We live in a time when everything, from our entertainment, our shopping experiences, even our news feeds, is tailored to us. Algorithms curate the music we listen to, the movies we watch, and the ads we see. Yet, in the workplace, most people still follow processes designed not for them, but for the system.
A software engineer, a financial analyst, and a customer service agent might use the same enterprise workflow tool, despite needing completely different levels of autonomy, flexibility, and structure. The rigidity of traditional workflows makes no distinction between how people actually work and how systems expect them to.
This has been the norm ever since the regimented processes of industrial assembly lines. The assumption was simple. If everyone follows the same steps, the work gets done faster, more reliably, and at scale.
But does standardisation always lead to efficiency? Could there be a way to make these workflows more efficient? Perhaps the real question is not whether workflows should evolve, but why they have not evolved already.
Why Has AI Personalisation Focused on Customers, Not Employees?
Much of the existing research on AI-driven personalisation has been focused outward, on tailoring experiences for customers. We have trained AI to recommend products in a specific way, chatbots to respond in a particular manner, and create marketing campaigns that target a specific customer. Enterprises have invested heavily on personalising external interactions. But what about the people working inside these organisations?
‘Hyper-personalised’ workflows recognise that work is deeply personal. What works for one individual, one team, or even one industry does not necessarily work for another. A marketing professional who thrives on visual planning will never be as effective when forced into rigid spreadsheets. A product manager who thinks in checklists will struggle with an abstract, unstructured workflow. A legal team handling sensitive compliance matters cannot operate the same way a creative team brainstorming an ad campaign does. And yet, for years, enterprises have forced diverse teams into identical processes.
Instead of designing processes and expecting people to fit within them, workflows should be designed around people, evolving dynamically based on real-time needs, skills, and priorities.
But this shift isn’t just about how work gets done. It’s about rethinking what productivity means in the first place. ‘Hyper-personalisation’ is a shift in mindset, a recognition that workflows should be fluid, adaptive, and responsive to the needs of both the workforce and the business itself. At the heart of this transformation is artificial intelligence.
Emerging research is beginning to explore this shift, however it is still quite raw. The workplace itself is an untapped opportunity for the same level of personalisation that has transformed customer experiences.
Reimagining Work with AI
First, organisations must define the role of technology in workflows. AI cannot simply be a tool for automating existing processes. It must be an active participant in shaping how work happens. Most enterprise systems today are static as they operate based on predefined logic. A task moves from one stage to another following a fixed path, regardless of who is working on it, their expertise, or the context surrounding the task. To enable personalisation, AI systems need to function more like real-time decision engines rather than passive automation tools. They must learn from how people work, understand patterns of collaboration, and adjust workflows in response to workload, expertise, and business priorities.
Second, data must become dynamic, connected, and accessible across departments. Hyper-personalised workflows require a continuous feedback loop between systems and employees. Today, much of an organisation’s knowledge is siloed. Customer data sits with marketing, financial insights with accounting, and operational information with IT. If AI is to personalise workflows, it needs visibility across these layers, not just in isolated segments. Organisations must move beyond fragmented software solutions and embrace interconnected, AI-powered platforms that allow workflows to be shaped by real-time data rather than rigid templates.
Culturally, this requires a fundamental shift in how organisations perceive efficiency. The traditional approach values consistency over adaptability, treating standardisation as the only way to work. Hyper-personalised workflows challenge this notion by recognising that the best way to complete a task may vary based on the person doing it, the conditions in which they are working, and the tools available. Leaders must be willing to step away from the assumption that personalisation leads to inefficiency and embrace the idea that AI-driven adaptability can create a more productive, engaged workforce.
Final thoughts
For an organisation to successfully implement hyper-personalised workflows, it cannot simply deploy new software and expect transformation. It must rethink its technology stack, its data infrastructure, its governance structures, and, most critically, its mindset toward work itself. This is not an overnight change. It requires a phased transition where AI is first used to supplement workflows, gradually taking on a more proactive role in shaping them as the organisation builds trust in its decision-making capabilities.
The real challenge is not whether AI can enable it, but whether organisations are willing to move beyond the rigid systems they have relied on for so long. Those that do will unlock not just greater efficiency, but a fundamentally better way to work.
Guest article written by:
Raaj Chitnis
Gen AI Engineer, Wolken Software
Raaj Chitnis specialises in creating smart AI automations using Large Language Models and Machine Learning. He has a Master’s degree in Computer Engineering from North Carolina State University, where he focused on AI and Machine Learning. Raaj’s focus is on developing practical, efficient AI solutions that make automation easier and more powerful.
Sumanth Balakrishnan
Gen AI Engineer, Wolken Software
Sumanth Balakrishnan holds an MSc in Operational Research and Statistics from Cardiff University, UK. Passionate about leveraging cutting-edge technologies, Sumanth focuses on building innovative and efficient AI solutions that deliver real-world impact and streamline automation.