Digital Transformation Management Explained

Introduction

Digital transformation management is no longer a side project—it has become the heartbeat of sustainable growth in 2025. Whether you operate a scale‑up, an established multinational, or a mission‑driven non‑profit, a disciplined approach to digital transformation management determines how quickly you can spot market shifts, pivot operations, and unlock new value streams. By orchestrating people, processes, and data on a common digital backbone, organisations can turn continuous change from a threat into a core competency.

Effective digital transformation management demands more than adopting new tools; it requires a deep cultural rewiring that encourages experimentation, accepts failure as feedback, and rewards iterative learning. Companies that embed digital transformation management into their DNA consistently outperform peers on speed to market, customer experience, and employee engagement. In today’s AI‑infused, regulation‑heavy, and sustainability‑focused landscape, embracing digital transformation management is indispensable for organisations of every size to stay relevant and competitive.

Digital transformation is broadly defined as “the integration of digital technology across all business domains, resulting in fundamental changes to the way a business operates and how it delivers value to its customers.”

Managing this transformation entails re-thinking corporate culture, encouraging experimentation, and embracing failure—all with the goal of becoming a better organisation than the day before. At its core, digital transformation management optimises business processes by automating repetitive, time-consuming tasks through advanced software solutions and now artificial intelligence, freeing human capital to focus on higher-value, strategic revenue generating activities. This ultimately reduces overhead costs and improves profitability. 

In a broader sense, Digital Transformation Management encompasses the strategic planning, coordination, and execution of all initiatives aimed at seamlessly integrating technology into business operations to drive continuous improvement, efficiency, and innovation.

• Rapid advances in AI & automation – Generative‑AI copilots, hyper‑automation, and low‑code tools can cut operational costs by 20–50 % and accelerate time‑to‑market by up to 36 %. Examples: ING’s digital‑lending platform halves loan‑approval time; Siemens’ “digital twin” trims engineering cycles 30 %. (templeton-recruitment.com, doit.software) If you don't get on board, your competitors will quickly outpace you, ultimately threatening your company's viability.
• Regulatory compliance & risk mitigation – New rules such as the EU AI Act, Digital Services Act, and Cyber‑Resilience Act demand rigorous data governance and model transparency; early movers turn compliance into a competitive edge. (digital-strategy.ec.europa.eu, kpmg.com)
• Sustainability & climate goals – Digital twins, IoT sensors, and AI‑optimised logistics lower energy use and emissions; Schiphol’s IoT baggage‑handling system cut equipment downtime 52 % and energy consumption 15 %. (softwebsolutions.com)
• Rising customer expectations – 76 % of global executives name customer‑experience improvement as their top digital‑transformation driver; CX‑focused programmes lift satisfaction 20‑30 % and revenue 20‑50 %. (docsumo.com, mooncamp.com)
• Operational resilience & supply‑chain visibility – AI‑powered “control‑tower” platforms provide real‑time insight, letting firms reroute shipments before disruptions hit; Maersk reports a 40 % drop in exception‑handling costs. (ft.com)
• Competitive advantage & growth – Digital leaders generate revenue and EBIT growth roughly twice that of laggards; BCG research shows digitally‑integrated operating models add 14 % to five‑year CAGR. (web-assets.bcg.com) It makes logical sense—if you can complete a task in half the time it takes your competitor, your clients will be satisfied and impressed. Additionally, this efficiency frees up time, allowing you to engage in more projects or upsell additional services.
• Talent attraction & future‑proof workforce – 57 % of organisations pursue digital transformation to boost employee productivity and support hybrid work, helping them attract top tech talent. (doit.software)

• People‑Process‑Technology (PPT) – the classic triad stressing balanced investment in skills, streamlined workflows and enabling tech.
• Prosci ADKAR Model – change‑management sequence (Awareness → Desire → Knowledge → Ability → Reinforcement) that steers individual and organisational adoption of new digital tools.
• McKinsey 7S Framework – aligns Strategy, Structure, Systems with staff‑centred elements (Skills, Style, Staff, Shared Values) to ensure technology initiatives land in a coherent organisation.
• McKinsey Three Horizons Model – helps leaders balance today’s core business, emerging bets, and future opportunities, mapping digital initiatives across time horizons.
• MIT Sloan 9 Elements → 6 Elements – research‑backed set covering Customer Experience, Operational Processes and Business Model; updated in 2020 to highlight data‑driven platforms.
• 4 Main Areas Model – focuses on Customer Experience, Operational Processes, Business Model and Data/Analytics (or Culture) as the primary transformation domains.
• Gartner Composable Business – designs organisations as interchangeable modules guided by the principles of Modularity, Autonomy, Orchestration and Discovery for resilience and speed.
• BCG Digital Acceleration Index (DAI) – maturity‑assessment tool benchmarking capabilities such as AI, data and customer journeys to prioritise investment and track progress.
• TOGAF Enterprise Architecture – structured method (Business, Data, Application, Technology architecture) used by IT teams to align technology roadmaps with business strategy.
• TM Forum Digital Maturity Model (DMM) – six‑dimension industry standard (Customer, Strategy, Technology, Operations, Culture, Data) popular in telecoms and beyond for planning transformation journeys. 

