Gen AI-enabled workforce transformation

To realise the most value from generative AI, organisations need to rethink how their people work. That is because adopting it is not simply about upgrading to the latest technology—it is about using generative AI’s revolutionary capabilities across the enterprise to create more productive, efficient, and innovative workers.

Without a thorough workforce review and overhaul plan, organisations risk missing out on the strategic and operational opportunities that implementing generative AI creates.

The technology’s ability to process natural language, digest large amounts of data, and produce unique content make it a potent tool for automating and enhancing knowledge work. This is the new frontier of AI-enabled productivity, where many industries will create much of their value.

With a well-constructed plan, organisations have an opportunity to define the workforce of the future and draw top talent to a best-in-class professional development environment employing leading-edge technology and work practices.

Conor McCarthy and our People and Change team explain how your organisation can leverage generative AI below.

Changing knowledge work

Generative AI is a step-change in technology-enabled working. Traditional AI and machine learning have excelled at numerical processing and optimisation, making them an excellent solution for automating and accelerating specific tasks.

Generative AI is different because of its potential to spur innovation in all kinds of roles by drawing from its massive foundation of organised knowledge. Generative AI is intuitive and adaptable. Not only can it handle simple workflow automations, but also it can support and inform knowledge workers, super-charging their creativity and decision-making.

This creates huge opportunities to augment the power of people quickly and drive businesses forward. The key is building an intentional and sustainable strategy of upskilling, overhauling, and augmenting roles. To accomplish this will require focusing on how and where the technology can best be applied not to replacing workers, but to empowering them to be more productive on high-value tasks.

This change to workforce composition, roles, skills, and organisation structures, alongside generative AI systems, is imperative. Companies that collaborate cross-functionally to deploy generative AI thoughtfully, govern it responsibly, and integrate it seamlessly, will gain sustainable competitive advantage.

The approach cannot be piecemeal. Organisations must commit to a holistic workforce transformation. By thoughtfully deploying generative AI across critical roles, they can improve productivity, cost efficiency, innovation velocity, and revenue growth. And it is all possible without leaving humans behind.

Generative AI success requires workforce remodel

A framework for reshaping the workforce

There is no question that generative AI will disrupt how businesses and people work. Minimising the shock to the workforce, managing organisational risk, and realising the full value of adopting the technology is possible through a cross-functional, holistic approach focused on four key areas:

Identifying capabilities, roles, and enablers
Addressing risk and compliance
Activating role augmentation
Capturing value

Transforming the workforce with generative AI

Workforce transformation, through immediate knowledge worker role augmentation, is at the centre of realising quantifiable value through generative AI.

Unifying the technology, workforce impacts and opportunities with the risk and responsible AI considerations is critical to launching a successful generative AI program.

1. Identifying capabilities, roles, and enablers	2. Addressing risk and compliance	3. Activating role augmentation	4. Capturing value
Identify high-impact knowledge worker roles and prioritise by value opportunity. Deconstruct the work by role to explore how activities can be automated, enhanced with AI: impacts on repetitive tasks, adding to creativity and critical thinking. Identify AI solutions, alliances, partners, and technology enablers.	Revisit safeguards of privacy and confidentiality when utilising data in the context of responsible AI. Establish ongoing policy and compliance capability for internal and external monitoring and revision. Actively manage relevant compliance and regulatory topics by jurisdiction. Set ethical and intentional guidelines and policies.	Deploy impactful generative AI solutions and vendor-specific AI capabilities through strategic alliance partnerships pointed at prioritised role augmentation. Manage ongoing active alliance and vendor based generative AI innovation applicability analysis. Adopt, monitor, refine, train, and optimise generative AI usage across the enterprise by role.	Capture productivity and capacity through SG&A savings, productivity enhancement, re‑investment, through workforce shaping. Differentiate and create value through businessspecific generative AI training and data. Anticipate and activate organisational changes from generative AI, ensuring employees are engaged, informed, and supported. Refresh and modernise workforce strategy with work breakdown and who does what work.

1. Identifying capabilities, roles, and enablers

Generative AI can automate and augment many knowledge-worker tasks. Understanding how work gets done and where generative AI can make a difference to work processes is critical to getting the most value from the technology. This is possible by:

Identifying roles where generative AI can have an impact. Back-, middle-, and front-office functions are all candidates for review.
Deconstructing job responsibilities and pinpointing those activities that can be automated or improved.
Building a workforce overhaul strategy that includes education and training for incorporating generative AI. Upskilling is necessary to fully absorb the technology into new work processes.

This focused, data-driven approach enables companies to demonstrate tangible benefits quickly in high-impact areas and build momentum.

Critical success factors for scaling Generative AI

Technology and infrastructure

AI requires a robust technology infrastructure, including computing power, storage capacity, and software platforms.
Consider current technology infrastructure and determine if additional investments are required to support AI ambitions.

Data quality and availability

AI algorithms rely on large quantities of high-quality data to learn and make accurate predictions.
Consider quality and availability of data, including issues such as data completeness, accuracy, and security.

2. Addressing risk and compliance

Deploy generative AI tools ethically and responsibly to limit risk and maintain the trust of customers, shareholders, and workers. We recommend adopting best practices such as:

Forming an AI Center of Excellence to oversee policies, compliance, risk management, and monitoring. Ensure legal, governance, and HR functions are involved early.
Revisiting data-governance practices to ensure privacy and confidentiality. Data usage and potential exposure are among the biggest organisational risks of the technology.
Monitoring regulatory compliance across jurisdictions where the company operates.
Managing compliance using fail fast with guardrails to continue fostering innovation and experimentation.
Instituting human-centric AI principles aligned to corporate values. For the workforce to embrace generative AI it will need to understand how the organisation govern its ethical application.

