In just a few short years AI has become a strategic partner in resource planning, offering insights and recommendations that were once unimaginable. Yet behind every successful AI implementation stands a skilled human professional whose role is evolving.
With over 20 years of experience in resource management and HR, Kathleen Fitzgerald has witnessed firsthand how AI is reshaping resource planning. In this latest collaboration between Retain and Linked Workforce, we explore how AI isn't diminishing the resource manager's role, but creating opportunities for those ready to evolve alongside the technology.
The myth of the AI takeover
Let’s start with the most persistent misconception about AI in resource management: that it will take over, making human expertise redundant.
"The biggest misconception enterprise businesses have about implementing AI is that it will completely streamline resource management, that it will take minimal work and automatically result in the best scheduling decisions," Kathleen explains. "AI can definitely streamline a number of resource management tasks and provide data to be considered in resource management decisions. Successful AI implementation, however, requires a strategic approach, high-quality data, skilled personnel, ongoing monitoring, and a clear understanding that AI is a powerful tool that enhances, but does not replace, human expertise and strategic thinking."
This misunderstanding leads organisations down the wrong path. Companies that view AI as a complete solution rather than a powerful tool often struggle with adoption and fail to realise its benefits. They invest in sophisticated systems but underinvest in the human expertise needed to guide, interpret, and apply AI insights effectively.
The reality is more nuanced and ultimately more promising for resource management professionals. AI is brilliant at processing vast amounts of data, identifying patterns, and making recommendations based on predefined parameters. But it lacks the contextual understanding, relationship management skills, and nuanced judgement that experienced resource managers bring to the table.
Organisations that successfully implement AI understand this complementary relationship. They view AI not as a replacement but as a partnership that allows their resource managers to work at a higher level, focusing on strategy rather than being bogged down by admin tasks.
🖱️Related reading: Understanding AI in resource management
The new resource management skillset
As AI handles more routine aspects of resource planning, a new and more strategic role is emerging for resource managers. You can't just rely on the same old skills anymore—you need to combine tech know-how with business smarts to really make the most of what AI offers.
"The key new skills for resource managers working with AI systems revolve around understanding AI capabilities and limitations, interpreting data-driven insights, collaborating with AI and technical teams, making strategic decisions informed by AI, and addressing the ethical implications of AI in resource management," Kathleen notes. "Continuous learning and adaptation will be essential for resource managers to thrive in an AI-driven environment."
Where resource managers once spent hours matching skills to projects or generating utilisation reports, they can now focus on higher-value activities. The admin burden diminishes as AI handles the data-heavy lifting, creating space for more strategic work.
Kathleen foresees resource management becoming a more influential function too: "As the role evolves to become more strategic with the integration of AI, responsibilities will expand beyond tactical allocation to encompass more forward-thinking and analytical functions." She highlights several new areas of focus, including "AI system governance and optimisation, strategic workforce planning and forecasting, driving data-driven resource decisions, and collaboration between humans and AI."
To succeed in this enhanced role, resource managers may need to go beyond traditional remits. Kathleen identifies several critical skills, including "advanced data analytics and visualisation, AI strategy and governance, business acumen and strategic thinking, systems understanding, stakeholder management and influence, change leadership, and problem-solving with an AI lens."
Perhaps most importantly, resource managers need to develop a good understanding of AI ethics and regulations. As the guardians of fairness in resource allocation, they play a crucial role in ensuring AI systems don't perpetuate biases or create inequitable outcomes.
🖱️ Related reading: AI in resource management ethical considerations
The human-AI partnership model
When AI implementation works well, it creates a powerful partnership that delivers better results than either humans or machines could achieve alone. Kathleen shared an example of this in action:
"A consulting firm struggled to efficiently staff projects. Manual matching was time-consuming and sometimes resulted in consultants being underutilised or mismatched. They implemented an AI-powered resource management platform that analysed consultant profiles, project requirements, and project timelines. The AI system provided recommendations for open roles, considering not only skills and availability but also factors like team structure and consultant career goals."
The results were impressive. The firm saw "increased project profitability by ensuring the right level of expertise at the right cost, improved consultant utilisation rates by minimising bench time, enhanced client satisfaction through better-matched teams delivering higher quality work, improved employee satisfaction, and reduced staffing lead times."
In practice, this partnership transforms daily operations. Like in this example below, AI might flag that a project needs a senior resource with specific expertise and suggest potential matches based on skills, availability, and past performance.
The resource manager then applies their knowledge of team dynamics, client relationships, and career development goals to make the final decision. They might know, for instance, that one of the suggested resources has previously shown interest in working with this particular client or technology.
Data governance as the foundation
For all the sophisticated algorithms and cutting-edge AI capabilities, there's one fundamental truth that Kathleen emphasises: without good data, even the best AI systems will fail to deliver value.
"Data quality and a strong data governance framework are absolutely critical if you are to use AI successfully," Kathleen explains. "They provide the foundation upon which reliable AI applications can be built. AI models learn patterns from historical data to make recommended matches, predictions etc. If they are doing this based on inaccurate or incomplete data, AI will learn flawed patterns."
