AI Beyond Cost Savings
AI’s Power to Accelerate Time-to-Market
A quick note: I see the stats showing people are opening my newsletters, but it’s hard to tell if they’re truly resonating or delivering value. I would really love to hear from you — your thoughts, feedback, or even just a quick comment. Thank you for being part of this journey ❤️.#beyondAI - I believe time-to-market is critical for every company, whether a scrappy startup or a massive multinational corporation. For both, the speed at which they move from concept to launch determines not only how quickly they gain insights from the market but also how fast they start generating revenue.
It’s a simple equation: the faster you get your product into customers' hands, the sooner you can iterate, adapt, and deliver something even better.To reduce time-to-market, the first step is understanding the actual processes and workflows that companies have in place to deliver.Let’s take a closer look at the typical process for delivering software, whether as a product or a service.
Most companies follow a structured process for product development, one that has evolved over time into what is often considered the standard. But let’s be honest - just because it’s the standard doesn’t mean it’s the best. And we’ll come back to that later.
These internal software processes are usually aligned with the Software Development Lifecycle Process (SDLP), which consists of six stages:
The Six Stages of the Software Development Lifecycle Process (SDLP)
Idea or Demand Generation
This is where it all begins. A need for a product or service is identified, often driven by customer feedback, market opportunities, internal optimization goals, or regulatory requirements. The main goal here is to clearly define the problem and assess the potential impact of solving it.
Concept Validation
At this stage, the idea is evaluated for feasibility. Teams assess whether the solution aligns with company objectives, whether the required resources are available, and if the market is ready. This phase often includes developing a preliminary business case and going through the initial approval processes.
Design
This is where the idea takes shape. Teams create detailed specifications, wireframes, or prototypes to define what the product will look like, how it will function, and how it will integrate with other systems. The focus here is to provide a clear blueprint for the development phase.
Development
This is the stage where the product is actually built. Developers write code, create features, and integrate systems. Whether it’s done in agile sprints or using a traditional waterfall model, this phase is all about execution.
Testing
Before the product is released, it undergoes thorough testing to ensure it meets quality, security, and functionality standards. This phase includes unit testing, integration testing, and user acceptance testing (UAT). It’s where bugs are identified and resolved.
Delivery or Launch
Finally, the product or service is released to the market or end users. This includes deployment, marketing efforts, and initial customer support to ensure everything runs smoothly. Feedback loops are typically established during this phase to gather early user insights.
Each of these stages is then tailored to fit an organization’s specific requirements and governance structures. And here’s where it gets tricky. These company-specific adaptations often inflate the SDLP in ways that slow it down over time, regardless of whether the process follows a waterfall, iterative, or agile model. While the SDLP in its raw form is relatively lean, organizational adjustments can make it cumbersome, leading to various pain points at the process level.
Process-Level Pain Points in Time-to-Market
Some common process-level pain points that stem from how processes are adapted to organizational needs:
Excessive Approvals: Endless layers of sign-offs can turn what should be quick decisions into weeks of delays.
Rigid Governance: Processes designed to ensure quality often end up becoming the biggest hurdles to speed and agility.
Misaligned Goals Across Teams: Different departments pursuing conflicting priorities can create bottlenecks and delays.
Inefficient Resource Allocation: Waiting on approvals for funding, personnel, or tools can completely stall progress.
These pain points stack up over time. Honestly, I don’t know a single team that hasn’t complained about them. It feels like the natural evolution of companies:
The more mature companies become, the slower they get.
What starts as a framework for consistency and quality eventually turns into a maze of inefficiencies that drag time-to-market to a crawl.
Now, many companies tackle these pains, often investing heavily to identify where processes need streamlining and how to overcome inefficiencies. These efforts typically focus on the process level, which isn’t wrong—it can, in fact, significantly reduce time-to-market. However, focusing solely on processes is only part of the solution. There’s another critical area to optimize: workflows.
Optimizing Where the Real Work Happens
Workflows are the specific activities carried out to complete a task within a process step. While processes define the overarching structure, workflows deal with the hands-on execution of tasks. Optimizing workflows is just as important as streamlining processes, yet it’s often skipped.
This isn’t because companies don’t want to address workflows. It’s because those responsible for optimizing processes are usually strategic, high-level thinkers who lack a detailed understanding of the specialized workflows used by domain experts. These are people focused on the bigger picture—governance, approvals, or resource alignment—rather than the granular tasks being done on the ground.
For workflows to be optimized, the drive must come from within, from the very core of where tasks are actually executed. It’s about empowering the teams and individuals who know these workflows best to identify inefficiencies and implement changes. This type of optimization requires collaboration between high-level strategists and domain experts to bridge the gap between processes and practical execution.
AI Superpower Reveals at the Workflow-Level
As we entered the digitalization era, companies, guided by expert advice, began introducing new software to make workflows more efficient. This was followed by automation technologies like RPA (Robotic Process Automation) and scripting, which automated even more tasks. But then, progress seemed to plateau.
