Most enterprise digital transformation efforts today fail. A recent Gartner report found that nearly 70% of CFOs believe digital spending is underperforming against expected outcomes. The root cause is a disconnect between those outcomes and the software teams’ capacity to deliver them. Planview’s independently commissioned Project to Product 2023 State of the Industry Report found that business leaders believe IT teams in charge of digital transformation efforts can deliver 10x more than their actual capacity. Only 8% of IT and software development plans are ultimately executed, while 40% of digital innovation work is wasted.

The simple answer for this inefficiency is disconnected and disparate data. Most organizations leverage a plethora of tools to deliver digital transformation at scale. From Jira and GitHub to GitLab and Azure DevOps, these systems all play a critical role across the end-to-end software development lifecycle. But here’s the catch — none of them are integrated or aligned. Value streams with minimal interoperability cause bottlenecks that hinder digital transformation from succeeding. Data integration and alignment is critical.

Enterprise digital transformation is nearing a new era of opportunity amid the rise of generative AI. Because generative AI’s large language models (LLMs) are domain agnostic and don’t reside in any single system, it has unrivaled potential to minimize wasted workflows when integrated with Strategic Portfolio Management and Value Stream Management technologies. The global digital transformation market size is expected to exceed $7 trillion by 2032. Even a 5-to-10% reduction in waste is more than enough to move the needle. The time to act is now.

Integration: A semantic foundation of data  

Effectively harnessing generative AI’s power to minimize wasted work first requires a common semantic layer of data. Fusing datasets from heterogeneous systems into a normalized data platform creates an essential foundation for optimizing the flow of value. Once that holistic data foundation is in place, organizations can then engineer prompts that train generative AI LLMs to produce impactful prescriptive insights, identify high-risk workflows, and refine resource allocations. This removes a lot of the drudgery from software team planning processes, essentially automating the fundamental aspects of value stream management with AI-powered productivity.

Another prescription could be identifying various underlying dependencies between different product or project initiatives that are causing significant delays — leading the organization to add direct resource capacity or rebalance capacity between teams based on what the data is surfacing. This level of acute data-driven decision-making relative to capacity and allocation helps align financial investments to high-priority projects, accelerating time to market for initiatives with the greatest ROI.

Alignment: The power of convergence

Organizational alignment is critical to leveraging generative AI for digital transformation success. It’s important to remember that technology is only as powerful as your ability to deploy it. For generative AI to effectively accelerate value, it’s imperative to eliminate the “black box” that exists between business outcomes and software development. An organization’s business and technology functions must be operating in unison. By synchronizing all the tools, processes and metrics associated with software development and delivery, organizations can optimize decision-making across portfolios, value streams and DevOps teams to link digital transformation capital allocation to impactful business outcomes.

This is where converging objectives and key results (OKRs) across strategic portfolio management, value stream management, and agile planning are worth their weight in gold. It doesn’t matter how brilliant the generative AI prompts are – they are incapable of achieving desired outcomes and driving digital transformation without a shared overarching mission. Universal alignment connects gaps between the technology and business facets of the organization with real-time visibility that provides more intelligence, predictions, and prescriptions. By integrating these actionable insights from portfolio management, enterprise agile planning, and value stream management into a single source of truth, a system of record, cross-functional teams have a clear roadmap for transforming ideas into outcomes.

It’s no secret that the ongoing generative AI hype over the past 10 months has sparked valid concerns about its ability to replace human workers across industries. However, in the context of digital transformation, we shouldn’t think about the future with a narrow “human or machine” mindset. It’s really about humans plus machines. Applying AI-powered technology to augment manual workflows is what will deliver the greatest impact on digital innovation in the years to come. 

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Of the 96% of responding companies that say they’ve undertaken a value stream management initiative, driving long-term customer value is their focus, according to the survey.

Value stream management is a process that involves spotting and eliminating bottlenecks in the development and delivery cycles with the result of continuously improving performance, quality and customer satisfaction…

Read the full article on VSM Times.

