I’ve been in the tech industry for more than 20 years. And I’ve seen many projects failing due to lack of clarity on requirements and jumping directly into implementation, lack of engagement of stakeholders, false expectations, lack of budget and many-many other factors. And I always suggested to my clients, lets do a discovery phase: to discover and understand your needs, to understand your stakeholders, to understand your ecosystem and constraints, to ask and understand.

In IT project implementation, the discovery phase stands as a pivotal stage in the journey towards success. It serves as the foundation upon which the entire project is built, providing clarity, direction, and crucial insights that guide decision-making throughout the development process.

1. I’ve seen organizations jumping directly into implementing large scale projects, spending millions of dollars and then cutting losses because the project did not go anywhere.
2. I’ve seen organizations spending tens of thousands of dollars into discovery phases and then decided not to invest anymore because the end product/solution did not fit their requirements or their budget or their stakeholders culture.
3. I’ve seen organizations spending tens of thousands of dollars into discovery phases and moving on implementing successful long running IT projects.

Understanding the Discovery Phase

The discovery phase represents the initial stage of an IT project, where stakeholders collaborate to gather requirements, define objectives, and outline the project scope. Key deliverables of this phase include a prioritized requirements backlog, estimated budget and timeframe, wireframes and user journeys, stakeholder map and documented assumptions and risks. By investing time and resources upfront to thoroughly explore and define project parameters, organizations lay the groundwork for a successful implementation.

The High Stakes of IT Project Failures

According to Gartner, Standish Group and PMI, a total of 14% of projects are canceled without any results, 31% do not meet their objectives, 43% exceed their budget and 49% exceed their agreed time frame, with factors such as poor planning, poor stakeholder involvement, inadequate requirements definition, and scope creep cited as common contributors to failure. These failures come at a significant cost to organizations, both in terms of financial losses and damage to reputation. Moreover, the fallout from failed IT projects can have far-reaching implications, impacting employee morale, customer satisfaction, and overall business performance.

Case Studies: Learning from Failure

Numerous high-profile cases serve as cautionary tales of IT project failures stemming from a lack of investment in the discovery phase. For example, the UK’s National Health Service (NHS) experienced massive cost overruns and delays in the implementation of its electronic health records system, largely due to insufficient upfront planning and requirements gathering. Similarly, the failure of Target’s expansion into Canada was attributed in part to inadequate preparation and planning, resulting in logistical challenges and operational inefficiencies.

The Discovery Phase as a Risk Mitigation Strategy

By contrast, organizations that prioritize the discovery phase are better equipped to mitigate risks and address challenges proactively. According to Gartner, projects that invest in thorough upfront planning are significantly more likely to achieve success than those that do not. By establishing a clear roadmap, defining project scope, and identifying potential pitfalls early on, organizations can minimize the likelihood of costly delays and setbacks down the line.

The discovery phase represents a critical mix in the success or failure of IT project implementations. By investing upfront in thorough planning, requirements gathering, and risk assessment, organizations can set themselves up for success and avoid the pitfalls that have plagued so many projects in the past. As the data from Gartner and other sources clearly illustrate, the cost of failure far outweighs the investment required to conduct a comprehensive discovery phase. Therefore, organizations would be wise to prioritize this essential stage in their IT project implementation process, ensuring a solid foundation for success.

While the discovery phase may seem waterfallish, it’s actually an iterative process, collecting feedback on requirements and assumptions, incorporating feedback and iterating during multiple workshops. Any implementation project following a discovery phase has an agile approach with regular sprints, demos and client feedback getting incorporated.

I used to be a decent to a good developer. Never a top coder, no matter the programming language, be it Java, .NET, PHP, or front-end frameworks. I believe I had a self-imposed limitation on how to binary and algorithmically think. And I am not ashamed to admit it. But I developed a certain skillset on top of my coding skills that made me an asset wherever I went. Call it consultancy, open minded, thinking outside of the box, creativity, authenticity…But enough of me bragging in here  Let’s go to the essence.

But before we go, spoiler alert: I truly believe that creativity, humanity, personal interactions will never be replaced by AI, and I think that we as humans can add value to businesses through the human centric actions. Anything else can be automated, generated, and replaceable. But not the human, personal touch.

Artificial Intelligence (AI) is the hot topic of the recent months, and everyone is talking about it, either about its power and innovation, either about the unknown dangers it may bring. AI has swiftly emerged as a transformative force across various industries, reshaping processes, enhancing efficiencies, and redefining job roles. And this can be seen especially in content creation, from writing well compounded emails to generating images and video clips.

One sector that’s particularly feeling the impact of AI is software development and engineering. As AI technologies advance, they’re not only revolutionizing how software is created and deployed but also influencing the landscape of software engineering jobs.

Traditionally, software development has been a labor-intensive process, requiring significant human intervention at every stage. However, AI is gradually changing this paradigm. With its ability to analyze vast amounts of data, identify patterns, and learn from experience, AI is streamlining various aspects of software development.

1. Automated code generation and unit testing

AI-driven tools can now generate code snippets, functions, modules, or even entire programs based on specific requirements. These tools leverage machine learning algorithms trained on extensive repositories of existing code to produce functional and optimized solutions.

I have played with multiple AI-based IntelliJ plugins and they’re really powerful tools in code generation. I personally see a lot of junior software development roles disappearing in the future given contextually code generation. And I’ve seen perfectly generated unit tests drastically improving speed of coding a particular module.

2. Bug detection and debugging

AI algorithms excel at pattern recognition, making them able to identify bugs and potential vulnerabilities in software code. Advanced AI-powered debugging tools can analyze code in real-time, pinpointing errors and suggesting fixes, thereby reducing debugging time and enhancing software reliability.

3. Predictive maintenance

In software engineering, AI is being utilized for predictive maintenance of applications and systems. By analyzing historical performance data and usage patterns, AI algorithms can anticipate potential failures or performance degradation, allowing developers to preemptively address issues before they escalate.

While AI promises to revolutionize software development practices, it also raises concerns about its implications for job roles within the industry.

1. Shifting skills requirements

As AI technologies become integral to the software development lifecycle, there’s a growing demand for software engineers with proficiency in AI, machine learning, and data science. Professionals need to upskill and adapt to these evolving requirements to remain competitive in the job market. Consultancy, industry knowledge and client centricity skills will need to be added on top of coding skills. Not the best coders will be highly paid, but rather the adapting engineers with business and client focus.

2. Augmented development teams

Rather than replacing human developers entirely, AI is augmenting their capabilities. AI-driven tools serve as invaluable assistants, automating repetitive tasks, and allowing developers to focus on higher-level problem-solving and creativity. This collaboration between humans and AI is fostering more efficient and innovative development processes.

3. New job opportunities

While AI may disrupt certain traditional roles, it also creates new avenues for employment. Roles such as AI system trainers, data annotators, AI ethics specialists, and AI solution architects are emerging as companies increasingly integrate AI into their software products and services.

The integration of AI into software development is inevitable, and its impact on software engineering jobs is profound. While some roles may undergo transformation or displacement, the overarching trend is one of evolution and adaptation. As AI continues to advance, software engineers must embrace lifelong learning, stay abreast of emerging technologies, and cultivate a mindset of innovation and human centricity.

The impact of AI on software development and engineering jobs is multifaceted. While it brings forth challenges and uncertainties, it also presents immense opportunities for innovation, efficiency, and career growth. By embracing AI as a collaborator rather than a competitor, software engineers can navigate this evolving landscape and contribute to the development of cutting-edge solutions that shape the future of technology