The Human Side of Automation

TL;DR: Automation is changing work quickly, but the winners keep people at the center by pairing AI with strong upskilling, job crafting, and internal mobility to boost agility, innovation, and retention.

• Scale of change: About 23% of jobs will change by 2027 (around 69M created, 83M eliminated). By 2030: 170M new roles, 92M displaced, which means +78M net jobs overall.
• Skills reset: About 39% of core skills will change by 2030. 59% of workers need reskilling and 11% risk getting none.
• What grows: Tech skills (AI, data, cybersecurity, digital fluency) and human skills (analytical and creative thinking, resilience, leadership, curiosity). The edge is the blend.
• Job crafting (task, relational, cognitive): Employees redesign roles around strengths. AI removes routine work so people do higher-impact tasks.
• Role shifts (examples): Financial analyst becomes strategy translator as AI does consolidation. Developer becomes system orchestrator guiding AI coders and tests. Support agent becomes high-touch problem solver while bots do Tier 1.
• Proof it works: IKEA: Bot handles about half of queries. 8,500 staff upskilled into remote design advisors, creating about €1.3B new revenue line with no layoffs. The Hartford: Automated simple claims. Adjusters redeployed to complex work, creating more throughput with no headcount cuts. Schneider Electric: AI talent marketplace unlocked 127k hours in weeks. 360k+ hours and about $15M in savings over time with higher retention.
• Career pivot: Move from executor to orchestrator (define problems, direct AI, integrate workflows). Build this through hands-on AI, purposeful networking, and sharper personal branding.
• Leader playbook: Explain the why. Co-design roles. Build dual tracks (tech and human). Stop talent hoarding (use internal gigs). Measure outcomes, not hours.
• Employee playbook: Growth mindset. Proactively job craft. Build an internal brand. Network with purpose.
• Avoid these traps: Tech-first and people-last rollouts, resistance to change (managers and staff), and underinvesting in reskilling.

Bottom line: Human-centric automation is not charity. It is competitive strategy. The organizations that upskill, redeploy, and design “orchestrator” roles will move faster and keep their best people while others chase talent in the market.

As automation accelerates, how can organizations ensure people remain at the center? Below, I explore how companies can embrace AI and automation in a human-centric way by investing in talent, skills, and new role designs that drive both technological and human innovation. I’ll unpack the latest data on job transitions, the skills of the future, and strategies like job crafting, internal mobility, and “orchestrator” roles that enable employees to thrive alongside automation. Ultimately, a human-forward approach to automation isn’t just good ethics. It’s good business, boosting agility, innovation, and retention.

Shifting Job Landscape: WEF Insights on 2027–2030

Global workforce trends show significant change and growth ahead. According to the World Economic Forum, nearly a quarter of jobs (23%) are expected to change by 2027 due to emerging roles and outdated ones. In concrete numbers, that means 69 million new jobs created and 83 million eliminated over five years. Looking further out, 170 million new roles could be generated by 2030 with 92 million displaced, yielding a net gain of 78 million jobs globally. In short, while automation and other trends will disrupt many positions, overall employment is still projected to grow in the long run.

Such change will be accompanied by a dramatic skills shift. Employers anticipate that roughly 39% of core skills could change by 2030, a slight moderation from earlier estimates (44%). This means that the average worker’s skill profile in a few years will look very different than today’s. Technical proficiency alone won’t be enough. Success will require continuously reskilling and upskilling to keep pace with new tools and workflows. In fact, WEF surveys find 59% of workers may need reskilling by 2030 as roles evolve, yet 11% of those in need might not receive it. Bridging this training gap is crucial to prevent job loss and unleash workers’ potential in newly created jobs.

“If the global workforce were 100 people, 59 would require upskilling by 2030, and 11 of them may not get it,” notes the WEF, highlighting the urgency for large-scale skill development.

Skills of the Future: Tech and Human Abilities

What skills will define the future workforce? Technology skills are surging in demand, but human skills are just as critical. The fastest-growing capabilities employers seek by the end of this decade span both domains. On the tech side, AI and big data analytics top the list, followed by networks, cybersecurity, and general tech literacy. In fact, 65% of WEF survey respondents expect job growth in data, AI, and machine learning roles as organizations invest in these areas. At the same time, roles in digital commerce, green energy, and other tech-driven fields are expected to expand sharply.