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According to Allstacks, R&D cost capitalization is something that is mandated by accounting standards in the US GAAP (Generally Accepted Accounting Principles).

Allstacks explained that the process of tracking R&D software development costs can be manual and tedious, and it hopes this new offering will help streamline the process. 

The Software Capitalization software also enables splitting up credit among multiple engineers when more than one person is working on a particular project. 

“Teams can easily produce records of capitalizable software work, automating a traditionally time-consuming and manual process, and improving the accuracy of the data collected,” said Hersh Tapadia, co-founder and CEO of Allstacks. “Allstacks customers can now seamlessly account for their engineering investment, understand the cost of capitalizable efforts, and generate defensible reports in audit-ready format that adhere to all applicable compliance standards.”

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According to a recent survey from Broadcom, more than two-thirds of respondents claimed that their visibility isn’t what it should be. In addition, the majority of companies are missing a key characteristic of mature VSM organizations, which is the continuous availability of data. Sixty-nine percent say they share their VSM metrics only quarterly or monthly. Only 9% continuously share data.

Just 2% of respondents to the survey consider themselves to be at a level of VSM maturity, where they are using VSM on all of their products. Sixty percent are in the early adoption stage, 13% are in planning, and 25% have a pilot project. In addition, 23% are only running VSM on a single product. “While organizations acknowledge the significance of VSM, a substantial portion find themselves in the early phases of adoption. This survey indicates a growing interest in ongoing performance improvements and aspiration for holistic, enterprise-wide visibility, alignment and efficiency to increase customer value,” said Jean-Louis Vignaud, head of ValueOps at Broadcom.

The survey also pointed out peoples’ challenges with mapping metrics to value. Sixty-three percent say their metrics aren’t mapped to product performance, and 11% say their companies don’t provide business metrics for measuring products.Decision making still remains largely centralized as well, with 85% of decisions either being made at the leadership level or requiring leadership approval before proceeding. 

To help companies better assess their maturity with VSM, Broadcom has now announced the Value Stream Management Maturity Model, which was created based on the survey results. The model provides insights, guidance, and actionable steps to help companies meet their VSM goals. It includes five levels: Foundational, Value Stream Aware, Collaborative and Visible, Data-Driven, and Full Transparency and Flow. 

“Value stream management has become a pivotal strategy for digital transformation, but success requires that teams understand where they’re at and where they’re going,” said  Vignaud. “To help enterprise teams on their VSM journey, we developed a progressive model that defines maturity from the early stages focused on breaking down silos to full maturity, delineated by data-driven alignment and an established culture of continuous improvement. Our hope is this model will serve as a guide for organizations as they progress along their digital transformation journey.”

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The release is part of the company’s initiative to give companies a concrete way to break down organizational silos with a single view of shared metrics and business outcome insights.

“For far too long, a black box has existed between business strategies and how software isdelivered against them,” said Razat Gaurav, CEO of Planview. “To thrive in today’s fast-paced environment, the entire C-Suite, from CEO to CFO to CTO, need shared, real-time, actionable data regarding the health of their entire organization’s business and digital transformation initiatives. Planview delivers an innovative way to leverage predictive delivery insights and sentiment details and put them back into portfolio, value stream, and team plans, helping organizations create faster feedback loops that yield better decisions and deliver better outcomes—ultimately shining a light on that black box.”

The new Digital Product Insights system utilizes AI-powered sentiment analysis to gather data from comments. It categorizes the language as positive, negative, or neutral, offering a comprehensive view of plan performance. This data-driven approach supports proactive decision-making and increases confidence in predicting successful deliveries.

Planview also launched two innovations focused on delivering data-driven visibility to further strengthen the connection between software delivery and business outcomes.

The first is Planview Universal Connector, which extends connectivity across the enterprise toolchain, allowing organizations to continue their use of in-house or industry-specific applications as part of their value delivery.