Is there a universal framework?
Not quite. These frameworks emphasise overlapping success factors—leadership, culture, data, technology, and customer focus—yet each sector and organisation starts from a different maturity level. Most companies therefore blend multiple models: for example, using ADKAR to manage change readiness, the MIT elements to audit capabilities, and TOGAF or Composable Business principles to future‑proof architecture.

1. Assess & Align

Benchmark digital maturity, map pain points, and identify competitive gaps. Start by engaging stakeholders from the C-suite to frontline employees to secure shared ownership. Before proceeding, clearly map your current business processes using process mapping techniques—such as flowcharts or value stream mapping—to visually represent tasks, interactions, and workflows.

This step is crucial for identifying inefficiencies, redundancies, or bottlenecks within existing processes. Conduct a detailed inventory of core processes, data flows, and your technology stack. The output of this stage should be a baseline scorecard and a clearly defined transformation charter, outlining targeted areas for improvement and strategic priorities.

2. Craft the Vision & Success Targets

Clearly articulate a compelling future-state narrative that aligns closely with the overarching business strategy. Translate this vision into three to five measurable North-Star KPIs—for example, improving customer experience scores by 20 points or reducing operational cycle time by 40%.

Additionally, ensure that your strategic vision aligns effectively with regulatory requirements, sustainability goals, and talent management objectives. A detailed proposal should be developed, outlining specific software or AI tools shortlisted for adoption.

These tools are selected based on their capability to streamline certain business processes, making them more efficient, cost-effective, and user-friendly, thereby driving significant improvements in productivity and overall performance.

3. Implement the Plan

In this stage, the focus shifts to implementing the transformation plan in a controlled manner. The primary goal is to integrate improvements without disrupting existing operations. Typically, this involves initiating a pilot where a complete cycle of current processes is transitioned into the newly designed, more efficient workflow.

This pilot allows teams to carefully monitor and identify any issues or bottlenecks. If the pilot cycle operates successfully without failures, the improved workflow can then be confidently adopted more broadly across the organisation.

4. Track & Iterate

This stage involves careful monitoring to ensure the newly implemented processes are functioning smoothly and delivering the anticipated improvements. Key metrics such as ease of use for stakeholders, productivity enhancements, and time or cost savings are continuously tracked and assessed.

Often referred to as the "hyper-care" phase, this period requires digital transformation specialists to remain readily available to swiftly address and resolve any issues or inefficiencies that may arise. Ongoing monitoring ensures minimal disruption and facilitates timely adjustments to maintain the integrity and effectiveness of the new workflows.

5. Scale, Embed & Optimise

At this stage, your pilot has demonstrated its value within one department or team, providing concrete proof of increased efficiency, reduced time spent on routine tasks, and enhanced overall productivity. These validated outcomes offer a strong case for obtaining approval to roll out the new workflow systematically across additional departments or processes, ultimately encompassing the entire organisation.

Furthermore, the pilot phase provides valuable data and insights that can inform future improvements. As new technologies or innovative tools become available, you can leverage this data to continually optimise and refine workflows, ensuring that your digital transformation strategy remains dynamic and responsive to evolving business needs.

Tip: Digital transformation is a flywheel, not a finish line. Re‑run this loop every 12–18 months to reassess maturity, update the roadmap and capture new value pools.

Case 1: ING Bank - Digital Lending Platform

ING Bank is a global financial institution headquartered in the Netherlands, known for its strong presence in retail and commercial banking across more than 40 countries. With a focus on innovation and digital-first services, ING serves millions of customers worldwide and is recognised for its commitment to simplifying banking through technology-driven solutions.

Problem / Situation

ING was having problems providing the service of traditional, paper‑based mortgage and small‑business lending which required multiple branch visits, repetitive identity checks and in‑person signatures. 

Processes were fragmented across channels, leading to incomplete applications, back‑and‑forth for supporting documents and long “time‑to‑yes” decisions—often days rather than minutes. Staff capacity was tied up in data entry and verification instead of advice, and customers found the experience inconvenient.

