Using AI and Machine Learning for Agile Development and Portfolio Management - SPONSOR CONTENT FROM CA TECHNOLOGIES

June 12, 2018

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You’ve probably already heard of the rise in artificial intelligence (AI) and machine learning (ML) across many industries and applications. But, what about the application of AI and ML to agile development, testing and even portfolio management?

We’re all striving to deliver better products faster to meet customer needs in the marketplace and stay ahead of the competition. For nearly two decades, many companies have utilized the principles within the Agile Manifesto to deliver faster time-to-market than traditional, or linear development models.

So what is sparking the need to deliver even faster now and where do AI and ML fit in?

It’s All About the Data
We are in the age of instant gratification. From Amazon NOW orders that drop products at your door within two hours, to continuous delivery practices that produce new bug fixes and functionality apps and software daily, the rapid delivery of products and services enabled by digital transformation have changed customer expectations. It has also changed the way that our customers research, buy, use and review products and technologies. And while customer expectations may be higher, in this on-demand digital world, companies have substantially more insight into customer behaviors and patterns, which sets the stage for organizations to not only create what customers want, but also what the data shows they need.

As leaders in the digital economy, we strive to deliver on these constant and instantaneous demands. In fact, many organizations have figured out how to tune their development processes and create agility in their portfolio to adequately meet time-to-market needs. Because of this – and because the pressure of “instant” is persistent – to remain competitive and meet further accelerated timelines, companies are going to have to embrace and adopt newer methods.

The practical application of artificial intelligence and machine learning will become a standard way of life across engineering, testing, and broader portfolio management in the near future. Imagine how much faster your time to market could be if you didn’t have to rely on people or traditional software systems to identify dependencies within your code or between your teams? What if you could accurately predict product or software delivery schedules?

Think about how you could insert a “smart” machine to look for patterns, coding anomalies, changes in team output or analyze plans for deliverability. Imagine an intelligent algorithm that overlays your development data to detect issues and then learns how to predict their frequency and impact? Take this concept to your portfolio and program management tools and have machines learn how to identify trends in investment misappropriation, funding allocations, and program derailments.

This all sounds great and like a definite advancement for the agile and program management office, right? But many of us are worlds away from implementing artificial intelligence or machine learning broadly. The question becomes, what can we do today to put us on the path of faster delivery, more reliable planning and better program investments without AI or ML?

Getting Your Data House in Order
In the world of application and software delivery, organizations need transparency when it comes to the work agile and portfolio teams are doing. Companies must create a way to dynamically visualize, plan and track work across their entire portfolio. Delivering products to market with the speed and precision customers demand means everyone across the organization needs access to the right data, at the right time, in a format to make insights accessible and actionable. By connecting teams—from top to bottom—around a clear strategy and shared understanding of the work, organizations can transform their businesses into engines of innovation and speed.

Having disparate teams working with independent team-level tools, without roll-up capabilities doesn’t enable business to run any faster or harder. It actually introduces a new problem or problems into the mix of delivering rapid customer value. It forces team members and leaders alike, to spend quality time trying to understand what they are building, how the strategy is connecting to the execution of the work and understanding why their plans are not yielding the anticipated results. Without clear visibility, organizations are essentially flying blind when it comes to how investments are tracking and if any dependencies or risks could jeopardize a product or software release.

The (immediate) future of agile planning and delivery as well as portfolio and project management, lies in clear, clean and customized data reporting and manipulation. If organizations can get their data in a centralized system of record, and get all of their teams (agile, or not) to standardize on their program, project and work hierarchies the resulting data schema will reveal and enable better business insights. A well-structured, and centralized data repository is not just important for more streamlined reporting today, it’s also essential for any business looking to implement more advanced capabilities like self-service business intelligence (BI), or machine-learning based predictive analytics, AI and automation. As leaders, that is where we need to go to deliver the “now” that our customers are demanding.

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