The need for
an engineering doctrine

Engineering leaders currently face an unprecedented set of challenges: the rapid pace of technological change, the convergence of digital and physical worlds, a background of increasing societal demands, all in an environment of unpredictable external forces. These complexities require more than just strategic plans; they demand actionable principles that can guide day-to-day operations and long-term projects.

An Engineering Doctrine is valuable in making decisions in this context. It is a set of principles that capture the fundamental technological objectives and policies of an organization. And it is vital for several reasons.

Firstly, it provides clear guidance on the approach to and execution of strategic priorities. It ensures that all engineering activities align with the company's broader objectives, fostering consistency and coherence across various projects.
Secondly, an engineering doctrine helps bridge the gap between high-level strategy and day-to-day operations. By linking strategic goals to specific actions, it enables teams to focus on priorities, driving efficiency and productivity. This operational clarity is particularly important in large organizations, where the complexity of projects can often lead to fragmentation and inefficiency.
Moreover, the doctrine promotes agility and adaptability. In the face of rapid technology evolution, a flexible framework allows an organization to pivot quickly in response to new challenges and opportunities - while maintaining direction toward its fundamental objectives. This agility is a critical component of modern engineering practices, ensuring that the company can stay ahead of the competition and continuously innovate.
A doctrine also establishes a common language and set of expectations across the organization. This shared understanding encourages better collaboration among teams, reducing misunderstandings and aligning efforts toward common goals.

Our
engineering
doctrine

At Capgemini Engineering, our own Engineering Doctrine encapsulates the lessons learned from extensive experience in engineering research and development, both in running our own projects and providing research and development services in a broad range of industries. Our doctrine addresses several key areas:

1 - Rethink core-context

A clear vision of an organization’s Core and Context activities, and their criticality, remains a crucial support to effective decision making (as discussed above). The trends identified above, however, do present challenges and opportunities to re-think the implications of this model. For example,

Advancing technology has the potential to shift the boundary between automation and outsourced services in the fulfillment of Context activities.
New technologies will require knowledge of, and capabilities in, new domains in Core activities (e.g. in data-driven research).
Market commoditization may encourage the movement of activities from critical to non-critical, even as opposing pressure arises from strategic concerns and the need for robustness in global supply chains.

Gaining and maintaining clarity on the challenges and opportunities here is key.

2 - Artificial intelligence is transformational

The consumerization of AI has enabled almost anybody to have a credible conversation about how to apply Gen AI to any domain and Agentic platforms with Low Code paradigms offer the ability to implement agentic solutions with very little engineering expertise. But applying AI in general (and Gen AI in particular) to isolated use cases risks investing in approaches that will merely accelerate a process with known deficiencies. Greater benefits, at scale, are likely to be achieved by the consideration of whole business functions, and the challenges and pain points that they encounter.

The scope and context of this AI transformation should be explicitly managed, considering the maturity of the organization and its ability to absorb and exploit change. This must take a holistic view, including adoption paths in the supply chain and among partners and technology suppliers. Achieving higher levels of improvement will require a progressive increase in adoption, accompanied, if successful, by more investment.

For example, a deployment initiative can be established that uses the savings generated by tactical Gen AI ‘quick wins’ to fund more extensive deployments, which enable more substantial longer-term benefits.

AI technologies are complex, immature and rapidly evolving; questions of trust and assurance are still open, as are questions of sustainability. Remaining grounded in a sound underlying engineering process will mitigate some risks. The technology (Gen AI) is almost the last topic to discuss, not the first.

3 - Treat sustainability as a systems issue

Our environment is the largest and most complex system we influence through our work, and consideration of its behavior and interactions was a foundation for systems engineering. All our activities have an environmental impact. Thus, sustainability must be considered as a whole lifecycle issue to be integrated into every aspect of engineering practice, to ensure long-term viability and environmental responsibility.

Engineering strategies are constrained in some respects (and regulations continue to become more stringent) but such strategies can also offer solutions. For example:

Introducing model-based engineering to provide a single source of information on a product may simplify life-cycle analysis and facilitate decisions on material substitutions.
Analysis of manufacturing and logistics data enables the evaluation and optimization of supplier choices and supply chain operations, from packaging design to vehicle fleet structure.
Comprehensive models of an organization’s input materials, processes, and markets allow quantitative evaluation of strategic options, and the evaluation of their impact.

Coordinated efforts are more efficient and effective than isolated actions.

4 - Agility through model-based engineering

Agility is a key attribute of modern engineering organizations, enabling rapid adaptation to various challenges while delivering value efficiently and effectively. The ability to communicate, collaborate on, and control information is the foundation of an organization’s ability to be agile. Our ability to react to change, to develop new products fast and efficiently, and optimize cost and risk all depend on this.