The other, Roadmaps for Teams helps organizations translate plans into visual timelines for achieving their business outcomes and completing deliverables and can be used in conjunction with Digital Product Insights. 

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Only 8% of respondents stated that they have operationalized the shift from project to product, meaning that 92% have yet to realize or capture the full value of a product operating model at scale. However, 63% reported that they are in the exploratory phase and 29% said they are expanding on earlier experiments.

It was also found that, until a mature product model is in place, enterprises spend 70% of their delivery capacity on defect remediation and waste 40% of their efforts due to overload and bottlenecks.

Read the full article here on VSM Times.

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Logic Flow Adapters were added to the platform in this release. These allow users to incorporate business logic into their value streams, based on inputs from multiple different applications. 

Users write and manage custom scripts for these in a single hub, which allows them to create and execute scripts without doing anything in the backend.

Read the full article here on VSM Times.

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The new dashboard provides an overall view of metrics like DORA and flow metrics. By tracking these metrics over a period of time, development teams will be able to locate trends early, drill down into individual metrics, take action to improve performance, and track innovation investments. 

And, going up the chain, business leaders can also look at these metrics to eliminate bottlenecks and make decisions like where to add resources to support developers.

Read the full story on VSM Times.

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Software development is a business differentiator that requires strategic investments to improve the bottom line. Having worked in all aspects of the software development lifecycle, I know most people in the industry think in terms of two types of deliverables – creating new features and fixing bugs. In reality, that’s too limiting. I hear management complain that developer productivity is down simply because developers are responsible for what appears to be everything now and may spend less than 50% of their time writing code. The amount of time a developer has available for coding is tracked, but many other activities are hidden and considered “tax” of the organization, like caretaking the pipeline, fixing problems with your environment and helping testers.

Developers want to write code. The business wants them to write code. Customers want the solutions their code provides. That means we need to understand where developer time is actually being spent and give them an opportunity to write code for time-consuming manual activities that can be automated. 

Four fundamental development categories  

For product management to be effective, four types of work need to be visible, supported, and funded. 

Features. Delivering cutting-edge features is the fun part of a developer’s job. Creativity takes time and delighting customers isn’t easy in highly-competitive markets. Resources need to be invested in developing new or improved functionality, usability, flexibility, and other customer-friendly features. 

Defects. I’ve heard it said that today’s features are tomorrow’s bugs. Fixing defects, bugs, and other issues is a routine part of software development. Releases often go out with bugs, so your team may be putting out fires related to your releases and those of your vendors and partners. Identifying and eliminating problems that hurt the customer experience is important, but they need to be weighed against other priorities. 

Risk. Risk-related activities represent the majority of the hidden work that developers do. Improving the software engine to produce more reliably involves setting up guardrails and security for better deployment. DevOps practices push for complete automation of manual checklists that have been used to validate items, including whitesource library checks, open source license validation, testing, deployments, and code analysis. Developers WANT to automate them, and implementing code that does the checks is fun. 

Technical debt. As soon as something is built, the need for modernization is a possibility as the world changes. The technology gap between modern standards and legacy systems grows over time, so it’s important to regularly assess the tradeoffs between investing in patches or updates and rethinking the design given new learnings and needs. This includes build vs buy decisions. 

Balancing development work investments

Peter Hyde of Gartner defines a work profile as, “The proportion of each type of work item delivered in a time period by the software value stream.” Too much focus on one area can throw the organization out of balance, making it harder to deliver customer value. For example, if too much time is focused on features/defects, you’ll end up with a fragile development environment straddled with technical debt, ultimately killing its ability to deliver new features.

Start by identifying the percentage of your resources going into adding features, fixing defects, reducing risk, and addressing technical debt to understand your current work profile, which is also known as your work distribution. This may take some digging if visibility is lacking, especially if everything is currently being categorized under features or defects. This will give you a baseline for analysis and allocation of future spending, which is based on your goals. 

