The Workforce of 2027: How AI Is Changing the Way Work Gets Done
The conversation about AI and jobs has often been framed as a simple question: Will AI eliminate jobs or create them?
For organizations planning their workforce strategies, that may be the wrong question.
AI is changing the work itself. It is altering which tasks people perform, the skills organizations need, how technology initiatives are implemented and how companies think about building teams.
That distinction matters as organizations look toward 2027.
In a recent conversation, Jen Franklin, Chief Strategy Officer at BCforward, shared what she is seeing across the workforce market after nearly 20 years in the industry. Her perspective is that AI is not simply reducing the need for people. Instead, it is changing where human expertise creates the most value.
That shift has implications for everything from talent strategy and change management to data centers, cybersecurity and the way companies structure their workforce.
- AI is changing the composition of work, not simply eliminating it.
- Skills that connect technology with business outcomes are becoming increasingly important.
- Organizations may underestimate the human expertise required to implement AI successfully.
- Data-center investment is creating workforce demand well beyond traditional IT roles.
- Change management and workforce development are becoming essential components of technology transformation.
- Workforce planning is shifting from “How many people do we need?” to “What work needs to get done, and where can people and technology create the most value?”
AI Is Changing Work More Than It Is Eliminating It
The AI workforce conversation can become overly binary.
Organizations automate a task, and the immediate assumption is that fewer people will be needed. But what BCforward is seeing is more nuanced.
As Jen explained, AI is changing the composition of how work is structured. Organizations can automate pieces of work, but they still need people to implement the technology, integrate it into the business, secure it, govern it and help employees adopt it.
That is creating demand around areas such as:
- Change management
- Project and program management
- Information security
- Data governance
- Business analysis
- Process and systems design
- Communication
The value of human expertise is shifting toward work that requires judgment, problem solving, collaboration and an understanding of both the business and the technology.
Highly repetitive and rules-based work may increasingly be automated. But that can also create capacity for employees to focus on activities that require greater creativity, critical thinking and innovation.
Buying AI Is the Easy Part. Changing the Business Is Harder
One of the most important distinctions for business leaders is the gap between acquiring AI technology and successfully operationalizing it.
An organization can purchase a platform quickly. Transforming a complex enterprise around that technology is a very different challenge.
Jen pointed to several layers that have to come together:
Data. AI requires information that is reliable, accessible and representative of the business.
Integration. Most enterprises do not operate from a single data source or technology environment.
Security and governance. Organizations need controls around how information and AI capabilities are accessed and used.
Testing. New capabilities have to function reliably within actual business processes.
Measurement. Ultimately, organizations need to determine whether the investment is producing the productivity, speed or business value they expected.
This is why AI implementation can actually increase the need for certain types of expertise.
The question isn’t simply whether a task can be automated. It is whether the organization has the people, processes and governance needed to turn that automation into a meaningful business result.
“Companies definitely underestimate the distance between buying the technology and actually changing the way their organization works.” — Jen Franklin, Chief Strategy Officer, BCforward
Moving from AI experimentation to execution?
BCforward helps organizations connect technology investments with the talent, governance, workforce enablement and delivery capabilities required to turn AI strategy into measurable business outcomes.
AI Is Also Creating New Workforce Demand
AI’s impact on the workforce extends beyond the applications employees use every day.
The infrastructure required to support increased computing and data demand is creating an entirely different category of workforce opportunity.
Data centers are one example.
It would be easy to think of a data-center build as primarily an IT initiative. In practice, Jen describes it as a much larger workforce ecosystem.
A major data-center initiative can involve:
- Infrastructure and networking
- Cloud expertise
- Data-center operations
- Construction
- Electrical and mechanical engineering
- Facilities
- Procurement
- Supply chain
- Security
- Finance
- Project and program management
And the workforce need does not end when construction does.
Organizations must continue operating, maintaining and upgrading that infrastructure. That creates longer-term demand for specialized expertise, vendor management and operational support.