In engineering, models provide the basic currency of engineering collaboration, and digital models – offering a single source of truth to a program and a continuous digital value stream – allow us to work efficiently and minimize the waste arising from converting information and managing disparate representations. Model-based engineering allows us to operate model-based enterprises and maintain digital continuity throughout our engineering activities.
For example:

Model-based systems engineering allows the formalization of our products to start early, in capturing concepts and context before a solution is identified.
Product lifecycle management and application lifecycle management tools allow the maintenance of a single coherent data set through a product or application’s life.
Digital engineering, including virtual development and validation, allows concepts to be explored, design decisions taken and consequences determined without physical prototyping and manufacture. Even in applications like aircraft certification, a combination of analytical evidence and test results will be presented to a regulator.
The existence and exploitation of common semantic views and shared ontologies allow the use of more automation, e.g. AI and ML techniques, and Multi-Disciplinary Analysis and Optimization.

Model-based systems engineering and digital continuity enable, with digital engineering, rapid and efficient development of the products that we build, and the processes and facilities we use to make them.

5 - Prioritize the role of the human in engineering

Engineering is a human activity, and human goals drive us to seek solutions that are safe, effective, ethical, and beneficial to society.

Interactions between people, engineering systems and artifacts provide the ultimate measure of our success, and no technology that neglects acceptability – including psychological and social acceptability – can expect long-term adoption. To facilitate this, our engineering activities must not only create technical products, but also provide appropriate assurance that human needs (often implicit) are met by systems that are often complex. For example, in developing autonomous systems, we can consider the psychological safety of human stakeholders as a factor that influences the public perception of the technology, and consequently the regulatory and legislative environment for future adoption.

Engineering processes, even if supported by AI, must reflect human needs and behaviors, ultimately leading to more successful outcomes and greater impact. Successful strategies will consider the contributions that both human and automated elements can make to the value chain, taking into account the strengths and weaknesses of each. For example, in supporting an engineering requirements process with Gen AI technologies, we may allow for a greater degree of (human) stakeholder review to validate machine-generated text, while still gaining overall productivity.

This approach incentivizes creativity and problem-solving, whilst ensuring that human-centered design and social acceptability are at the forefront of product development and engineering processes.

6 - Engage with key ecosystems

No single enterprise can marshal all the capabilities and resources that it needs to deliver evolving engineering value in the challenging context that we now face. Companies must build, manage and cooperate in ecosystems in order to gain access to the products, technology, knowledge and capabilities on which their business depends. This approach is often referred to as “open innovation”^[10]Successful organizations will be able to introduce new products and technology faster than others, reinforcing their market position. Partners in these ecosystems will range from idea-generating universities, research institutions and start-ups, through supply chain partners to industry peers and consortia. Wider social partnerships with market bodies, regulators and government will also be required.

A balanced approach to engagement might, for example, include (among other things):

Longer term research partnerships with academic partners and research consortia.
Joint innovation programs with customers, and with specialist start-ups.
Concrete co-development of market-facing solutions and assets with strategic partners.

7- Exploit universal connectivity to deploy world-class capabilities

In many sectors, the combination of universal digital communication and modern transport networks enables truly global competition in engineering products and services. This challenge is balanced by the accompanying opportunities to develop and deploy world-class teams and ecosystems with an equally global reach. We can build high-performance enterprises that balance the opportunities of human resources (such as educated staff), local industrial capacity, costs and proximity to clients.

Such models are exemplified by many software businesses, or for example, by the semiconductor industry – where specialist ecosystems in IP, tooling, semiconductor fabrication, and electronic product assembly provide an integrated global market, of which many end-users are unaware.

But such examples also illustrate limitations. Strategic (geopolitical) imperatives and localization requirements arising from cultural or regulatory contexts do still exist and are subject to change by forces beyond any enterprise’s control. Asymmetries in competition can arise, for example, where different regulations in product safety, environmental constraints, or employee relationships apply. These factors will constrain flows of knowledge, work and materiel to various extents, depending on sector, geography and political factors.

Given the range of possible disruptions and the speed with which they can occur, successful approaches are likely to combine the ability to adapt to changing circumstances with a range of partners who possess a global footprint. This will allow the rapid growth of capabilities, to take advantage of emerging opportunities and mitigate emerging risks.

Developing and adopting an Engineering Doctrine -
a strategic tool for tactical excellence

An Engineering Doctrine is indispensable for anyone leading engineering innovation programs. It transforms strategic priorities into actionable plans, enhances operational efficiency, promotes agility, improves communication, and enables a process of progressive improvement. By leveraging such a framework, engineering leaders can navigate the complexities of the technological landscape and drive their organizations toward sustained success and innovation.

Deploying such a doctrine requires all the elements of a strategic change: communication, metrics and feedback to drive continuous improvement, and ongoing alignment with a company’s strategic goals.

At Capgemini Engineering, we continue to refine our Engineering Doctrine to meet the evolving challenges of the technological landscape, providing a clear path for our clients to achieve their strategic goals. As engineering leaders, embracing such a doctrine can help us navigate the complexities of our roles and lead our organizations toward a successful and sustainable future.