Top 10 fastest growing skills by 2030 (World Economic Forum Future of Jobs Report 2025). Both tech skills (AI, data, cybersecurity) and human skills (creative thinking, leadership, resilience) are on the rise.

Equally important are the core human skills that automation cannot replace. In a world of smart machines, uniquely human attributes give workers a competitive edge. Employers report rising demand for creative thinking and innovation, resilience and flexibility, curiosity and lifelong learning, and social influence and leadership. These skills enable workers to do what AI cannot, such as thinking outside the box, adapting to change, leading teams, and empathizing with customers. In fact, analytical thinking and creative thinking are rated among the most important skills by nearly half of companies, even as they implement AI. The message is clear: the ideal future employee is tech-savvy and human-savvy. Success lies in blending digital skills with strong soft skills, allowing people to work with machines effectively. As one WEF analyst put it, the goal is a workforce adept in “a combination of both skill types” to thrive in a fast-shifting job market.

Deep Dive: Job Crafting in the AI Era

Adapting to automation is not just about top-down reskilling programs. It is also about empowering employees to reimagine their own roles. Enter job crafting, a concept pioneered by Yale professor Amy Wrzesniewski and Jane Dutton. Job crafting means proactively redefining your job to better suit your strengths and interests, instead of passively accepting a job description. According to Wrzesniewski and Dutton’s framework, employees can reshape their jobs in three ways: task crafting, relational crafting, and cognitive crafting.

• Task crafting involves altering the what and how of your work tasks. For instance, an employee might add new responsibilities they find engaging, automate repetitive tasks, or change the way they perform a task to make it more efficient or meaningful.
• Relational crafting means changing up the who. This involves redefining work relationships and interactions. For example, you might collaborate with different colleagues, engage more with customers, or seek mentoring to shape the social dynamics of your role.
• Cognitive crafting is about reframing the why. This is about shifting how you perceive your job and finding new meaning in the work. For example, a hospital cleaner might view their work not as “mopping floors” but as “creating a safe, healing environment.” This mindset shift boosts purpose.

Crucially, AI can be a powerful tool for job crafting. By automating drudgery and providing new insights, AI allows employees to redesign their roles around more rewarding, high-impact activities. Research finds that when employees self-adopt generative AI tools at work, it enhances job crafting. AI helps them analyze their work, spot improvement areas, and adjust tasks to better align with their strengths and interests. In other words, rather than waiting for managers to redefine jobs, employees can use AI to proactively reshape their own work. They can delegate routine tasks to algorithms and expand the creative and problem-solving aspects of their jobs. This bottom-up adaptation can increase not just productivity but also career satisfaction and commitment.

Role Transformations with AI: Three Examples

Let’s make this concrete. Here are three traditional roles and how forward-thinking individuals are “crafting” them into new, AI-enhanced versions:

• Financial Analyst: Instead of spending days manually consolidating spreadsheets, an analyst might deploy an AI assistant to crunch numbers and generate draft reports. This frees the human to focus on higher-value tasks like interpreting trends, crafting strategy recommendations, and communicating insights to leadership. The analyst evolves from a number-cruncher into a strategic advisor, using tools like GPT-4 to explore “what if” scenarios and identify hidden patterns. In essence, AI augments the analyst’s quantitative heavy lifting, allowing them to craft their job more toward analysis and decision support rather than data wrangling. The result is a role with greater impact and meaning. They translate data into business strategy, not just produce reports.
• Software Developer: Today’s developers are increasingly co-coding with AI. Routine coding and debugging can be offloaded to AI pair programmers (such as GitHub Copilot), letting the developer concentrate on system design, orchestration of modules, and creative problem-solving. A coder thus “crafts” their job from writing boilerplate code to becoming an architect and orchestrator of development. They design what the system should do and guide AI tools to generate pieces of it. They effectively integrate multiple AI “colleagues” (code generators, test bots, etc.) into the workflow. This can expand a developer’s capacity tremendously. One engineer described it as “learning to reallocate mental energy toward what’s newly valuable: systems thinking, creativity, and integrating AI tools. Embracing AI as an amplifier, not a threat.” In short, the developer’s role shifts toward higher-level thinking and AI oversight rather than typing out every line of code.
• Customer Service Agent: Rather than handling the same FAQ inquiries all day, a support agent can let an AI chatbot resolve the simple questions (password resets, order status, etc.). The human agent then steps in for complex or sensitive cases, where empathy and problem-solving are vital. By crafting their role this way, the agent transitions from an “answering machine” to a customer care specialist who focuses on high-touch interactions. They might use AI tools to summarize customer history and sentiment before a call, so they’re better prepared to help. The agent’s job becomes more engaging. They solve unique issues and build customer relationships while AI handles the repetitive tier-1 queries. Importantly, this also enriches the customer experience. Routine issues get instant answers via bots, and tricky issues get thoughtful human attention. Many companies have adopted this model. For example, IKEA introduced an AI chatbot to field 47% of customer queries, and retrained 8,500 call center workers to provide personalized interior design advice instead. The result was not only zero layoffs, but also a new revenue stream (around $1.4B) from these higher-value advisory services.

