The Executive’s Guide to Gen AI

Generative AI is coming to the enterprise. Are you ready??

Imagine an engineer sitting with a product owner as the product owner describes a new system that they want to build. The developer takes notes and together they iterate though a mockup on the white board of what a user interface looks like. When satisfied with the mockup, the engineer takes a picture and loads it into an AI chatbot along with the notes he or she took. A few minutes later after collaborating with an AI bot, the engineer has a working prototype of the demo aligned to company standards and controls up and running and starts iterating with the product owner to make any necessary changes. At the end of the meeting, the product owner and the engineer agree on the initial requirements and the engineer can now build a production quality system. When was the last time that much was accomplished in a single meeting???

Our research reveals that early adopters of Gen AI are adopting use cases like these and are already achieving early returns of 10% improvement in productivity. We estimate that this number will grow to 25-30% across the entire software development lifecycle as enterprises gain maturity and expand adoption. This is why many enterprises are taking a hard look at how Gen AI can help bring more value to their customers faster.?

What is Gen AI??

The pace of innovation is increasing at a velocity we have never seen before. Leaders are challenged to balance adopting the latest technologies with running their business. Every year executives are asked to deliver more value faster while doing it with less budget (or funding, whichever makes more sense). One of the biggest costs for delivering software is people. How can you get more out of your workforce? How are you planning to deliver software in a new way so engineers can bring more value to market faster and for less costs??

Enter Generative AI (Gen AI). Gen AI is more than the next shiny object to add to the toolbox. Gen AI can boost your workforce’s productivity as much as 10 times if implemented correctly. Are you preparing for a revolution in software development? In this paper we discuss how to navigate the hurdles that come with implementing Gen AI to help your team deliver “Better, Faster and Cheaper.”??

Generative AI marks a pivotal shift from traditional programming paradigms to an era where artificial intelligence plays a central role in the development process. This transition is not merely a technological leap but a conceptual revolution, reshaping the very foundations of how developers engage with software creation.?

Since the mainframe days there have been numerous products that attempted to automate the coding process. These previous attempts at code generation failed because they employed a one way “conversation” where developers supplied data to describe the task at hand and the code generators spit out some very basic and generic (context devoid) code.??

With Gen AI, developers now have a two-way conversation with an AI bot and continuously collaborate with it to produce high quality code requiring very little customization. It is like paired programming but with an AI bot instead of another human. But this paired “developer” is an architect, a historian, a full stack engineer of any computing era, a tester, a database architect, an industry expert and is knowledgeable of about any skillset required for a team to get the job done. This allows your developers to become exponentially more productive and can empower your business to produce more output faster, with higher quality, while requiring less resources to do it.?

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How can Gen AI help??

Generative AI is set to revolutionize traditional coding practices. By leveraging advanced algorithms and vast datasets, AI tools can automate significant portions of the coding process. This does not just mean generating boilerplate code; it involves applying sentiment, creating complex, functional code structures, offering solutions to intricate problems, and even optimizing existing code bases for better performance and efficiency.?

One of the most immediate impacts of AI in code generation is the substantial enhancement of developer productivity. AI tools can rapidly generate code that might take human developers hours or days to write. This acceleration allows developers to focus more on strategic, creative aspects of projects, such as designing better user experiences, conceptualizing innovative features, or solving more complex problems.?

Generative AI in code generation can also act as a bridge in the skills gap, especially in areas where there is a scarcity of expert developers. For instance, AI can assist in generating code for specialized fields or newer technologies where developer expertise might be limited. This capability enables more teams to undertake ambitious projects without the constraint of finding highly specialized talent. Gen AI can supplement the domain knowledge of less experienced engineers and empower them to build solutions that previously required them to be dependent on many other experts.??

Another significant advantage of AI in code generation is its potential for enhancing quality assurance and reducing errors. AI tools can analyze vast amounts of code to identify patterns that might lead to bugs or vulnerabilities, thereby proactively suggesting improvements or corrections. This capability could lead to a notable increase in the overall quality, reliability of software products, and improved profitability to build and operate software products.?

But why now, you may ask? As companies become more reliant on producing software, the speed and efficiency of delivering business value through software has never been more important. The companies that embrace Gen AI early can leapfrog their competition in value generation, thus creating more opportunities to grab market share. At the same time, the early adopters will be able to exponentially produce more value per employee and even reduce staff while delivering more at the same time.?

How Does GEN AI Bring Value to Workforce??

A few years back there was an industry discord about 10x engineers. The term "10x engineer" refers to a software engineer who is thought to be as productive as 10 others in his or her field. The concept is based on the belief that certain individuals have exceptional talents or skills that allow them to achieve significantly more than their peers over the same period.??

Most engineers’ skillsets are T-Shaped, meaning they are deep in one domain and broad in many and some engineers are Pi-shaped meaning they are deep in two domains and broad in many. But the 10x engineer was said to be deep in all domains, an expert at all phases of development and the entire technology stack, as well as an industry expert and capable of doing all this autonomously.?

The 10x engineer concept was a very controversial topic and many critics mocked the notion of 10x engineers and referred to them as unicorns.?

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But if a solid engineer is armed with modern Gen AI technology, is the possibility of a 10x engineer that far-fetched? If a professional services company leverages Gen AI at the core of all their development, could they 10x their competition? At Deloitte we think so which is why Gen AI is at the core of everything we do in the software development lifecycle.?

