Simulate software delivery before it starts. Use AI and digital twins to predict costs, timelines, and risks - so you can choose the best path to successful outcomes with confidence.
What if you knew at the outset of your software development project what kind of team is needed, how much everything will cost and when the final results will be ready?
Digital twins have proven effective in manufacturing, where factory operations are simulated in virtual environments to optimize processes like component assembly before work begins in the physical world. This same approach is now being applied to software development, and at Vivicta we have some exciting simulation pilots in the making.
Software development is inherently digital, so it’s easier to simulate than the physical world. With AI, we can simulate software delivery in advance as many times as needed. Let’s create 100 digital twins. Not enough? Then let’s make 1,000 or 10,000. There are no limits; if the data exists for the first simulation, the next ones are easy.
Waterfall, Agile, DevOps – although the software industry has spent well over 30 years optimizing delivery models, the world is full of stories about projects that overran schedules and budgets and failed to deliver on business expectations.
Software development projects can go off track for many reasons. Deliveries may be managed too reactively, people may be trying to control too many variables at once, or complexity may simply grow faster than an organization’s ability to manage it.
However, I would argue that the main reason software projects fail is that organizations don’t make the most of all the data generated by previous projects. Commits, Jira tickets, productivity models, and all other forms of metadata should be captured and systematically used to simulate future projects.
Vivicta can utilize data from your organization’s previous project deliveries as metadata and combine it with datasets we have accumulated over decades. From there, we can begin simulating: creating probability-based models in which parameters are refined and alternatives iterated until we identify the most likely path to delivering a project successfully, on budget and on time.
With a world full of variables, nothing can be predicted with complete certainty. Even so, I believe simulation will soon become every CFO’s trusted companion, allowing organizations to safely, cost-effectively, and quickly test and optimize team structures, architectural choices, and timelines while also assessing operational risks.
With AI, simulations can be used to identify bottlenecks, analyze dependencies, and uncover hidden risks. Simulations also frequently deliver tangible business benefits, for example lower project costs.
Simulations are possible in genuinely data-driven organizations. If you work in one and think simulations sound exciting, reach out! But if you don’t, there’s no reason to get discouraged. AI is transforming software development on numerous fronts, but there is still time to get on board.
Simulations are my favorite example of the opportunities ahead, but AI agents and outcome-based pricing are also exciting. High-quality data is at the center of these as well. In the AI era, data tied to an organization’s own internal context and industry expertise is crucial.
I’m painting with a broad brush here, but if you don’t have an AI agent as a coworker yet, you will soon. For an AI agent to work properly, it needs context and domain knowledge about its tasks and industry. This kind of data is like superfood for the agent’s brain: its memory doesn’t work properly without it.
If your coworker is forgetful, they might just own up to it. But if an AI agent doesn’t understand the context and use case, it may simply hallucinate – and no one wants that.
Most AI agents today are still one-trick ponies, specialized in single tasks, but they are being trained into full-fledged software team members that handle entire roles or functions. Alongside humans, a specialized AI agent can take care of things like testing, code reviews, documentation, or keeping compliance in check.
At Vivicta, I serve as a bridge between engineering and sales, and I’ve got a number of AI agents handling specific tasks, like tweaking PowerPoint presentations. Right now, I’m developing a personal AI assistant for a broader role. My assistant can already search our intranet to answer my team’s most common HR questions, give advice, and loop me in when necessary.
In many industries, buying and selling outcome-based deliveries is already the norm. Now, this pricing model is coming to software development as well. The idea is simple: customers no longer pay for hours worked or resources consumed, but for business outcomes such as improvements in customer satisfaction or other key metrics.
If you want to pay for business value, your data needs to be in shape. No one can help you improve your KPIs unless they understand your needs properly – and in the AI era, industry expertise is always built on high-quality data.
I think the future looks bright. Software project simulations, AI agents as everyday helpers, and outcome-based pricing – all of these shifts are already underway, and Vivicta is helping drive them forward from the front lines. While AI is first and foremost an opportunity for software development, we obviously need to learn how to manage the risks that come with it. I explore this topic in more detail in another blog post: AI in software development: Threat or opportunity?
If this sparked your interest, let’s talk!