For workforce leaders, this illustrates a larger trend: technology investment can redistribute demand rather than simply reduce it.
Organizations will need to understand not just which roles disappear or change, but which capabilities become more important as their technology environments evolve.
Digital Transformation No Longer Has a Finish Line
Organizations once tended to approach digital transformation as a defined journey.
Implement the system. Move to the cloud. Digitize the process. Complete the change-management program. Reach the other side.
AI is making that model increasingly difficult to maintain.
Jen’s view is that AI is beginning to transform the operating model itself.
That requires organizations to ask broader questions:
- Where is there unnecessary friction in the customer experience?
- Which workflows should be redesigned rather than simply automated?
- How should data support those workflows?
- How should that information be governed and secured?
- How do employees’ roles change as the work changes?
- What business outcome is the organization actually trying to create?
Those questions move transformation beyond technology implementation and into business strategy.
And that makes another discipline increasingly important.
Change Management Cannot Be an Afterthought
Organizations can invest heavily in technology while underinvesting in the people who need to use it.
That is one of the risks Jen sees as AI initiatives accelerate.
Successful transformation starts with understanding the purpose behind the change: what customers, employees or other stakeholders actually need.
Only then can organizations determine how technology should support that experience and how employees need to work differently.
Change management becomes the bridge between implementation and adoption.
It helps organizations determine whether the business can absorb the change, how day-to-day work will be affected and what employees need before and after a new platform or process goes live.
That is particularly important with AI because adoption can bring uncertainty alongside opportunity.
Organizations that handle this well are not simply telling employees that AI is coming. They are helping people understand how their work will change and building the capabilities needed to succeed in that environment.
The Workforce Planning Question Is Changing
Perhaps the biggest workforce shift heading into 2027 is not a particular technology or job category.
It is the question leaders are asking.
Historically, workforce planning often starts with:
How many people do we need?
Jen expects the conversation to become more intentional:
What work needs to get done?
What should the technology do?
Where does human expertise create the greatest value?
This leads to a very different workforce model.
Organizations may increasingly combine:
- Full-time employees
- Specialized external talent
- Project-based expertise
- Professional Services
- Outcomes-based managed services
Rather than defaulting to one workforce model, companies can select the talent and delivery structure that best matches the work.
That also makes skills such as engineering, DevOps, business analysis, project management and change management particularly important because they sit at the intersection of technology, transformation and business outcomes.
Start With the Business Strategy, Not the AI Hype
There is pressure for organizations to move quickly on AI.
But speed without direction can create activity without meaningful transformation.
Jen’s recommendation is to start with the work creating the greatest value for the organization.
Then ask:
Where could technology accelerate that value?
From there, organizations can determine how employees, specialized talent and external partners should contribute.
That approach also changes the workforce-development conversation.
Instead of creating fear around whether AI will replace someone’s job, organizations can focus on a more productive question:
How can AI help people do their best work?
Skills training, tool training, capability assessments, demonstrations, communication and change management can then support employees as their roles evolve.
That may ultimately be one of the defining workforce challenges of the next several years.
The organizations positioned to benefit most from AI will not necessarily be the ones that deploy the technology first.
They may be the ones that are most deliberate about where technology belongs, where people create value and how the two work together.
Preparing the Workforce for What Comes Next
AI, infrastructure modernization and digital transformation are changing how organizations operate.
But technology alone does not create the outcome.
Businesses still need people who can connect strategy to execution, translate technology into usable capabilities, manage change and continually improve how work gets done.
For workforce leaders preparing for 2027, the goal is not simply to predict which jobs AI will replace.
It is to understand how the work is changing — and build the workforce capable of changing with it.
Build a workforce strategy around the work ahead.
BCforward combines workforce expertise with Professional Services capabilities to help organizations access specialized talent, manage transformation and deliver business outcomes as technology and workforce needs evolve.