That is one of the main value propositions we see in Gen AI. An engineer, skilled at interacting with Gen AI technologies can accelerate the speed at which they bring value to market. Gen AI can make the promise of 10x engineers a reality. Armed with 10x engineers (and 10X product owners, 10X testers, 10X architects, etc.), businesses can bring value to market faster than ever, with higher quality and with fewer resources. What business would not sign up for that??

How Do I Navigate This Revolution??

This all sounds great but there is no easy button for adopting Gen AI. Preparing for an AI-integrated future in software development requires a shift in mindset and approach. Developers and organizations need to embrace continuous learning, adaptability, and openness to new ways of working.??

New Talent Models?

The emergence of generative AI (Gen AI) in software development is not just transforming the tools and technologies used by developers; it is fundamentally reshaping the skillset required in this new era. This shift is characterized by the integration of AI capabilities into every aspect of the development process, requiring developers to adapt and evolve their skills accordingly. The future will demand a harmonious blend of technical prowess, creativity, and an ability to interact effectively with AI systems.?

Developers will need to adapt and shift from writing code to collaborating with AI technologies to produce working code. Effective communication with AI systems, known as prompt engineering, is becoming a highly sought and crucial skill. Developers need to learn how to instruct and guide AI tools accurately and efficiently. This skill involves not just technical know-how but also creativity and strategic thinking, as developers must translate complex requirements into prompts that AI can understand and execute. The output that the AI tools generate is only as good as the prompt engineer who is collaborating with the tool.?

There is a fear among some developers that AI will take their jobs. The reality is, those that master prompt engineering and can get the most out of the AI tools and systems will be more valuable than ever, like the 10x engineers we always dreamed of. But those who refuse to learn the new skills will find it incredibly challenging to market themselves in the years to follow.?

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Challenging Traditional Software Development Norms?

Gen AI impacts more than just the engineers. Gen AI is reshaping the structure of software teams and challenging the age-old methodologies of software development. A prompt engineer can instruct Gen AI to create supporting documentation, architecture diagrams, automated testing, search for security vulnerabilities and much more. This shift has implications for team dynamics. Teams in the future will be less specialized because engineers can leverage AI as a specialist in most domains. This can lead to an optimization in team size while increasing in output/value.??

In traditional software development, teams were often divided by specific functions or expertise areas – front-end developers, back-end developers, database experts, etc. However, with AI taking over more routine tasks and offering insights across various domains, the lines between specialized roles are blurring. Teams are becoming more fluid and integrated, with members expected to interact with AI tools and collaborate across different areas of expertise. This integration fosters a more holistic development approach, where the team's collective skills are leveraged more seamlessly and efficiently.?

Generative AI is becoming a central player in team collaboration, acting as a facilitator and enhancer of the development process. AI tools can provide real-time insights, automate mundane tasks, and even assist in decision-making processes. This allows teams to focus on more complex and creative tasks, increasing overall productivity and innovation. AI-driven collaboration tools are also enabling more effective remote work, breaking down geographical barriers and allowing teams to work together more efficiently from different locations.?

Although Gen AI might not totally replace tasks like test automation, technical writing, project management and many others, it may reduce the number of resources required to deliver those tasks.??

Traditional methodologies like waterfall and agile were designed for managing teams of specialists and relied heavily on producing documents or user stories before any development can get done. These methodologies never accounted for a 10x software delivery team paired with Gen AI tools that can deliver working code faster than the legacy methodologies could produce an approved requirements document. Leaders need to “unlearn” how we traditionally managed projects and create modern practices for this new era.?

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What Does Tomorrow Look Like??

We believe that initially enterprises will use Gen AI to accelerate their existing agile processes by automating user story creation from meeting notes, drawings and recordings and expediting the process from ideation to a working prototype. The image below shows how the early trailblazers are incorporating Gen AI into today’s modern agile development.?

In many agile shops, the projects start with a laborious process of creating epic stories and then breaking them down into smaller user stories with acceptance criteria and story points. This process was created to coordinate how to deliver working software with teams of engineers, a scrum master, and a product owner. Another reason for this method is to measure productivity in terms of velocity and throughput. AI tools can assist in producing all this information for the scrum master but the question going forward is, “Does it still make sense to work this way in Gen AI development world?”??

What if working software can be produced in less time than it used to take to build and approve user stories? I predict within 5 years the current methods for delivering software will become outdated and hinder the development in Gen AI development. Initially, Gen AI will be incorporated into our existing agile practices, but soon you should expect new methodologies to emerge and replace these decades old methodologies with a more lightweight and productive set of processes.?

Managing Change?

The biggest hurdle to adopting Gen AI will not be the technology but instead, the resistance from the existing workforce who are engrained in the classical ways of delivering software. To overcome this, leaders must describe, as famous change author John Kotter penned, the WIIFM (What’s In It For Me) for each skill group. A clear vision of the future must be constantly communicated. Why are we doing this? Why is it good for the business? How will this help our careers? And so on.??

This will be a challenging era for those who do not like change. Change resistant cultures will not be able to make the leap. Leaders will need to excel at organization change management skills, negotiation skills, influence, selling and social psychology. People will be the biggest challenge to success. The technology is here already and will only get better. But technology is only as good as the people that have the proper mindset and skills to extract the value from it.?

Make no mistake, the journey will be long and challenging but the results of successful adoption of Gen AI is well worth the battle. Companies that sit on the sidelines will get 10x’d by their competition before they know what hit them. Here at Deloitte, we have accelerators and frameworks to help you navigate both the adoption of the technology and manage the transformation required to succeed on your journey.?

*** The banner and unicorn images were created using Midjourney, an Gen AI image creation tool ***

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