Internal Mobility and Redeployment: Real-World Case Studies

Forward-looking organizations are not treating automation as a trigger for mass layoffs. Instead, they are redeploying and upskilling employees into new roles where they can add more value. This internal mobility approach turns potential disruption into opportunity. It retains company knowledge, boosts morale, and often creates new business growth. Consider these real case studies:

• IKEA (Ingka Group): When an AI chatbot (“Billie”) was brought in to handle common customer inquiries, IKEA did not simply cut its customer service staff. Instead, the company reskilled 8,500 call center workers into remote interior design consultants, offering online design advice to customers. This pivot not only prevented layoffs, it unlocked a lucrative new service line. These virtual design services generated €1.3 billion (about $1.4B) in revenue in 2022, about 3.3% of IKEA’s total sales. By upskilling instead of downsizing, IKEA turned an automation-driven change into a win-win: employees gained future-ready roles, and the company gained a fresh revenue stream and a more differentiated customer experience.
• The Hartford: This large insurer discovered many workers’ comp claims were simple “medical only” cases that could be automated. Rather than fire their claims handlers, The Hartford built an automation system for routine claims and upskilled its adjusters to focus on complex cases. According to an Aspen Institute study, “The Hartford did not find savings through eliminating workforce. Rather, they reformed roles to fill different business needs, enabling the entire department to handle more (and more efficiently)”. In practice, automation took over low-value tasks (like basic bill approvals), while human staff were redeployed to higher-touch areas like serious injury claims and customer support. The outcome: capacity went up with no layoffs. The team handles more claims faster, quality remains high, and employees moved into more engaging work. This case shows how redeployment plus retraining can boost productivity and employee morale, turning automation into a scalability lever instead of a downsizing trigger.
• Schneider Electric: To better utilize its 155,000-person workforce, Schneider launched an AI-driven internal talent marketplace (using Gloat’s platform). This system matches employees to short-term projects, gigs, or roles based on their skills and interests, increasing agility in talent allocation. The results were striking: within weeks of launch, Schneider unlocked about 127,000 hours of hidden capacity. This was dormant employee time now applied to projects, simply by making internal opportunities more visible. Over time, the platform has unlocked over 360,000 hours, translating to an estimated $15 million in productivity savings and recruiting cost avoidance. Equally important, it improved retention by empowering employees to take charge of their career development and find new growth paths within the company. Schneider’s case demonstrates how an internal mobility strategy (backed by AI) can increase workforce agility. It essentially finds “talent on the bench” and redeploys it to where it’s needed, fast. The business benefits include greater innovation (fresh skills on projects), better engagement, and significant cost savings on hiring.

The common thread: Companies that invest in their people when automating come out ahead. By reskilling or moving employees into new roles, they retain valuable institutional knowledge, avoid the costs of layoffs (and subsequent hiring), and often spin up new capabilities or services. This human-centric approach yields tangible business gains (from IKEA’s revenue boost to Schneider’s capacity surge) while also strengthening culture and loyalty.

