Digital Twins: From Modelling to Business Impact
Digital twins are increasingly recognised as a strategic capability; but they are also frequently misunderstood, oversimplified, or dismissed as a “Gartner buzzword”, which creates confusion about their purpose and potential.
This article sets out to describe their purpose and objectives, and to generalise three broad categories of digital twin approaches, each with its own audience, tooling, and value proposition.
According to Gartner, a digital twin is a concept within a Business Operations System (BOS): an online model that is continuously updated by people across the organisation and becomes more accurate with each input. It acts as a central knowledge base and management tool for enterprise assets and related strategy, performance improvement, and architecture; making it easier to run calculations, simulations, and predictions to guide targets and performance metrics.
We commonly see three broad categories; let’s explore them:
Phase 1: Ad-Hoc Twinning; Patch workers Getting Started
Many organisations begin their digital twin journey driven by a business need: a specific operational problem where organisational data, real-time data, and metadata start to become a barrier. The first step is often simply to locate existing data sources, assembling whatever is available into spreadsheets and lightweight integrations. This is a grassroots effort, driven by operational needs and built using familiar tools.
In a recent post, Jessica Jones describes how she created a digital twin of a manufacturing shop floor using only Microsoft tools and IoT devices. The solution was pragmatic, built with tools already available to her team:
- Visualisation using Visio
- Data management with SharePoint
- Simple interfaces powered by existing tools, with data captured via CSV files
This typifies how many architects first “get something done”. It is not framework-driven; it relies on readily available tools with minimal financial investment though substantial internal effort. The focus is on solving an immediate challenge, without certainty about how it will scale. It is typically built by those closest to the business, often without formal architecture experience. This phase delivers quick wins, but remains limited in scalability, structure, and strategic alignment.
Phase 2: Architects as Builders
As organisations mature, teams begin adopting smarter tools to model disparate data, generate dashboards, and onboard information into a unified platform more quickly. This is where general-purpose architecture tools like MooD (and similar case tools) become powerful.
MooD stands out by simplifying and accelerating the process. It acts as a case tool that connects disparate systems and data into a central hub, supporting both cloud and on-premises setups. It enables bespoke information models while integrating with existing online data sources without full restructuring.
It can be configured for a wide range of needs; from operational dashboards to strategic planning. In this phase, architects take ownership of the modelling, structure, and delivery: iterating information models, defining relationships, and ensuring consistency. This is especially relevant in engineering and finance sectors, where measurable value emerges quickly.
Phase 3: Digital EA as Twinning; Architects as Transformation Drivers
The third maturity step marks a fundamental shift: prioritising speed of onboarding and enabling business change, specifically to solve Enterprise Architecture. This is critical for strategy execution, risk and investment management, and scaling business architecture.
Much of the information model here is consistent across industries. Today, we no longer need to start from scratch and model; we have reached a point where ready-made strategic platforms deliver intelligence out-of-the-box. This is precisely what we offer with Next-Insight; a business-led digital twin solution needing only your data and a coach to guide the strategy planning and improvement initiatives.
To solve the the execution challenge of strategy and digital transformation, an intelligent solution with strategic planning, digital governance, and IT optimisation, it is too time-consuming to model this from scratch. Instead, it is faster to adopt a strategic intelligence platform such as Next-Insight to avoid lengthy setup and then start accelerating value realisation.
With this approach you adopt a pre-configured digital twin that reflects best practice, supports collaboration, and scales effortlessly, reducing effort and shifting focus to enabling efficiency and effectiveness.
Next-Insight is a ready-made web application supporting strategy, planning, optimisation, and transformation. It solves EA with a digital twin model of your organisation; easy to onboard, easy to share, and built for co-creation. Integrated with AD, HR systems, finance platforms, ITSM tools, and other real-time systems via standard APIs, it enables enterprise-wide decision-support, compliance, and continuous improvement. It may be led by Enterprise Architects or the CIO office, but it is designed to be usable by anyone with a role in delivering outcomes.
Why Each Phase Matters
Each phase serves a distinct audience and level of impact. The most advanced approaches enable faster execution; exactly what Next-Insight delivers: a digital twin focused on strategy, optimisation, and business outcomes.
- Ad-hoc twinning remains a practical starting point for many.
- For organisations focused on engineering or bespoke modelling, general-purpose tools like MooD offer strong capabilities. But for those aiming to solve EA at scale, a strategic platform like Next-Insight accelerates outcomes.
- But if you want to solve the enterprise architecture space specifically, no need to take the long route, and onboard using fast-track with ideally Next-Insight. The faster you onboard your twin, the more value you unlock for senior stakeholders. Higher maturity brings more structure, integration, and alignment. And the better you onboard and engage people, the more sustainable your transformation.
The Role of Automation and AI in Digital Twins
As digital twins evolve, automation and artificial intelligence (AI) increasingly play a central role in unlocking their full potential. While digital twins begin as structured models of organisational data, their true power emerges when intelligence is layered on top, enabling predictive insights, automated decision-making, and adaptive behaviour.
AI enhances digital twins by:
- Forecasting outcomes based on historical and real-time KPI’s
- Identifying anomalies and prompting for corrective actions
- Optimising processes through continuous learning
- Supporting strategic planning with better options for scenarios
This convergence of digital modelling and intelligent automation transforms digital twins from static representations into dynamic engines of business improvement. The more structured and integrated your twin, the more effectively AI can operate in addition to it; enhancing its predictive and adaptive capabilities; making the case for platforms like Next-Insight, which are designed to support collaborative optimisation and planning.
In summary
Digital twins are not about diagrams; they are about online help to drive business outcomes. Whether you are starting or scaling, digital twinning is a journey. We are here to guide and help you move from modelling to meaningful impact, faster and safer.
Let’s connect if you require assistance converting to a digital twin, that is better looking, more consumable, and simpler to succeed with. Book a demo here.