If the goal is to make significant future investments into sets of components, continual modernization of the technology base (technical debt) and refinement of the software delivery engine (risk) will reduce the overall “cost” and time of delivering new features or fixing bugs. Investments into risk may also increase the ability to obtain certifications, improve agility to react to outages, reduce MTTR, and boost customer trust.  

Ultimately the team, typically driven by product, must make tradeoffs with the limited resources they have. The team needs a way to tradeoff investing into better security automation vs improving the technology stack vs just adding features and fixing bugs.

Connecting value stream management and outcome mapping 

Outcomes are the core of delivering value to the customer, and every outcome can be supported by one of the four types of work. Teams should think hard about the outcomes they are working towards. They need to clarify why items are important, identify obstacles, explore how to learn more, and identify how to measure progress towards the outcomes. 

The work of product management is central to translating the outcomes into the core work types. Ultimately they are responsible for justifying and defending investment decisions.  Value stream management helps in this journey by providing the data and associated visualizations connected to outcomes.

Investment strategy advice

Balancing work is essential to effective product management. Start with your outcomes, but communicate in terms of the work distribution. You know the strategy you’re trying to accomplish from a business perspective. Translate that for your individual product managers, so they can connect those business outcomes to actual work that has to get done. 

To keep everyone on the same page, train your executive team on how to translate outcomes to the work profiles, and proactively sell why the tradeoffs were made in the first place between the various types of work. Finding the right balance will make a developer’s job more enjoyable while also improving customer value.

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They were wrong.

The remainder of 2020 and 2021 were spent trying to figure out how to get an entire workforce to work remotely, while still being able to collaborate and innovate. Sales of cloud solutions soared. Much of the new software companies invested in required training just to get up to speed.

But training in the form of in-person conferences ceased to exist, and organizers sought to digitalize the live experience to closely resemble those conferences.

Fast forward to 2023. The software and infrastructure organizations have put in place enabled them to continue to work, albeit not necessarily at peak performance. Most companies today have figured out the ‘what’ of remote work, and some have advanced to the ‘how.’

But this move to a digital transformation has provided organizations with tools that can help them work even more efficiently than they could when tethered to an on-premises data center, and are only now just starting to reap the benefits. 

Thus, the editors of SD Times have determined that 2023 will be “The Year of Continuous Improvement.” It will, though, extend beyond 2023.

Bob Walker, technical director at continuous delivery company Octopus Deploy, said, “The way I kind of look at that is that you have a revolution, where everyone’s bought all these new tools and they’re starting to implement everything. Then you have this evolution of, we just adopted this brand new CI tool, or this brand new CD tool, whatever the case may be. And then you have this evolution where you have to learn through it, and everything takes time.”

Development managers, or a team of software engineers, or QA, have to worry about making sure they’re delivering on goals and OKRs, to ensure the software they deliver has value. So, Walker noted, “it’s a balance between ‘what can we do right now’ versus ‘what can we do in a few month’s time’? What do we have right now that is ‘good enough’ to get us through the next couple of weeks or the next couple months, and then start looking at how we can make small changes to these other improvements? It can be a massive time investment.”

Show me the metrics

Continuous improvement begins with an understanding of what’s happening in your product and processes. There are DevOps and workflow metrics that teams can leverage to find weaknesses or hurdles that slow production or are wasteful time sucks, such as waiting on a pull request. 

Mik Kersten, who wrote the book “Project to Product” on optimizing flow, holds the view that continuous improvement needs to be driven by data. “You need to be able to measure, you need to understand how you’re driving business outcomes, or failing to drive business outcomes,” he said. “But it’s not just at the team level, or at the level of the Scrum team, or the Agile team, but the level of the organization.”

Yet, like Agile development and DevOps adoption, there’s no prescription for success. Some organizations do daily Scrum stand-ups but still deliver software in a “waterfall” fashion. Some will adopt automated testing and note that it’s an improvement. So, this begs the question: Isn’t incremental improvement good? Does it have to be an overarching goal?