Now, with the emergence of Generative AI, we’re standing on the brink of a new wave of transformation—automation, or at the very least, expert augmentation, that can make workflows even more efficient.
This potential, however, comes with a caveat:
Optimizing workflows with GenAI isn’t about simply applying the technology wherever possible. It requires a deep understanding of routines and workflows at the working level. You can’t just plug it in and expect miracles—you have to figure out where it truly fits.
Every GenAI initiative aimed at reducing time-to-market, especially in the context of software development, must be developed in collaboration with Subject Matter Experts (SMEs) who own those workflows. These are the people who live and breathe the daily tasks. They know exactly where bottlenecks are, what areas can be improved, and what must remain untouched.
For internal AI Product Managers, SMEs are not just collaborators—they’re key stakeholders, or even better, customers in the process. AI Product Managers need to work closely with SMEs to ideate, explore, and assess whether parts of a workflow can be augmented with AI or, in some cases, fully automated.
When this deep collaboration succeeds, it opens the door to endless possibilities. AI use cases across the software development lifecycle start to reveal themselves, each one holding the potential to reduce inefficiencies and speed up the process.
How I Create AI Use Cases to Reduce Time-to-Market
Let me walk you through how I typically proceed when creating an idea to support reducing time-to-market in software development. The process is structured but flexible enough to adapt to different organizational needs:
Understand the Overall Process (e.g., SDLP)
Start by mapping out the entire process you’re focusing on. In software development, this could be the Software Development Lifecycle Process (SDLP). Understand how the stages connect and identify where bottlenecks or inefficiencies might occur.Understand Which Workflows Typically Exist Within a Process Step (e.g., Concept Validation)
Dive deeper into a specific process step. For example, in the Concept Validation stage, identify the workflows involved, such as defining requirements, validating feasibility, or aligning stakeholders.Ideate on Potential AI Use Cases for Specific Activities Within a Workflow
Once the workflows are clear, focus on individual activities within them. For example, if a workflow involves gathering customer requirements, think about how AI could assist—perhaps through a Generative AI model that analyzes historical data to pre-fill requirements or suggest templates.Make a Rough Estimate About the Benefit of a Potential AI Use Case
Evaluate the impact of your ideas. Would automating a task save time, reduce errors, or free up resources? Estimate potential gains in terms of time savings, cost reduction, or improved output quality.Prioritize the AI Use Cases and Find the Key Stakeholders
Not all ideas will have the same impact, so rank them by potential value and feasibility. Then identify the stakeholders—usually SMEs or team leads—who are critical to validating the idea and implementing the solution.Approach the Stakeholders to Validate My Assumptions
Present your prioritized ideas to the stakeholders. Share your assumptions about the benefits and feasibility and gather their feedback to refine the use cases. Their input is essential for making realistic plans.Get Them on Board and, If Agreed, Start the AI Product Journey
Once stakeholders see the value and agree to proceed, bring them on board as collaborators. This is where the actual AI product journey begins—from proof of concept to full implementation.
Sure, you can adapt this approach to fit your unique situation. Every company is eager to find ways to improve time-to-market, and maybe now you can take the lead by applying a similar approach. 🚀
Final Thoughts
When I first started writing this article, I planned to simply list AI use cases for improving time-to-market. However, I quickly realized that the approach might not be very helpful. Instead, I decided to share how I think about processes, workflows, and tasks—and how I use this mental model to identify impactful AI use cases. My hope is that this resonates with you more than just a generic list ever could.
Here’s what I’ve touched on in this article:
Time-to-Market is Critical: In today’s fast-paced world, speed isn’t optional—it’s the difference between staying relevant or falling behind.
Processes and Workflows Are Different: Addressing inefficiencies at both levels is key to achieving meaningful results.
AI Unlocks New Possibilities: Generative AI is a game-changer, enabling smarter automation and augmenting expertise in entirely new ways.
Collaboration is Key: The best solutions emerge when internal AI Product Managers and SMEs work together to uncover and implement impactful AI use cases.
A Structured Approach Works: By following a clear, step-by-step process, you can ensure your AI initiatives are both practical and valuable.
And here’s something even more important to keep in mind: stop positioning AI as just a tool for cost reduction. That narrative is tired. Instead, highlight its potential to accelerate time-to-market—because that’s where the real competitive edge lies.
Sadly, this perspective is often overlooked. Too many companies see AI solely as a way to save money. But imagine what could happen if they realized how AI could help them get to market faster. It might just change everything.
Maybe.
JBK 🕊️
A quick note: I see the stats showing people are opening my newsletters, but it’s hard to tell if they’re truly resonating or delivering value. I would really love to hear from you — your thoughts, feedback, or even just a quick comment.
Thank you for being part of this journey ❤️.