Career Pivots: From Executor to Orchestrator

For individual workers, thriving in an automated world often means pivoting your role. You reposition yourself from being a task “executor” to an “orchestrator.” What does that mean? In essence, as AI handles more execution-level work, humans will spend more time prompting, guiding, and integrating AI systems into workflows. The role of a professional evolves to define problems, set direction for AI, and ensure all the pieces fit together. As one tech observer noted about software engineers, “success depends less on coding and more on how you architect, direct, and govern intelligent AI collaborators. This is the logical evolution… the shift from executor to orchestrator.” In many fields, we’re seeing this pivot: prompt engineers, AI project leads, AI ethicists, AI integrators. These are roles focused on steering AI rather than doing all work manually.

To navigate a successful career pivot, professionals should consider strategies to reposition themselves as orchestrators. Here are three approaches:

• AI-Powered Networking: Leverage AI tools to expand and nurture your professional network. Today there are AI assistants that can suggest relevant industry contacts, draft personalized outreach messages, and even remind you to check in with connections. For example, AI-driven networking platforms can match you with like-minded professionals or flag contacts you haven’t engaged with recently. Use these tools to systematically build relationships. They can handle the legwork of identifying who to connect with and even propose conversation starters. By growing a strong network (with a little AI help), you increase your exposure to new opportunities, mentors, and collaborations essential for evolving your career.
• AI-Enhanced Personal Branding: Let AI polish how you present yourself in the job market. Generative AI writing tools can help refine your CV/resume, LinkedIn profile, and even personal blog or portfolio content. For instance, AI resume assistants will analyze a job description and suggest how to tailor your resume with the right keywords and phrasing. They can also help draft compelling summaries or cover letters. Similarly, AI can assist in creating insightful LinkedIn posts or articles to showcase your expertise, ensuring you have a consistent, professional brand. The key is to use AI as a savvy editor/enhancer of your own voice (not to fabricate qualifications!), thereby amplifying your visibility and credibility in your target field.
• Hands-On AI Experimentation: Nothing builds understanding like direct experience. Roll up your sleeves and experiment with AI tools relevant to your field. If you’re in marketing, play with an AI campaign optimizer; if in finance, try a machine learning model on some data; if in content creation, explore image or text generators. By incorporating AI into side projects or volunteering for automation initiatives at work, you develop firsthand skill in prompting, evaluating, and improving AI outputs. This kind of practical exposure not only boosts your resume, it also shifts your mindset from fearing AI to seeing it as part of your team. Many “orchestrator” careers are born from hobbyist experimentation. The employee who became the go-to person for AI solutions simply because they tinkered and learned. Treat AI as a sandbox and invest time in learning by doing.

Overall, the goal for an individual is to proactively adapt your career before your job is automated out. By networking purposefully, curating a strong personal brand, and building AI fluency, you position yourself as someone who drives automation in your organization, not someone sidelined by it. In practice, orchestrators often end up designing and managing the processes that automated their old tasks. This is a far more strategic and secure position to be in.

Leadership & Employee Playbook for Human-Centric Automation

Building an adaptive, human-centric workforce in the age of AI requires action at all levels of the organization. Below is a playbook of strategies for managers and for employees to navigate this transition successfully, as well as common pitfalls to avoid.

For Managers (Leaders):

