Next Generation Business Process Intelligence Shaping the Future of Work
- Danica Tupa
- Jun 4
- 12 min read
This thought-piece shares collective experience, insights, and opinions on the relationship (interdependency) between business processes and the future of work shaped by advanced technologies (including AI). It explores the symbiotic evolution of work and next-generation process intelligence, and questions if this is technological evolution, or the potential for revolution? It is intended to generate discussion and ideas as we navigate the next wave of change.
Historically jobs have been shaped by business processes. Arguably, the first wave of business process management outlined by F. W. Taylor [1] focused on improving productivity in the workplace by applying scientific methods to efficiency, standardisation, and division of labour. Whilst concentrating at the task level (with little consideration to well-being of workers), and using time and motion studies, jobs were redesigned to optimise completion of tasks. Subsequent waves of business process management have become increasingly sophisticated integrating with advancing technology, and through this re-shaping jobs and work. The adage ‘form follows function’ (although originally a principle applied to architectural design) is relevant here in that an organisation’s structure and roles, should be determined by the activities and functions (business processes) it needs to perform to achieve its strategic objectives.
Looking forward, the future of work and next-generation process intelligence are not just connected; they are evolving in tandem, feeding off each other like a neural network of human potential and machine intelligence. This relationship extends beyond efficiency; it’s about reinventing how we work, think, and create in an era where adaptability and intelligence increasingly define success. Strategist Dr Mark Powell encourages urgency in his latest book The Fifth Phase[2], to shift away from traditional performance thinking (around continuous improvement, costs savings, efficiency gains, and process optimisation) to creative, insight-led and data-enabled transformation. The next generation of transformation using AI-enabled Business Process Intelligence (BPI) and simulation will significantly shape the future of work, making businesses more adaptive, efficient, and data-driven.
In this thought-piece, we explore and challenge our thinking on how next gen advanced process intelligence (AI-enabled) might shape the future work. We have further applied this to consider potential impact and opportunities in the healthcare and infrastructure/ construction sectors (see appendices 1 and 2).
A Digital Nervous System of Work
Imagine the future of work as a living organism, a vast ecosystem of human talent blended with dynamic workflows and AI-driven automation. Next-gen process intelligence acts as its nervous system, sensing inefficiencies, predicting outcomes, and optimising performance in real time:
- Business Process Intelligence (BPI) is the neural network, mapping and analysing every workflow, from human collaboration to machine execution.
- AI-driven simulation acts as the brain’s predictive model, forecasting different work scenarios before they unfold.
- Automation functions as the muscle memory, executing tasks with precision and evolving through reinforcement learning.
In this model, work is not static; it adapts fluidly to change, self-optimising like an intelligent organism that learns, grows, and evolves. DnA Digital Technologies[3] calls this hyper-agility—not about replacing people but about amplifying human potential, spending less time on the mundane and more on what really matters.
The Era of Hyper-Agility: when work becomes adaptive
Traditionally work was based around rigidity - of structure and hierarchies, fixed processes, and linear decision-making. The next generation of process intelligence turns work into something dynamic, more responsive, and more decentralised. It enables organisations to test different strategies, predicting the ripple effects of decisions before committing to action. Smarter decision-making informed by data-driven insights and AI-driven analytics will predict outcomes, helping leaders make faster, more informed decisions.
Workflows are becoming more flexible, enabling remote, hybrid, and gig-based work models. Next-gen intelligence enables businesses to operate across geographies, time zones, and even realities (think metaverse-based collaboration). Globalisation expert Richard Baldwin describes the disruption, upheaval and opportunities of automation, artificial intelligence and robotics in his book The Globotics Upheaval[4].
Simulation models are encouraging organisations to test different scenarios before making changes, and reducing risks. Businesses can simulate disruptions (like supply chain breakdowns or patient pathways) and prepare adaptive strategies. Advanced BPI tools such as BusinessOptix[5] analyse business processes in real time, identifying inefficiencies and bottlenecks, whilst simulating changes and opportunities.
Instead of employees adapting to rigid job roles, work is adapting to the worker, matching skills, preferences, and business needs in real time. The Deloitte AI Institute 2023 research[6] makes a clear differentiation between work, jobs and tasks, maintaining that advancing technology is not directly replacing jobs; but changing tasks and skills used to get work done. Not only is this increasing the capability for on-demand skills, it is also upskilling employees and enhancing the employee experience. AI-powered process intelligence is reducing mundane tasks, increasing employees’ opportunity to focus on creative, high-value work. Simulations can train employees in different scenarios, improving skills in real time. For example [7]Airbus’ development of digital twin models is revolutionising its production systems. By simulating tools, robots, workflows, and supply chains, it predicts the performance of designs under differing scenarios. It uses interconnected devices - tablets and smart glasses - to provide virtual training for operators before even stepping onto the shop floor.