With rigorous, ethical oversight, organisations can demonstrate their commitment to using generative AI in ways its people, partners, and customers will trust.

Critical success factors for scaling Generative AI

Ethical, legal, and cyber considerations

AI can raise ethical, legal, and cyber-related concerns and risks related to privacy, bias, and fairness.
Ensure AI systems are designed and deployed in a way that is consistent with legal and ethical frameworks, such as data protection regulations and ethical principles.

3. Activating role augmentation

While generative AI is adaptable to all kinds of use cases, choosing the right tools for the right tasks will maximise the technology’s enterprise-wide value. Ensuring solutions are fit for purpose instead of taking a one-size-fits all approach is critical, as is combining a continuous learning culture with a compliance culture. To accomplish this, we recommend:

Continuously evaluating new and existing vendor offerings against priority use cases. Leading platforms like ChatGPT, Bard, and Anthropic’s Claude show immense promise for rapid iteration and capability development.
Running controlled pilots to validate solution fit before deploying them.
Training employees to get the most out of the new tools. This includes upskilling workers with best practices on how to prompt a generative AI system in order to get the most effective and impactful output.
Empowering workers to make the shift from a rigid, repetitive industrial revolution way of working.
Discovering what workers are doing differently now that their roles have been disrupted and deconstructed.
Monitoring how the tools are used and consistently collecting feedback to improve workflows. This agile, human-centric approach enables companies to integrate the most impactful generative AI for their needs.

Critical success factors for scaling Generative AI

Workforce and operating model adoption

AI implementation can require significant changes to processes, systems, and culture.
Be prepared to manage these changes effectively and provide support to employees who may be impacted by them. This includes communication, training, and incentives to encourage AI adoption and engagement.

Generative AI talent and capacity

AI implementation requires specialised skills and expertise, including data scientists, AI engineers, and domain experts.
Assess current AI talent in-house or whether there is a need to hire additional staff or partner with external experts.

4. Capturing value

As generative AI matures, the structure of your workforce and organisation must evolve in parallel to realise the full benefits. Understanding where the opportunities to contribute value lie, and adapting quickly to pursue them, is key to achieving the productivity goals of the organisation. Key initiatives include:

Capture productivity and capacity through SG&A savings, productivity enhancement, re‑investment, through workforce shaping.
Reskilling employees to take on more strategic roles as tasks are automated. This might also include changing how productivity is measured given this new level of technology-supported production.
Engineering processes and talent strategies for human/AI collaboration.
Redefining and right-sizing roles augmented by AI to capture productivity gains.
Quantifying by role the value technology co-working will contribute to strategic priorities like reducing costs or driving innovation.
Prioritising for investment those jobs with the biggest potential impact to organisational objectives.
Proactively communicating changes and supporting your impacted employees.
Reorganising departments and reporting structures to optimise human/AI workflows.

When undertaken together to build a coherent, repeatable generative AI workforce strategy, focusing in these four areas can prepare the organisation to deliver sustainable value. This process can also help manage the risk and inevitable workforce upheaval associated with adopting any transformational new technology.

The goal is to help bring a generative AI strategy to life defining what a future workforce empowered by generative AI might look like and explaining how it might behave. This includes demonstrating the impact generative AI will have at the role and task level for back-, middle- and front-office functions. It means identifying where long-term capability and upskilling programs are needed. It is also important to show where it is possible to be more efficient and to add new value to the organisation.

Critical success factors for scaling Generative AI

ROI and business value

Organisations must carefully evaluate the potential ROI and business value of AI implementation as it can be a significant investment.
Organisations should consider factors such as cost savings, improved efficiency, and enhanced customer experience.

The role of human resources

Human resources (HR) will play a pivotal role in designing and driving a generative AI-focused workforce optimisation plan. For generative AI to be successfully implemented, the workforce needs to understand it and embrace it. That means the organisation must do its part to identify the opportunities and risks to workforce stability and business continuity and proactively develop a plan for managing this workforce transition.

That includes talent planning, both to understand opportunities for sourcing workers and to evaluate the types of skills required for new generative AI co‑working models. It also means reimagining the employee experience, including reviewing how the workforce is evaluated and rewarded and how new roles might impact morale and productivity.

It is also imperative to identify and protect critical roles so the business can continue to deliver for customers through workforce reengineering.

During this highly transitional period, HR’s goal should be to become a true innovation hub for the enterprise. It should drive new ways of thinking, deconstructing outdated structures and roles that were developed during the industrial revolution. It should also work to ensure that the human element is never lost in this new dynamic.

The rise of generative AI presents an incredible opportunity for HR to step up and become strategically important to the business in new ways.

How KPMG can help

An early and enthusiastic advocate for the power of AI, KPMG is well positioned to help your organisation leverage generative AI.

Drawing on our deep experience in machine learning and natural language processing, we can help guide your organisation through strategy, use case development, vendor selection, and implementation— and then provide ongoing support to help you enhance your investment in this transformative technology. We understand both the promise of generative AI and the process and cultural changes that will be required to realise its full potential.

Our KPMG EU AI Hub, created in collaboration with Microsoft and Cranium, will allow you to immerse and ideate in solving complex business, regulatory and technical challenges across the AI life cycle along with partner ecosystems.

KPMG also recognises that all users of generative AI have a responsibility to learn about the technology’s risks and how to control those risks to prevent harm to customers, businesses, and society. Those risks will grow and evolve as AI technology advances and becomes more pervasive, and as public pressure from regulators increases.

Generative AI success requires workforce remodel

To realize the most value from generative AI, organizations need to rethink how their people work. That is because adopting it is not simply about upgrading to the latest technology–it is about using generative AI’s revolutionary capabilities across the enterprise to create more productive, efficient, and innovative workers.

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