• Communicate the “Why” and Purpose: Clearly articulate why automation is being implemented and how it aligns with the organization’s mission. Employees are more likely to embrace change if they understand the purpose (e.g. improving customer experience, staying competitive) and see a future role for themselves. Frame automation as an opportunity for the team to tackle more interesting problems, rather than a threat to jobs. Consistent, transparent communication helps build trust through the transition.
• Co-Design Evolving Roles: Don’t unilaterally impose new workflows. Co-create them with your team. Engage employees in brainstorming how their roles could shift alongside AI: what tasks can be offloaded and what new responsibilities or projects they could take on. This collaborative job redesign process (akin to guided job crafting) ensures buy-in and often uncovers creative role evolutions that managers alone might miss. It signals respect for employees’ knowledge of their own work.
• Enable Dual Tracks (Tech + Human Skills): Support development of both technical and soft skills in parallel. For example, pair an AI tools training with a workshop on creative thinking or customer empathy. Encourage a “T-shaped” growth mindset: deep expertise in one area, with broad skills in others. Formally, this could mean offering cross-training, stretch assignments, or rotations that broaden skill sets. Leaders should also reward learning, not just performance, to incentivize continual upskilling.
• Empower (Don’t Hoard) Talent: It’s vital for mid-level managers to let employees grow and move into new internal opportunities, even if it means losing a star performer on your team. Adaptive organizations avoid the “talent hoarding” trap. Create a culture where managers are talent stewards who actively help their people pursue growth roles (in your department or elsewhere in the company). This might involve setting up internal gig marketplaces (like Schneider Electric did) or mentorship programs. Managers who empower their people in this way see higher engagement and loyalty, and those organizations win long-term as they retain skills in-house.
• Measure Outcomes, Not Hours: As roles evolve, ensure your metrics of success evolve too. Shift performance management to focus on outcomes and impact rather than old-school metrics like hours worked or widgets produced (especially when AI is handling many widgets!). For instance, if an employee’s new role is “AI overseer + client consultant,” define what success looks like (e.g. number of successful client implementations, AI error rate reduced, client satisfaction scores) and track those. This reinforces that the new job is valued and helps all parties understand how to excel in it. It also avoids punishing employees for productivity dips during reskilling periods. The emphasis is on ultimate outcomes.

For Employees:

• Adopt a Growth Mindset: Approach automation changes with curiosity and openness. Instead of “Will a robot take my job?”, ask “What new skills can I learn, and how can I work with the robots?” Those who view career as a continuous learning journey will fare best. Embrace training opportunities (even if they feel daunting) and seek feedback. A growth mindset turns setbacks into lessons and sees the influx of AI as a chance to expand your capabilities.
• Be a Job Crafter: Take initiative to reshape your role around what you do best. As described earlier, look for tasks you can redesign, delegate to AI, or approach differently to make your work more meaningful or efficient. Don’t wait for perfect instructions. Experiment (within reasonable bounds) with new workflows. For example, if you find a way to automate a report and free up 2 hours for analysis, propose that. Managers often appreciate employees who proactively improve their jobs. By job crafting, you also demonstrate adaptability, a key trait in the future workplace.
• Build Your Internal Brand: In a dynamic environment, simply “doing your job” may not guarantee recognition or security. You also need to broadcast your skills, achievements, and growth to the organization. This means updating colleagues and managers on new things you’ve learned or successes you’ve had (without bragging). It could involve sharing a short presentation on an AI tool you experimented with, or writing an internal blog about a project win. Networking internally is part of this too. Cultivate relationships outside your immediate team. A strong internal reputation as someone who learns and adds value will make others eager to pull you into new opportunities that arise.
• Network with Purpose: Similarly, keep expanding your external network with an eye toward learning. Connect with peers in your industry who are working with new tech or tackling similar transitions. Join communities or forums around your skill areas. Having a rich network can expose you to emerging roles and in-demand skills early, and perhaps provide a safety net if you do decide to make a larger career leap. Purposeful networking is not just collecting contacts, but engaging with people who inspire you to grow.

Pitfalls to Avoid:

• Tech-First, People-Last Implementation: Avoid rolling out automation for automation’s sake, without considering the human element. Deploying a fancy AI tool without adequate training, change management, or alignment to workers’ day-to-day reality is a recipe for failure. Always plan the people strategy (communication, training, role design, feedback loops) in tandem with the tech deployment. A tool that alienates or confuses staff will never deliver its promised ROI.
• Resistance to Change (on Both Sides): Change is hard, but clinging to old ways can be fatal in the long run. This applies whether it’s a manager refusing to evolve processes or an employee refusing to learn new tools. Managers should watch out for their own resistance too: e.g. dismissing employees’ ideas for new ways of working, or undermining an automation initiative because it wasn’t invented by them. Foster an atmosphere where experimentation is welcome and failure is treated as learning, so that resistance gives way to innovation.
• Underinvestment in Reskilling: Perhaps the biggest pitfall is not putting money and time into developing your people. Automation savings often tempt leaders to cut training budgets, but that is short-sighted. Without reskilling, you end up with either an underutilized workforce or costly layoffs (and then shortages of skilled talent). Underinvestment can also manifest as training that’s too shallow or generic (“learn to code in 5 days!”), which doesn’t truly prepare employees for new roles. Companies that win devote significant resources to robust upskilling programs, coaching, and continuous learning platforms. Skimping on this virtually guarantees a skills gap and talent crunch down the line.