This can often be overlooked in the rush to implement AI solutions. Organisations get excited about the potential benefits but underestimate the work required to ensure their data is fit for purpose. You know how it goes—everyone wants the shiny new AI tool, but nobody wants to do the unglamorous work of cleaning up years of inconsistent data entry.
When asked what one change enterprises could make immediately to prepare for AI integration, Kathleen didn't hesitate: "Ensure a solid data governance framework is in place. AI will leverage that data available to base decisions and develop patterns. Having accurate/updated, consistent and accessible data is critical."
For resource management specifically, this means addressing several common data challenges. Many organisations struggle with fragmented information spread across multiple systems, inconsistent skills taxonomies, outdated profiles, and incomplete historical project data. Fixing these issues is essential for successful AI implementation.
Similarly, data quality also connects directly to ethical considerations in AI-driven resource management. "AI algorithms learn from the data they are trained on. If that data contains biases, the AI can perpetuate and even amplify those biases in resource allocation decisions," Kathleen cautions. "To mitigate this, companies should thoroughly analyse training data for potential biases, use diverse and representative datasets, implement fairness-aware AI algorithms and techniques, and establish auditing processes to monitor AI decisions for bias."
🖱️ Further reading: AI in resource management implementation guide (complete roadmap)
The next 3-5 years: strategic resource management
Looking ahead, Kathleen sees AI pushing resource management into increasingly strategic territory. AI won't stop at automating administrative tasks—it will literally transform how organisations think about deploying talent.
"In the next several years, we expect continued automation of resource management tasks, freeing up resource managers for strategic work," Kathleen predicts. "Resource Managers will become more skilled at integrating AI into resource management decisions, while still providing the necessary oversight."
One of the most exciting developments she anticipates is a shift toward more personalised resource allocation. "AI will go past matching resources based on skills and availability. It will likely consider individual preferences, career goals, and personality traits to create personalised and optimised teams."
So, rather than simply maximising utilisation rates, resource planning will increasingly centre on people and teams. By taking a more holistic view of what makes teams effective and what keeps employees engaged, AI-powered resource management has the potential to simultaneously improve business outcomes and employee satisfaction.
Kathleen also sees AI playing a more active role in talent development. "Currently AI helps identify skill gaps and recommends training. In the future AI will play a more active role in career development by mapping career paths based on individual skills and aspirations, recommending projects and assignments that support skill growth, and facilitating internal talent mobility by identifying opportunities for employees."
Of course, these advancements will come with ethical considerations. "Going forward AI will be built with strong ethical frameworks to prioritise fairness and unbiased decision-making," Kathleen notes. "There will be more transparency in AI recommendations and more experienced human oversight and control."
🖱️ Further reading: Security and privacy in AI resource management
Starting small, thinking big
Despite all the possibilities of AI in resource management, Kathleen advocates for a measured, strategic approach to implementation. Rather than attempting to transform everything at once, she recommends starting with focused initiatives that address specific challenges.
"Have clear objectives," Kathleen advises. "Identify pain points and specific resource management challenges that AI can address." This approach helps you demonstrate value quickly while building momentum for broader adoption.
Just as important is a phased implementation strategy. "Start small... have phased implementation and iterative learning. Consider pilot projects to start," Kathleen suggests. "Implement AI in stages, continuously evaluating its performance and making adjustments as needed."
This approach offers several advantages. It reduces risk by allowing organisations to test and refine their approach before committing significant resources. It also creates opportunities for resource managers to develop new skills gradually, working alongside AI systems in increasingly sophisticated ways.
Likewise, communication and change management are equally key. "Focus on communication, change management and training," Kathleen emphasises. "Get buy-in by communicating the benefits to stakeholders, resources managers and employees. Provide comprehensive training on how to use AI tools and interpret AI-generated insights. And address concerns as needed."
Similarly, organisations often underestimate the human side of AI implementation. The technology might be ready, but if people don't understand it, trust it, or know how to use it effectively, adoption will stall. Effective change management acknowledges both the rational and emotional aspects of technology adoption.
This dual focus—on both systems and skills—creates a foundation for sustainable success with AI-powered resource planning.
Elevate resource management through AI
As admin burdens lower and data-driven insights increase, resource managers are positioned to become more strategic partners in business success.
"Successful AI implementation requires a strategic approach, high-quality data, skilled personnel, ongoing monitoring, and a clear understanding that AI is a powerful tool that enhances, but does not replace, human expertise and strategic thinking," Kathleen reminds us.
This balanced perspective is key to unlocking AI's potential while avoiding common implementation pitfalls. The most successful organisations will be those that invest not just in AI technology but in developing their resource management teams' capabilities alongside it.
Ready to explore how AI can elevate your resource management function? Speak with Retain's experts to learn how our intelligent resource planning solutions can help your organisation make the move to more strategic, AI-enhanced resource management.
