a 30-minute strategy conversation where we can discuss your change capability challenges and share practical insights about what successful organisations do differently. No sales pitch, just an honest conversation about building change-literate teams that turn disruption into competitive advantage.

Book your strategy call here or email hello@wearem1.com

References

1. Goldman Sachs (2025). How Will AI Affect the Global Workforce?https://www.goldmansachs.com/insights/articles/how-will-ai-affect-the-global-workforce/
2. Nice (2025). Top AI CX Trends for 2025: How Artificial Intelligence is Transforming Customer Experience. https://www.nice.com/info/top-ai-cx-trends-for-2025-how-artificial-intelligence-is-transforming-customer-experience
3. Prosci (2023). What We Mean by Organisational Change Management.https://www.prosci.com/blog/what-we-mean-by-organizational-change-management
4. Software House (2025). How Quantum Computing Will Influence AI Development in 2025: Breakthroughs, Challenges, and Real-World Applications. https://softwarehouse.au/blog/how-quantum-computing-will-influence-ai-development-in-2025-breakthroughs-challenges-and-real-world-applications/
5. World Economic Forum (2025). Future of Jobs Report 2025: The Jobs of the Future – and the Skills You Need to Get Them. https://www.weforum.org/stories/2025/01/future-of-jobs-report-2025-jobs-of-the-future-and-the-skills-you-need-to-get-them/