In this paradigm, work is no longer predefined but is an evolving ecosystem of talent, technology, and intelligence, shifting seamlessly between automation and human ingenuity. It enables organisation agility, to pivot quickly in response to market changes. DnA Digital Technologies describes this as ‘The Synergy of Five’ - the fast lane to hyper-agility. In the race to remain agile, efficient, and customer-focused, one thing is clear - it’s not one silver bullet but the powerful combination of five strategic forces: Rapid Process Discovery, Digital Process Automation (DPA), Generative AI & Machine Learning (ML), Hyperautomation, and Continuous Business Improvement (CBI).
Together, these elements form a self-reinforcing cycle that helps organisations uncover inefficiencies, automate intelligently, and continuously evolve - fast-tracking their journey to hyperautomation. [see appendix 3 below]
From Efficiency to Imagination: A New Creative Renaissance
The relationship between the future of work and next-gen process intelligence goes beyond the efficiency gains of automation; it’s about unlocking human imagination:
- AI as a Thinking Partner: Instead of replacing jobs, AI-driven BPI augments human creativity, automating routine tasks so workers can focus on innovation.
- Digital Twins of Ideas: Just as digital twins is used to simulate physical environments, they will be used to prototype business strategies, creative projects, technology deployments, transformational change, and even entire industries before they exist.
- Intelligent Workflows and Intelligent Workers: As process intelligence becomes more autonomous, workers will shift from executing tasks to designing intelligent workflows, shaping the very fabric of business.
This is more than just an evolution of work, it’s the birth of a new way of thinking, where technology doesn’t constrain creativity but expands its possibilities. Daugherty & Wilson[8] describe this as the ‘Missing Middle’, identifying those blended (hybrid) roles between human-only activity, and machine-only activity, where humans and machines work together to complement and exploit what each does best, where humans develop, train and manage AI applications, and in turn, machines augment and supercharge the capabilities of humans.
Future Vision: The Conscious Organisation
Looking to the future, businesses won’t just operate, they will sense, think, and evolve like intelligent entities. Work will be proactive with AI predicting and solving problems before they emerge. Organisations will function as living, learning systems, continuously adapting in real time; and the workforce will transcend mechanical productivity, stepping into a realm of creativity, strategy, and purpose-driven innovation. This is further emphasised in a paper[9] published by the Aspen Instituted and Accenture on advancing the skills of the missing middle. It talks about exiting the ‘Information Era’, where data was produced to inform and improve processes and products, moving into the ‘Experience Era’, with humans using their unique skills to deliver more personalised and adaptive experiences. This new ‘Experience Era’ needs people with social and leadership abilities who can empathise, imagine, improvise, and show good judgment, who can train AI systems and make sense of the data generated by AI. In this new reality, process intelligence is not just a tool, it’s the foundation of a world where work, technology, and human potential are seamlessly intertwined. The future of work will be more intelligent, automated, and agile. BPI and simulations will enable businesses to anticipate changes, improve efficiency, and create better work environments, ultimately leading to higher productivity, innovation, and resilience.
In summary, the future of work is where technology amplifies human ingenuity, making work smarter, safer, and more impactful than ever before. Work that thinks, learns, and evolves:
Workplaces that optimise themselves
AI that supports — not replaces — human workers.
A shift in proactive decision-making.
Organisations embracing process intelligence aren't just surviving; they're thriving, ready to conquer the challenges of tomorrow and transform them into opportunities for growth and excellence.
[1] The Principles of Scientific Management, F. W. Taylor, 1911
[2] The Fifth Phase, an insight driven approach to business transformation, Dr Mark Powell, 2023
[4] The Globotics Upheaval, Globalization, Robotics and the Future of Work, Richard Baldwin, 2019
[6] Generative AI and the future of work The potential? Boundless, Deloitte AI Institute 2023
[8] Human + Machine – Reimagining Work in the Age of AI, Paul R. Daugherty & H. James Wilson, 2018
About the contributors:
Robert Williams is an independent management consultant, advisor, member of the acumen7 network, and Non-Executive Director who helps private and public sector organisations worldwide to deliver business transformation from strategy to execution. He brings curiosity, creativity and deep insight to his expertise in complex organisational and technology-led change. He collaborates with organisations on the future of work, to implement next-generation business process intelligence and AI-enabled technologies, enhancing leadership alignment, improving strategic decision-making, and automating change management to accelerate impact and effectiveness
Alfred Pawson is CEO at DnA Digital Technologies. With over 25 years of experience in the information technology sector, he brings a deep passion for process intelligence and business optimisation. He has helped countless organisations align strategy with execution by delivering transformative business systems that meet both strategic and compliance goals. Renowned for his ability to bridge the gap between business and IT, he excels in target operating model design, digital transformation, and enterprise-wide change initiatives. A driven innovator, he thrives on generating bold, practical and fresh ideas to solve complex challenges and unlock real value.