Embracing a Human-Centric Automation Strategy (Conclusion)

The case is clear: putting people at the heart of your automation strategy yields better results for both organizations and employees. Companies that have treated their workforce as partners in automation (not collateral damage) see benefits in agility, innovation, and talent retention. By redeploying workers into new roles (IKEA’s design advisors, The Hartford’s complex claim specialists) or unlocking latent skills (Schneider’s internal gig marketplace), organizations become more adaptable and resilient in the face of change. They can respond to new opportunities faster because the talent is in-house and ready, and they maintain critical institutional knowledge, which drives innovation. Additionally, employees who feel invested in are far more engaged and loyal. This reduces turnover costs and often increases customer satisfaction because a stable, skilled workforce delivers better service.

In sum, the “human side of automation” is not a soft, feel-good add-on. It is a strategic imperative. A data-driven, human-forward approach to AI adoption is what separates the companies that merely install new tech from those that truly transform and thrive. As the WEF notes, we are entering a future where technological skills and human skills must combine. Organizations that cultivate this blend (through upskilling, job crafting, internal mobility, and enlightened leadership) will be the ones that leap ahead, powered by engaged employees working alongside intelligent machines.

CTA

If you’re a tech or HR leader looking to turn these principles into practice, we invite you to explore how OutsideCapital can help. From crafting upskilling programs to deploying talent marketplaces, OutsideCapital specializes in building adaptive talent strategies tailored for the age of AI. Embrace automation with a human touch. Your people and your bottom line will thank you. Let’s shape the future of work together.

References

• Future of Jobs Report 2023: Up to a Quarter of Jobs Expected to Change in Next Five Years > Press releases | World Economic Forum – https://www.weforum.org/press/2023/04/future-of-jobs-report-2023-up-to-a-quarter-of-jobs-expected-to-change-in-next-five-years/
• Future of Jobs Report 2025: 78 Million New Job Opportunities by 2030 but Urgent Upskilling Needed to Prepare Workforces > Press releases | World Economic Forum – https://www.weforum.org/press/2025/01/future-of-jobs-report-2025-78-million-new-job-opportunities-by-2030-but-urgent-upskilling-needed-to-prepare-workforces/
• Future of Jobs Report 2025: The jobs of the future – and the skills you need to get them | World Economic Forum – https://www.weforum.org/stories/2025/01/future-of-jobs-report-2025-jobs-of-the-future-and-the-skills-you-need-to-get-them/
• What is Job Crafting? (Incl. 5 Examples and Exercises) – https://positivepsychology.com/job-crafting/
• Job crafting and meaningful work – https://positiveorgs.bus.umich.edu/wp-content/uploads/Job-Crafting-and-Meaningful-Work.pdf – Generative AI adoption and employee outcomes: a conservation of resources perspective on job crafting, career commitment, and the moderating role of liking of AI | Humanities and Social Sciences Communications – https://www.nature.com/articles/s41599-025-05656-4?error=cookies_not_supported&code=55ac7770-a154-4ee8-bebc-2bcecb7f3c34
• 5 Things That Will Change AI Software Engineering by 2026 – https://www.crossover.com/resources/5-things-that-will-change-ai-software-engineering-by-2026
• IKEA bets on remote interior design as AI changes sales strategy | Reuters – https://www.reuters.com/technology/ikea-bets-remote-interior-design-ai-changes-sales-strategy-2023-06-13/
• Scale Claims with Automation, Minimize Headcount Growth – https://www.clever-docs.com/blog/scaling-claims-departments-efficiently-with-automation-minimizing-headcount-expansion/
• Case Study: Upskilling Around Automation at The Hartford – https://www.aspeninstitute.org/publications/case-study-upskilling-around-automation-at-the-hartford/ – How Schneider Electric increased employee retention | Gloat
• https://gloat.com/resources/customer-stories-2/how-schneider-electric-increased-employee-retention/
• Best AI Tools for Career Development | Coursera – https://www.coursera.org/articles/best-ai-tools-for-career