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What ‘AI Governance’ Means Inside ITX for Financial Services

A Risk Management Approach to Responsible AI Adoption

Artificial Intelligence is here to stay. But that doesn’t mean we should throw up our hands and let AI run roughshod over otherwise sound product development operations, right? The stakes are too high, and much of the technology’s potential (for good and for ill) remains unknown. 

 Since our founding nearly 3 decades ago, ITX has been committed to helping clients manage risk. In that time, we’ve learned that diving headlong into any new technology – let alone one as potentially disruptive as AI – without careful exploration invites all sorts of risks: financial, reputational, ethical, and legal. 

 We documented our approach to AI integration in a position paper, ITX’s Perspective on AI and Our Approach to Adoption. It reflects a clear statement of our current thinking – as AI evolves, so too will our assessment – complete with practical governance framework and set of rules that guide our behavior. This blog describes that framework and explains what AI governance means inside ITX. 

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AI Expedites Backlog Development To Help Project Team ‘Start Fast, Finish Strong’

122% of Planned Value Delivered Using 97% of Budgeted Timeline

Until now, ITX research blogs have explored whether the integration of artificial intelligence might help our clients deliver high-quality software more efficiently by cutting costs and reducing time to value. Thanks to knowledge gained from those experiments, we have confidently leveraged new learning to the benefits of our clients and our teams. Research continues, but as shown in the post that follows AI integration into software development workflows is a bona fide difference maker.

A recent ITX project with a longstanding client stands as a powerful example of how AI tools in the hands of skilled developers can modernize an organization’s entire digital experience. Faced with a modest budget, tight timeline, and interactions with multiple SSO vendors, we partnered with our client to deliver a full website reskin, SSO integration, and significant security enhancements.

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Can AI Accelerate Your Platform Modernization Project’s ‘Time to Value’?

ITX slashes project timeline and cost by 50% to preserve client’s vital revenue stream.

This research post describes in detail how a second ITX project team – with an assist from AI – worked in parallel with their colleagues to revitalize a legacy platform modernization project. Through extensive experimentation and testing, the team used GitHub Copilot’s AI-assisted IDE (Integrated Development Environment) tool to complete 7 months’ worth of roadmap in fewer than 4 months.

Observations: The Client and Their Challenge

Our client is a non-profit organization committed to helping students plan for future success in post-secondary academics and beyond. They reached out to ITX to help modernize their aging online portal – a critical project. Failure to develop, test, and release the new platform in time for their annual selling cycle and busy onboarding period threatened a vital revenue stream.

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The ITX Checklist for Evaluating AI Tools (Step 2)

Step 2: Best Practices for Tech, Legal & Financial Review

By Peter Sullivan

 

This blog is the second in a series that explores ITX’s approach to integrating AI tools into our product development workflows; here, we look at our internal Tools Committee and its policy-driven process for proposing, reviewing, and selecting the right tool for the job.

Introduction

ITX is a globally distributed organization with team members and clients across the United States and around the world. Increasingly complex client demands often require ITX Delivery Teams to match each new challenge with an even more sophisticated set of tools. At the same time, though, we need to protect against adding new tools unnecessarily. This may be especially true when it comes to artificial intelligence.

 

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The ITX Checklist for Evaluating AI Tools (Step 1)

Step 1: Assess competing stakeholder needs to identify the right tool for the job

By Peter Sullivan

 

This blog is the first in a series that explores ITX’s approach to AI tools integration – our proven method for vetting, selecting, and deploying artificial intelligence into our workflows.

Software product development companies face unrelenting pressure to stay ahead of tech’s accelerating pace. This is not a new phenomenon, of course. But when paired with an already volatile market environment and its ever-lower barriers to entry, just keeping pace becomes more than many companies can manage. Add artificial intelligence into the mix, and the strain can push them to the breaking point.

In the “earlier” days of AI (let’s face it: AI is still just a baby), every new tool felt like ‘a hammer in search of a nail.’ It felt like there was no job AI couldn’t do. These days, though, the calculus is changing. Tech companies that have been around for a while have figured out that specific stakeholders need specific tools with specific applications. And it takes a skilled craftsperson to choose the right tool for the right situation.

 

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How ITX Tested Miro’s New AI Assistant

Experiments led to increased productivity and reduced costs

In an era where digital transformation and AI integration are converging, the tools we choose can significantly impact our productivity. Powerful tools in the hands of skilled craftspeople can deliver impressive results. A recent project with a longtime client validates this mindset.

A pair of ITX Delivery teams had been working alongside their client counterparts for years; together, they’ve used Miro’s collaboration tool to support their joint activities. When ITX Product Owner Brandon Pierce discovered ways to leverage Miro’s AI Assistant, he started encouraging his teammates to play with it a bit to see if there might be room for it in their shared toolbox.

Initially, the client’s reluctance to bring AI technologies into their product development process constrained the team’s early work. That is, until Brandon’s experiments started to show real promise. Encouragement from these early-stage experiments proved to be the gentle nudge the client needed to explore Miro’s AI Assistance more thoroughly.

 

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What ‘AI Governance’ Means Inside ITX

A Risk Management Approach to Responsible AI Adoption

Artificial Intelligence is here to stay. But that doesn’t mean we should throw up our hands and let AI run roughshod over otherwise sound product development operations, right? The stakes are too high, and much of the technology’s potential (for good and for ill) remains unknown. 

 Since our founding nearly 3 decades ago, ITX has been committed to helping clients manage risk. In that time, we’ve learned that diving headlong into any new technology – let alone one as potentially disruptive as AI – without careful exploration invites all sorts of risks: financial, reputational, ethical, and legal. 

 We documented our approach to AI integration in a position paper, ITX’s Perspective on AI and Our Approach to Adoption. It reflects a clear statement of our current thinking – as AI evolves, so too will our assessment – complete with practical governance framework and set of rules that guide our behavior. This blog describes that framework and explains what AI governance means inside ITX. 

Read Article

Can Flow-Based Development Boost Team Efficiency and Quality?

Testing Our Hypothesis: Results and Learnings

Efficiency is important. But it’s also difficult to measure. How does your team measure efficiency?

Do you base it on perceived productivity, by counting the number of stories finished per sprint? Or maybe on a perception of how busy your team members are, by calculating their idle time? Without question, a team’s perceived efficiency can change based on what criteria you choose to measure.

And that begs the question: which metrics are best suited for indicating a highly efficient team? In this research blog, we explore this essential question.

 

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How Product + Design Work Together To Build ‘A Better Future’

3 Tips from Jared Spool (Strategic UX) and Roman Pichler (Product)

Compared with other software development disciplines, product management and user experience (UX) design are still pretty young professions. That said, they’re maturing rapidly and growing more specialized every day. As their evolution continues, it isn’t always clear who’s responsible for what on a product team and how best they can work together (we offered guidance on this topic in a post last year). Maybe it’s no surprise, then, that UX designers and product managers seem to get in each other’s way on the road to success.

This post canvasses the views that Roman Pichler (PM) and Jared Spool (UX) recently shared in consecutive podcast episodes of Product Momentum. What’s most intriguing about both conversations is how they each arrive at the same desired outcome – improving the lives of end users – despite taking parallel paths.

Jared calls that desired outcome a better future, while Roman describes it as the positive change a product should create. But both agree that whatever the solution, it’s less about building shiny new features or making things look pretty (i.e., outputs). Outcomes always trump the digital knickknacks we create along the way.

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