MPUG members are invited to an extended free trial of empwr.ai

Increasingly, all enterprises are becoming software-led, which is changing the traditional project management lifecycle into the continuous iterative loop that modern software teams have employed for decades. The advent and rapid maturation of generative AI is accelerating this shift and changing it in ways that seemed impossible just three years ago.

Our team at Envorso, a boutique consulting company comprised of West Coast software and Rust Belt manufacturing leaders from companies like Microsoft, Ford Motor Company, Meta, and Lucid Motors, has been front and center for this massive transformation. Our CTO, Ludo Hauduc, the recent SVP for AI Infrastructure at Meta, just gave a talk to project managers at Boeing about the impacts of AI on Project Management.

In our experience, there are a few key themes to watch in 2024 as generative AI impacts Project Management:

1. Unlocking the knowledge hidden in an organization’s unstructured data

2. Making previously unscalable interaction scenarios scalable

3. Empowering unprecedented automation

Unlocking the Knowledge in the World’s Unstructured Data

According to Gartner, 80–90% of all new enterprise data is unstructured, and it is growing 3X faster than structured data. This data includes emails, text messages, IM conversations, call center logs, customer feedback, user interview transcripts, blogs, videos, product reviews, replies in questionnaires, product requirements, status reports, risk assessments, dependency maps, and the countless meetings we all have throughout the year.

The volume of unstructured data we collectively create is staggering. According to International Data Corporation (IDC) estimates, less than 10% of that data is analyzed. Advancements in AI are beginning to change this.

At Envorso, we are incubating a software product called Empwr.ai that is designed to transform unstructured data into usable structured data to help teams optimize their operations, information sharing, and communication. Currently, the product turns unstructured meeting data into structured meeting outcomes. The team plans to expand its capabilities to include other types of unstructured data, such as written communications and project documentation.

Imagine the potential of being able to unlock and easily access the collective knowledge of an organization. This could revolutionize the way businesses operate and make informed decisions.

Making Previously Unscalable Scenarios Scalable

Generative AI is a game changer in that it brings previously unscalable solutions within the reach of a small team. AI agents can be deployed to autonomously accomplish previously unscalable tasks like extracting feedback from call center logs (Tethr), deploying extensive interviews at the scale of surveys (Outset.ai), conducting code reviews for huge software organizations (Jacb.ai), and embedding product requirements virtual coaches into product requirements software (Jama Connect Advisor).

Recently, we worked with a global automotive OEM on a project that would have been impossible just a few years ago. The auto OEM had 100K+ legacy product requirements in various antiquated requirements tools. They needed to migrate the old requirements into Jama and rewrite all the requirements according to EARS and INCOSE standards. Before generative AI, this would have taken three years of full-time work from a team of 20 people. We built and deployed a system using large language models (LLMs) to do the work with accuracy that far exceeded the OEM’s expectations. Once the system was built, it took just a matter of days to run through the process.

Empowering Unprecedented Automation

The big payoff for generative AI systems is the promise to automate complex tasks. By now, most people with even a passing interest in technology have experimented with large language models (LLMs) like Anthropic’s Claude, Open AI’s ChatGPT, or Google’s Gemini. Increasingly, companies are building applications on top of these models to accomplish astounding automation.

GitHub’s Copilot helps software engineers generate deployable code. Research has found that while still in its infancy, Copilot is driving 55% faster code efficiency, resulting in 88% of engineers feeling more productive.

Our engineering team at Empwr.ai employs an AI agent called Jacb.ai to conduct code reviews and write code based on Figma designs. One of our newest engineers is just now getting to the point where he has surpassed the AI agent’s contribution to our code base. The AI agent not only accelerates the code contribution process but also enhances the overall quality of the code.

The power of automation that generative AI will enable for a given organization in 2024 will be determined by how much unstructured data is transformed into and combined with structured data to make more types and an increased volume of data accessible to systems. Identifying and redesigning previously unscalable processes within an organization is the other key automation enabler.

In 2024, as these three themes take shape, we expect the most successful project managers to do more with less by reducing their administrative workload, make decisions with a more nuanced view of reality combining qualitative and quantitative insights, and being able to focus on the more strategic aspects of their projects.

Empowering Project Management with Advanced Generative AI

At Envorso, we’ve been evaluating, implementing, and building advanced generative AI systems that are capable of learning from vast datasets to create solutions, content, and forecasts that impact every phase of project management and the product development lifecycle. This is where Empwr.ai comes in.

We’d like to offer all of you at MPUG an extended free trial to Empwr.ai in exchange for your feedback as we iterate on our solution for project managers. Go to https://empwr.ai and click “sign up for free.” Once you have an account, email help@empwr.ai and tell us you are an MPUG member so we can give you an extension to your free trial.

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