Chris Gardner, VP and research director at Forrester, said data bears out the need for organization-wide improvement efforts, so that as they adopt things like automated testing, or value stream management, they can begin to move down the road in a more unified way, as opposed to simply being better at testing, or better at security.

“When we ask folks if they’re leveraging DevOps or SRE, or platform methodologies, the numbers are usually pretty high in terms of people saying they’re doing it,” Gardner said. “But then we ask them, the second question is, are you doing it across your organization? Is every application being supported this way? And the answer is inevitably no, it’s not scaled out. So I believe that continuous improvement also means scaling out success, and not just having it in pockets.”

For Gardner, continuous improvement is not just implementing new methodologies, but scaling the ones you have within your organization that are successful, and perhaps scaling down the ones that are not. “Not every approach is going to be a winner,” he said. 

Eat more lean

Agile programming, DevOps and now value stream management are seen as the best-practice approaches to continuous improvement. These are based on lean manufacturing principles that advanced organizations use to eliminate process bottlenecks and repetitive tasks.

Value stream management, particularly, has become a new driver for continuous improvement.

According to Lance Knight, president and COO of VSM platform provider ConnectALL, value stream management is a human endeavor performed with a mindset of being more efficient. “When you think about the Lean principles that are around value stream management, it’s about looking at how to remove non-value-added activities, maybe automate some of your value-added activities and remove costs and overhead inside your value stream.”

Value stream management, he noted, is a driver of continuous improvement. “You’re continually looking at how you’re doing things, you’re continually looking at what can be removed to be more efficient,” he said.

Knight went on to make the point that you can’t simply deploy value stream management and be done. “It’s a human endeavor, people keep looking at it, managing it, facilitating it to remove waste,” he said. So, to have a successful implementation, he advised: “Learn lean, implement, map your value stream, understand systems thinking, consistently look for places to improve, either by changing human processes or by using software to automate, to drive that efficiency and create predictability in your software value stream.”

At software tools provider Atlassian, they’re working to move software teams to mastery by offering coaching. “Coach teams help [IT teams] get feedback about their previous processes and then allow for continuous improvement,” said Suzie Prince, head of product, DevOps, at Atlassian. In Compass, Atlassian’s developer portal that provides a real-time representation of the engineering output, they’ve created CheckOps, which Prince described as akin to a retrospective. “You’re going to look at your components that are in production, and look at the health of them every day. And this will give you insights into what that health looks like and allow you again to continuously improve on keeping them to the certain bar that you expect.”

Another driver of continuous improvement, she said, is the current economic uncertainty. With conditions being as they are, she said, “We know that people will be thinking about waste and efficiency. And so we also will be able to provide insights into things like this continuous flow of work and reducing the waste of where people are waiting for things and the handoffs that are a long time. We want to use automation to reduce that as well. All which I think fits in the same set of continuously improving.”

Key to it all is automation

Automation and continuous improvement are inexorably tied together, heard in many conversations SD Times has had with practitioners of the course of the year. It is essential to freeing up high-level engineers from having to perform repetitive, mundane tasks as well as adding reliability to work processes.

So whether it’s automation for creating and executing test scripts, or for triggering events when a change to a code base is made, or implementing tighter restrictions on data access, automation can make organizations more efficient and their processes more reliable.

When starting to use automation, according to John Laffey, product strategy lead at configuration management company Puppet (now a Perforce company), you should first find the things that interrupt your day. “IT and DevOps staffs tend to be really, really interrupt- driven, when I got out and talk to them,” he said. “I hear anything from 30% to 50% of some people’s time is spent doing things they had no intention of doing when they logged on in the morning. That is the stuff you should automate.” 

By automating repetitive little things that are easy fixes, that’s going to start freeing up time to be more productive and innovative, Laffey said. On the other hand, he said there’s not point in automating things that you’re going to do once a month, “I once had a boss that spent days and days writing a script to automate something we did like once a quarter that took 15 minutes. There’s no return on investment on that. Automate the things that you can do and that others can use.”

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