Solution

To fix this, ING implemented a cloud‑based digital lending and video‑banking platform (LiveBank) that digitises the entire application, onboarding and verification journey. Customers can schedule secure video calls, co‑browse forms with an advisor, upload documents, complete eKYC/AML checks and e‑sign—all in one flow, from mobile or desktop. 

A unified agent workspace and integrations to credit decisioning and document management reduce manual steps and keep audit trails compliant by default.

Results

• “Time‑to‑yes” for eligible loans dropped to under five minutes, with funds disbursed within 24 hours for straightforward cases.
• Within six months, engagement in the mortgage journey rose by 60%, more than 7,000 remote advisory meetings were completed, and customer satisfaction averaged 4.8/5.
• Branch staff reclaimed time for higher‑value conversations, while digital records improved compliance and reduced rework.

Key Takeaway

End‑to‑end digital journeys delight customers and free staff capacity. When advice, onboarding and risk checks live in one secure flow, banks boost conversion, reduce operational cost and create space to cross‑sell relevant products.

Case 2: Siemens & Bausch + Ströbel – Digital Twin in Machine Engineering

Siemens is a global technology leader headquartered in Germany, specialising in industrial automation, digitalisation, and smart infrastructure solutions. Bausch + Ströbel, also based in Germany, is a renowned manufacturer of high‑precision pharmaceutical packaging and filling machines. 

Together, they combined their expertise in engineering and digital technology to optimise the design and commissioning of complex pharmaceutical machinery.

Problem / Situation

Designing and commissioning complex pharmaceutical filling machines relied on physical prototypes and sequential hand‑offs. Late design changes surfaced during on‑site commissioning, stretching lead times and tying up engineering resources. Quality and ergonomics reviews were slower, and documentation across disciplines was scattered.

Solution

Bausch + Ströbel adopted a comprehensive Siemens digital‑twin toolchain—NX (CAD), Teamcenter (PLM) and Mechatronics Concept Designer—so mechanical, electrical and software teams could work in parallel on a single source of truth. Virtual commissioning in TIA Portal validated kinematics, logic and safety before any hardware was built; VR reviews let customers test accessibility and changeovers early.

Results

• Engineering efficiency targeted an increase of ~30% through parallel development and reuse of validated modules.
• Virtual commissioning shortened on‑site ramp‑up, reduced first‑article defects and improved “first‑time‑right” acceptance.
• Consistent data continuity across the lifecycle (design → build → service) enabled faster updates and remote troubleshooting.

Key Takeaways

A full lifecycle digital twin shifts risk left. By validating behavior before metal is cut, manufacturers compress lead times, raise quality and standardise platforms without sacrificing flexibility.

Case Study 3: Amsterdam Airport Schiphol – IoT‑Enabled Baggage Handling

Amsterdam Airport Schiphol is the main international airport of the Netherlands and one of Europe’s busiest aviation hubs, serving over 70 million passengers annually. Located just outside Amsterdam, it is renowned for its extensive network of global connections, innovative facilities, and commitment to operational excellence. Schiphol operates as a key gateway for passengers and cargo, with a strong focus on efficiency, customer experience, and sustainability.

Problem / Situation

Baggage‑handling system (BHS) outages triggered delays, manual inspections and mishandled bags. Traditional time‑based maintenance couldn’t always catch bearing wear, belt tension or heat‑related issues in time, and skilled technicians were stretched thin.

Solution

Schiphol and Vanderlande instrumented critical conveyors with ~300 vibration and temperature sensors and built real‑time dashboards with anomaly detection. The data fed condition‑based maintenance workflows—auto‑flagging assets for inspection, generating work orders and helping teams prioritise interventions with the biggest operational impact.

Results

• Analysis showed ~26% of incidents could be predicted and prevented with sensor data.
• The pilot saved an estimated 162 hours of manual inspections per year and avoided around 750 mishandled bags.
• Between June 2021 and April 2022, 42 sensor‑triggered inspections found issues in 83% of cases, enabling targeted fixes before failures.

Key Takeaways

Start small, measure relentlessly, and scale. Low‑cost sensors plus simple analytics can move airports from reactive to predictive maintenance—improving punctuality, customer experience and asset life.

Case 4: ASML – Cloud AI for Supply‑Chain & Engineering Analytics

ASML is a Dutch multinational and one of the world’s leading suppliers of photolithography systems for the semiconductor industry. Headquartered in Veldhoven, the Netherlands, ASML develops complex technology used by major chipmakers to produce smaller, faster, and more efficient microchips. The company is renowned for its cutting-edge innovation, precision engineering, and global collaborations across the electronics supply chain.

