News & Insights
AI Workflows and the Future of Business Operations
"If AI changes how work gets done,
should organisations also rethink how the business operates?"
Artificial intelligence is moving beyond individual productivity and into the workflows that run businesses. The shift is not simply about automating tasks. It is beginning to change how work is organised, decisions are made and accountability is assigned.
According to Bain & Company, the companies gaining the most from AI are not necessarily those utilising the most tools, but those redesigning how work gets done around them. PwC makes a similar case at enterprise level - the greater opportunity comes when AI connects strategy, technology, operations and governance rather than running AI projects in isolation.
From automation to operating models
Early applications of AI have largely focused on individual tasks: generating content, analysing information or automating repetitive work. While these applications can improve productivity, they do not necessarily change how the wider organisation operates.
The next stage is broader. AI agents can increasingly work across multiple systems, plan steps towards an outcome and carry work through a process rather than completing a single task.
That changes the opportunity from automating existing work to redesigning the workflow itself. Organisations need to consider which steps are still necessary and where decisions should sit. Bain describes this as a shift from organisational charts towards “accountability charts.” The focus moves from who performs each task to who is responsible for the result.
[Source: Bain & Company]
Productivity is only the starting point
The business case for AI extends beyond making individual employees faster. Productivity and cost reduction are increasingly becoming the baseline, according to a recent report by PwC. There is an opportunity to use AI to strengthen what already differentiates a business, or to make new products, services and operating models viable.
If competitors can access the same AI tools, automating the same tasks is unlikely to create a lasting advantage. Greater value may come from applying AI to areas where a business has distinctive expertise, customer relationships, data or operational capabilities.
AI leaders that have moved beyond pilots and scaled AI across workflows report 10% to 25% EBITDA gains (an increase in a company’s core operating profitability), according to research by Bain. Specifically in technology and telecommunications, companies further along in adoption report productivity improvements of 15% to 25%, with some approaching a 30% EBITDA uplift.
[Source: Bain & Company, PwC]
Adoption may be the bigger challenge
Redesigning the operating model can be more challenging than deploying the technology, and there can be a significant gap between how leaders and employees experience organisational change.
88% of senior leaders surveyed believed a new structure would achieve its aims, compared with just 36% of employees working within those structures. Only 22% of employees said they received sufficient support through training, coaching or tools.
[Source: Bain & Company]
Changing a workflow on paper does not necessarily mean people will work differently. For AI to deliver its intended value, organisations need to clarify new responsibilities, equip employees for changed roles and give teams enough support to adapt. Otherwise, new technology risks being layered onto old ways of working.
Who owns the work when AI does more of it?
As AI takes on more work, accountability becomes harder to separate from the technology itself. AI agents can increasingly act across systems while maintaining context to carry work forward. That creates new requirements around permissions, oversight, decision rights and human intervention.
PwC argues that governance needs to be designed into the enterprise rather than added after deployment. Its model puts clear accountability, trusted data and security alongside strategy, technology and operations.
As AI moves closer to decisions with real business consequences, organisations will need to be clear about who is accountable for the outcome, what AI systems are allowed to do and where human intervention is required.
La Royale Group Perspective
AI adoption alone is unlikely to remain a meaningful measure of competitive advantage. As access to increasingly capable AI becomes more widespread, the differentiator will be how effectively businesses redesign their operations around it.
From our perspective, this makes the quality of the operating model increasingly important. Companies that can redesign workflows, clarify accountability and connect AI investment to measurable business outcomes may be better positioned to build lasting advantage.
For investors, AI spending is only part of the picture. More revealing is whether a company is actually changing how it operates as a result.