Appendix 1: The Future of Work: potential opportunities in Healthcare
Hospitals as Self-Optimising Systems: Imagine a hospital that thinks for itself, with fewer delays, higher efficiency, and patients getting the right care at the right time; a hyper-intelligent system that optimises hospital workflow and patient journey management:
Predicts patient surges before they happen.
Allocates staff dynamically based on real-time need.
Automates and optimises supply chains, ensuring critical medicine or equipment is available in the right quantities, at the right time, all the time.
In the future this will be enabled through:
AI-powered Process Intelligence (BPI) continuously monitoring hospital workflows, spotting inefficiencies and optimising processes in real time.
Digital twin simulations evaluating new staffing models, reducing burnout and improving care delivery.
Predictive analytics forecasting ED demand, operating room usage, ICU occupancy, and patient flow/discharge, allowing proactive adjustments.
The AI Care Assistant: Today care workers spend too much time on administration and repetitive tasks, reducing their time to focus on human-centred care. A future vision of empowered (AI augmented) care workers focusing more time with patients, not paperwork, leading to better outcomes and patient experience:
Patient scheduling and records using automated data input and analysis.
Symptom analysis and preliminary assessment, assisting clinicians with more informed diagnosis.
Moving away from one-size-fits-all treatment plans and suboptimal patient outcomes to more personalised treatment plans, using real-time patient data and insights.
This will be enabled by:
AI-driven decision support systems providing instant recommendations to clinicians.
Simulation tools determining options for care pathways and optimising treatments before implementation.
Voice-activated AI tools to draft documentation, reducing manual data entry by doctors and nurses.
More predictive and preventive healthcare: From treatment to prevention – a shift from reactive treatment to predictive analytics helping doctors make data-driven decisions for individualised treatments leading to more proactive health and care management in the right place, reducing hospital inpatient stays and reducing costs:
AI analysis of patient lifestyle, medical history, and genetics helping predict illnesses or conditions before they arise.
Patient wearables e.g. smart biosensors that alert doctors before a condition worsens.
Simulations modelling different intervention strategies, allowing for more personalised preventive care.
Optimising medical supply chains and drug distribution: Reducing or removing supply chain disruptions leading to medicine shortages and inflated costs, using:
BPI to monitor supply chains in real time, identifying inefficiencies and forecasting shortages.
Utilising simulation models that evaluate alternative distribution strategies to optimise inventory. AI-driven demand forecasting ensures just-in-time medical supplies and reduced waste.
Appendix 2: The Future of Work: potential application in the Construction & Infrastructure sector(s)
Smart, Self-Managing Construction Sites: Imagine construction sites in the future that think for themselves, automatically adapting to challenges, minimising delays, reducing costs and creating safer jobsites:
AI-driven supply chain intelligence - materials are ordered and delivered automatically based on real-time site data.
Digital twins of entire projects, simulating different weather conditions, workforce availability, material costs and other variables to find optimal project plans.
Next-gen BPI continuously analyse project timelines, adjusting them dynamically.
Drones and IoT sensors monitor real-time progress and flag inefficiencies; AI-driven predictive maintenance ensures that machinery is repaired before it breaks down
AI-powered risk management, identifying safety hazards and performance issues before accidents or events arise.
Automating routine work and elevating skilled labour: A future vision where skilled workers focus on complex problem-solving, while automation handles repetitive work. Robots handle repetitive construction tasks like bricklaying and concrete pouring, AI allocates workers dynamically, shifting teams based on demand and expertise, and VR-powered simulations train workers before stepping onto the job site, reducing mistakes and improving safety, enabled by:
Process mining tools analysing past projects to predict future labour needs.
AI-powered resource allocation ensures teams are always working at peak efficiency.
Exoskeletons and robotic assistants reduce physical strain, making construction safer.
AI-driven simulations create virtual safety drills for workers to train in lifelike scenarios; real-time BPI dashboards monitor safety compliance and predict potential hazards; and, digital twins of sites help test safety measures before implementation.
Sustainable and Smart Infrastructure - The Conscious City: Next-gen infrastructure is alive - it learns, adapts, and optimizes itself. Imagine infrastructure that is sustainable, cost-efficient, and resilient, where:
AI-driven traffic flow analysis reduces congestion utilising BPI and digital twins to simulate urban layouts, optimising transport and utilities.
Smart energy grids dynamically adjust power usage based on real-time demand using AI-driven climate modelling helping cities adapt to extreme weather conditions.
Buildings auto-adjust energy consumption, reducing waste.
