Techflow’s Adoption Engineering Framework


The Adoption Engineering framework is a powerful tool created by TechFlow and derived from years of experience on successful large-scale project delivery. The framework helps development teams rapidly identify, build, and deliver modern software that maximizes business and mission value to users. It balances people and technology requirements while promoting software product adoption that avoids the waste of building the wrong thing.

Customers and users of an organization’s and mission’s software and systems expect their business needs to be met or problems solved in an intuitive and efficient way. They expect rapid change and don’t have the patience for organizations to figure that out. Organizations must not only speed the delivery of software but ensure real value is delivered.

TechFlow’s Adoption Engineering framework is a way to improve how value is identified, solutions are designed, and software is delivered using modern architecture and ways of working. The framework is compatible with modern methodologies and processes, such as Agile, Scrum, SAFe, DevSecOps, UX/CX, Lean, and cloud-centric solutions. The framework improves the product team understanding of the problem to be solved leading to greater engagement, productivity, and quality. It also avoids the waste of building the wrong thing, eliminates unnecessary work, is responsive to feedback and usability, and can quickly pivot, leading to faster time to market for software solutions.

The Adoption Engineering framework strikes a balance between user needs and technology implementation. This approach allows development teams to rapidly deliver value where it can only exist: in the hands of users.

The framework is comprised of seven steps that focus on People, Technology, and the goal of Adoption.

Our focus on people helps organizations to create software that solves a user need in an intuitive way. The first step is to target the need which, through research and interaction, identifies what users will require from the system (what business problem are we trying to solve). The next step is to understand the user, including how they interact with the system and the identification of barriers or problems that exist in their experience. Lastly, we design for usability through experimentation, design thinking, and hands-on usability testing to ensure software improves outcomes.

TechFlow’s framework also focuses on technology, helping organizations to field software faster by using modern architecture, design patterns, and opportunities for automation. By using rapid delivery methods, software teams eliminate wasted time in development by using agile methodologies, modern architecture, and repeatable cloud practices. We augment development, which increases developer productivity and development velocity through automated tools, plugins, and analytics that identify and remove impediments and technical debt. As software is being delivered, we automate deployment and operations by using Infrastructure as Code, Site Reliability Engineering, and other practices to eliminate manual effort.

Finally, TechFlow’s approach maximizes value by implementing steps to further adoption and incorporate metrics to continuously measure business and customer experience metrics to iteratively improve outcomes, such as satisfaction, engagement, revenue, transactions, etc.

TechFlow is striking the balance between people and technology by applying our Adoption Engineering framework to achieve successful outcomes and maximize software value.

Share:

Facebook
Twitter
Pinterest
LinkedIn
On Key

Related Posts

Let’s Celebrate Math! By Robert Baum, TechFlow CEO

April is Mathematics and Statistics Awareness Month! The goal of this recognition is to increase public understanding of and appreciation for mathematics and statistics worldwide. At TechFlow, math is the backbone of our innovation. We harness it to decipher data, unearth trends, and craft groundbreaking solutions. The realm of Artificial Intelligence (AI) is deeply rooted in mathematics—principles like linear algebra, calculus, and probability breathe life into AI, enabling it to make smart decisions. This mathematical foundation extends to machine learning, where data science and deep learning leverage complex mathematical frameworks for problem-solving. Concepts such as vector calculus and optimization are indispensable in driving advancements in machine learning. Moreover, programming and logistical operations within our company draw upon mathematical skills for efficiency and precision. In essence, math empowers us to describe, analyze, and resolve the challenges we tackle daily.