Problem / Situation

Exploding data volumes and model complexity outgrew on‑prem infrastructure. Teams spent hours parsing and prepping data, release cycles lagged, and security controls were harder to standardise across pipelines, slowing innovation.

Solution

ASML modernised its data and ML platform on Google Cloud—BigQuery for analytics, Kubernetes Engine and Vertex AI for MLOps—with CI/CD via Cloud Build; partner expertise helped automate ingestion, training and deployment. Fine‑grained IAM and end‑to‑end monitoring standardised governance while enabling self‑service for engineering teams.

Results

• Product release cycles accelerated from monthly to bi‑weekly.
• Query time improved by ~25×, saving each engineer about four hours per day previously spent on parsing/pre‑processing.
• Encryption, build and test phases shrank from hours to ~10 minutes, while unified controls strengthened security and auditability..

Key Takeaways

A scalable cloud‑native AI stack turns data work from a bottleneck into a force multiplier. Investing in platform capabilities (not one‑off projects) compounds productivity and keeps teams focused on high‑value innovation.

Measuring Success: KPI / ROI

Measuring key performance indicators (KPIs) and calculating return on investment (ROI) are essential components of evaluating the success of digital transformation efforts. While these calculations may vary depending on the context, the underlying principle remains straightforward: track performance before and after the implementation of changes.

Take, for example, a bookkeeping department where the invoice preparation process initially consumes four hours per day. After implementing automation or workflow optimisation tools, the process might be reduced to just 45 minutes daily—an 81% time reduction. This not only improves operational efficiency but also frees up staff time for higher-value activities.

Other departments can benefit similarly. In customer service, KPIs might include first-response time, average resolution time, and customer satisfaction scores. Marketing teams might track lead conversion rates or campaign ROI. In HR, metrics such as onboarding time or employee engagement rates can offer insight into transformation effectiveness.

To maintain visibility and accountability, it’s critical to use dashboards to track these KPIs in real time. Dashboards not only provide a clear picture of improvements post-implementation but also empower individual team members to monitor and enhance their own productivity. Ultimately, consistent tracking supports data-driven decision-making and sustained performance gains across the organisation. 

Core Skill Set

• Change management & organisational psychology
• Lean Six Sigma and process optimisation
• Agile/Scrum project delivery & product ownership
• Data analytics & visualisation (SQL, Python, Power BI/Tableau)
• Cloud, AI & automation tooling (RPA, machine learning)
• Stakeholder management, project management & persuasive communication
• Regulatory, cybersecurity & data‑governance literacy

Typical Day‑in‑the‑Life of a Digital Transformation Specialist

1. Facilitate workshops with business owners to map current processes and pain points.
2. Analyse operational and customer data to quantify potential improvements.
3. Build ROI models and present transformation roadmaps to executive sponsors.
4. Coordinate sprints with IT and vendors to deploy new digital tools.
5. Lead change‑management activities—training, communications, adoption metrics.
6. Monitor KPI dashboards and drive continuous‑improvement cycles.

What could I get started with Digital Transformational Management (career paths)?

Throughout the world, in the private and public sectors and at non-profit organisations, there is a rapidly growing demand for programmes that combine the fields of business and digitalisation. Yet, a few universities of applied sciences in the Netherlands or abroad have a programme that prepares you to become the connector between these fields.

A degree in Digital Transformation management prepares you for the following labour market positions:

• Project manager in the field of design and management of innovative ICT and data solutions and related digital transformation processes
• Process manager of more complex digital transformation processes
• Product owner in technology-based companies
• Independent expert / advisor in the field of ICT, data solutions or digital transformation management
• Entrepreneur in ICT, data management or digital transformation processes

Why study Digital Transformation Management at SRH Haarlem Campus

A Bachelor’s degree (BSc) in Digital Transformation Management at Haarlem campus will teach you to apply business-oriented ICT (Information, Communication & Technology) solutions using the latest tools and methods for data collection, analysis and visualisation. These competences are combined with strong entrepreneurial, personal and intercultural competences to enable you to lead innovative and ethical digital businesses and projects.

The programme includes business-related modules such as business strategies, management, business informatics and data science:

• In business, students will gain insight into traditional and digital business-related topics, including business strategies and models, operations and information management, human resources management, marketing, finance and accounting.
• They will also acquire competencies in management, leadership and change management that will prepare them to contribute to the required change processes in companies and organisations.
• Students will acquire knowledge in the area of business information technology and learn how to identify and manage ICT solutions. This aspect includes business information systems, software development, information management, IT management and governance, geographical information systems and global ICT & ethics.
• In the area of data science, students will become familiar with data analytics and visualisation, open and big data, business and artificial intelligence & tech ethics and Cybersecurity & ethics.