Smart construction planning and risk mitigation: Reducing project risks, improving scheduling, and minimising financial losses due to poor planning, delays, cost overruns, and unforeseen risks
BPI tools analyse past projects to identify causes of delays and budget overruns.
Simulation models predict project outcomes under different conditions (weather, labour shortages, etc.)
AI-based scenario testing helps optimize materials, labour allocation, and timelines.
Appendix 3: DnA Digital Technologies: The Synergy of Five: Your Fast Lane to Hyper-Agility
In the race to remain agile, efficient, and customer-focused, one thing is clear: It’s not about one silver bullet - it’s about the powerful combination of five strategic forces: Rapid Process Discovery, Digital Process Automation (DPA), Generative AI & Machine Learning (ML), Hyperautomation, and Continuous Business Improvement (CBI).
Together, these elements form a self-reinforcing cycle that helps organisations uncover inefficiencies, automate intelligently, and continuously evolve - fast-tracking their journey to hyper-agility.
1. Rapid Process Discovery: See the Full Picture
How well do you really know your business processes? Rapid Process Discovery removes the guesswork. Using tools like process mining and simulation, it analyses system data (from ERP, CRM, etc.) to reveal exactly how tasks flow across teams, systems, and departments.
Forget whiteboards and assumptions - this is data-driven clarity. You’ll uncover hidden bottlenecks, deviations, and delays you didn’t know existed. Even better, simulation features let you test process changes before rolling them out, aligning stakeholders and reducing risk. It’s the foundation of smart automation strategy.
2. Digital Process Automation (DPA): Automate the Obvious
With insights from discovery in hand, DPA helps you transform routine, rule-based tasks into automated workflows. Thanks to low-code/no-code platforms, process owners and business analysts can now build applications and workflows themselves - without waiting months for IT.
From onboarding new customers to processing invoices, DPA streamlines repetitive tasks while enforcing compliance and governance. It handles the structured work so your people can focus on higher-value activities that require critical thinking and human judgement.
3. Generative AI & Machine Learning (ML): Think Beyond the Rules
While DPA automates the predictable, AI and ML bring intelligence to the table. ML analyses patterns in your data to anticipate issues, recommend actions, and optimise decisions. Whether its forecasting demand, identifying fraud, or segmenting customers, ML helps you act before problems arise. Generative AI goes one step further by creating content - emails, reports, code snippets, even workflow suggestions. When paired with DPA, AI can auto-generate dynamic processes, escalate exceptions, and personalise communications - all in real time. This unlocks a whole new level of automation.
4. Hyperautomation: Make It Seamless
Hyperautomation is where everything comes together. It’s the coordinated use of RPA, AI, process mining, and integration tools to automate entire end-to-end processes, not just isolated tasks. Picture RPA bots completing system tasks, AI interpreting unstructured data like emails, and orchestration engines managing the flow - all integrated in a single ecosystem. It creates a digital workforce that complements human effort, boosting speed, accuracy, and consistency across departments. And with built-in analytics, you can monitor performance, identify new opportunities, and fine-tune your operations as you go.
5. Continuous Business Improvement (CBI): Always Getting Better
Even the best systems need fine-tuning. That’s where CBI comes in. It’s not a one-off project - it’s a mindset. By embedding feedback loops across the automation lifecycle, businesses can constantly review what’s working, what’s not, and what could be better. Regular performance reviews, backed by both data (cycle times, error rates, cost savings) and qualitative input (user feedback, business outcomes), ensure your automation efforts stay aligned with business goals. CBI also encourages collaboration, breaks down silos, and builds resilience in a world where change is the only constant.
One Cycle, Endless Possibilities
Each pillar plays a role, but the real power lies in how they work together:
Discovery finds the problems worth solving.
DPA streamlines the solutions.
AI/ML adds intelligence and adaptability.
Hyperautomation scales it all enterprise wide.
CBI keeps it fresh, relevant, and evolving.
It’s not just about technology - it’s about transformation. This synergy enables companies to operate more efficiently, adapt faster, and deliver more value to customers.
Making It Happen: Strategic Priorities
To tap into this potential, leadership must focus on more than just tools. Success depends on how these elements are adopted and embedded:
C-Suite Sponsorship: Executive backing is essential. Make hyper-agility and hyperautomation a strategic priority with clear objectives and governance.
Collaborative Teams: Build cross-functional teams that include process experts, data scientists, IT, and change managers.
Modular Technology: Choose platforms that are flexible, interoperable, and support low code/AI capabilities out of the box.
Skill Building: Invest in training staff on process discovery, automation tools, and data literacy.
Culture & Communication: Support change with strong messaging, leadership engagement, and a digital-first mindset.
Meaningful Metrics: Measure impact with KPIs that go beyond cost - look at speed, quality, user satisfaction, and agility.
Copyright © Robert Williams 2025 | Copyright © Alfred Pawson 2025
