What’s next? Evolving from Digital Engineering and Digital Transformation to Intelligent Enterprise Transformation.

DIGITAL ENGINEERING?

In 2018, the DoD released the Digital Engineering Strategy, initially issued by the Office of the Deputy Assistant Secretary of Defense for Systems Engineering (DASD(SE)) (which has since evolved into the Office of the Under Secretary of Defense for Research and Engineering (OUSD R&E)). Mr. Steven Welby was the DASD(SE) who coined the term “Digital Engineering” to replace the model-based engineering terms that were prevalent during that time.? While other engineering disciplines were inherently model-based, systems engineering (the youngest of the engineering disciplines) remained predominantly document-based.? I supported the office under Philomena Zimmerman as a fellow for the American Association for the Advancement of Science (AAAS) Science and Technology Policy Fellowship (STPF) and eventually led an amazing team as a subcontractor. I developed the initial Digital Engineerng concept and led the initial strategy and implementation across the military services, industry, and academia. I studied extensively how the mechanical engineering discipline transitioned from the drafting table to?3D models. I also leveraged Industry 4.0, which revolutionized the manufacturing and industrial processes by integrating advanced digital technologies. Engineers were focused on engineering, but I remembered from my?days developing large-scale systems the importance of culture and people in adopting new systems and practices. ?

These concepts were needed not just for systems engineering, but for the overall disciplines across engineering.? The Digital Engineering goals were structured around 5 concepts 1: Developing, integrating and using Modeling across disciplines and the lifecycle phases, 2: Providing Data access to the right people at the right time to do their jobs, 3: Incorporating Advanced Technology to improve the practice of engineering, 4: Building the Infrastructure and Environments, and 5: Transforming the Culture and Workforce. I am still amazed at how the strategy has brought the community together around common goals. Now, we are focused on reimagining and implementing a future for our customers that evolves these same goals to address common challenges and to keep pace with the changing landscape.?

DIGITAL TRANSFORMATION?

Modeling as a continuum was at the heart of the Digital Engineering concept and is still fundamental to the engineering disciplines.?The community is now evolving beyond a focus on just engineering and expanding to how all aspects of the enterprise must digitally transform. This transformation will be accelerated in the era of AI, which has reshaped how people interact with technology. AI has transformed our daily lives, and ChatGPT is my go-to example of how we should question our preconceived notions about transformation. ?

We have always considered transformation to be a marathon, but ChatGPT has shown that cultural shifts for digital adoption can happen rapidly. After ChatGPT reached 100 million active users within two months of its launch, the usual transition was swift and the resistance to change was noticeably absent. While tool use typically requires specialized skills and extensive training, ChatGPT didn’t require any training. It seamlessly integrated with our daily lives and work practices. Though ChatGPT only scratches the surface of AI's potential, using it has expanded my vision of how we can reimagine our jobs in the era of AI. Today, AI is a footnote in our discussions about Digital Engineering and Digital transformation.?

A major, and possibly provocative, shift in my initial thinking is that non-engineers?should not be required to understand or interact with models. In this era of AI, we need to democratize data for the end-user so it is easily accessible to all. Now, with large language models, the collected information is no longer locked behind the languages of Python, R, SQL, statistics, and the like. Instead, it is accessible through plain English in the form of natural language querying. The ability to access and synthesize information in natural language has democratized data by making it immediately accessible to the end-user. Non-specialists can now gather and analyze data without requiring specialized training.?

Engineering disciplines have instilled rigor and vigilance into their practitioners throughout their transition to digital models by way of repeated mantras such as “garbage in, garbage out” – a warning not to let the computer think for you, but with you.?As we broaden our scope beyond Digital Engineering to encompass all aspects of the enterprise and harness the power AI, we must also address the significant challenges of explainability, data security and privacy, accuracy, and ethical considerations – just to name a few.?

However, if you are not using AI consistently in some aspect of your life, I strongly encourage you to do so. I attended a celebration last year for the Fathers of the Internet, and you could replace the fears and concerns of people not wanting to use the internet with those concerns expressed today for AI. I also think back to my math professor who wanted us to be great mathematicians without a calculator. AI is this generation’s internet on steroids – it is here to stay, and we must embrace it while addressing its challenges.?

INTELLIGENT ENTERPRSE TRANSFORMATION?

Our approaches to Digital Engineering and Digital Transformation have been stove-piped. The silver lining is that this has allowed organizations to think critically about solutions to meet their unique needs and provided?them the opportunity to explore and innovate independently. We are now at a pivotal moment where we can leverage those best practices and standardize solutions across the enterprise.?

As we evolve to end-users?interacting with the data, and the models being left to the engineers, our next frontier of enterprise transformation will need to embrace data as a continuum. This means treating data as an enterprise dynamic product that is up-to-date, relevant, accessible, and actionable for decisions. By embracing data as a continuum, we can harness the full power of AI and advanced analytics -revamping and accelerating how we do our jobs in the era of AI.??