With 20 years of experience in implementing PPM tools, data models, processes, and technology, I've gained an in-depth understanding of the data challenges most organizations face. As an independent consultant, I particularly favored Broadcom's solutions. With their robust architecture and data governance capabilities, these solutions enabled organizations to establish a "golden” source of data. This golden source supports various critical functions, including organizational reporting, governance, and regulatory compliance.
Now, working at Broadcom and advancing in my own AI journey, I have an even better understanding of the immense advantages our tools offer customers. These tools provide the fundamental, well-structured data that is necessary for many aspects of an organization. Our tool modules comprehensively support financials, resource management, roadmaps, demand management, delivery, risks/issues/dependencies, portfolio management, product management, and value stream management. And now, I’m excited by how we can support our customers’ AI journey too.
AI innovation is globally dominant and has become a priority across all industries, with teams looking to further evolve their AI projects, knowledge, and experience. Larger organizations have even established "AI factories," with dedicated departments focused on developing AI capabilities. It's noteworthy that organizations have continued to invest in AI despite reducing other expenditures. However, it's also essential to consider Gartner's survey finding that only 54% of AI projects make it to production, and [fewer than half deliver meaningful results].
Billions of dollars have already been spent on advancing AI, and it is predicted that billions more will be spent in the coming years. While many executives view these initial costs as research and development (R&D), most are growing increasingly concerned about realizing benefits and calculating the return on investment (ROI) of these endeavors.
Failing fast and aiming to be the first in an industry is only financially feasible for a short period; the long-term realization of ROI is essential. A Forbes article shares that a typical calculation of project cost that is determined by people, process, technology, and service does not effectively apply to AI projects. That’s because these calculations fail to account for data, and the complexity and costs of managing data in AI scenarios.
So the key question leaders need to ask is, “How can we ensure our AI investments deliver strong ROI?”
AI ROI
In the future, agentic AI will present significant opportunities for organizations to integrate technology across various business functions and accelerate innovation. However, to achieve a positive ROI from any AI initiative, it's crucial that the benefits of AI clearly outweigh the associated costs.
At this stage, there are still many unknowns regarding agentic AI and how it will ultimately help organizations. For most organizations, the full benefits of agentic AI have yet to be realized, and the path to doing so remains an enigma.
To effectively determine the ROI of AI at this early stage, organizations need to define budgets, set clear targets and measurable use cases, and meticulously track development costs. By ensuring data accuracy and measurability, it becomes possible to account for costs and calculate ROI accurately.
Here are some practical examples of how an AI strategy and associated costs can be managed within an organization:
- Leadership AI insights. Leverage expert advice to provide direction on AI initiatives.
- Formulate a strategic AI approach. Establish objectives, key results, and budget parameters.
- AI R&D. Explore options and make investments in AI R&D.
- Data readiness and measurement. Ensure your data is prepared for AI applications and establish clear metrics for success.
- Implementation and continuous improvement. Plan for the successful implementation of AI solutions and foster a culture of continuous improvement.
- AI cost management and ROI. Analyze the financial implications and expected returns from your AI investments.
1. Leadership AI insights
Before embarking on an AI journey, the organization’s leadership should obtain relevant insights that will empower them to navigate the road ahead. This includes seeking expert advice to fully understand the opportunities, market trends, and capabilities, as well as to determine the necessary resources.
Leadership must also invest in increasing their knowledge so they can provide clear direction and establish a well-defined strategy. This involves understanding what aspects require R&D, assessing measurable costs and benefits, determining budget requirements, and outlining how to achieve desired results. While this initial exercise may seem costly, it will ensure effective execution of an AI strategy that can become profitable more rapidly than competitors.
2. Formulate a strategic AI approach
With any new technology, there are many unknown aspects. Plus, overly passionate pioneers can swiftly increase costs. Therefore, a strategic approach is crucial to ensure results are achieved cost-effectively. ValueOps can seamlessly manage AI objectives, control deliverables, organize and track resources, and help any organization realize benefits and ROI.
AI Objectives and Key Results (OKRs)
A strong leadership strategy and clear objectives are crucial for keeping delivery teams and the rest of the organization focused on, and aligned with, key high-level outcomes. The OKR framework provides a clear structure for alignment and accountability.
ValueOps enhances this framework by linking key results directly to delivery features, allowing teams to track and evaluate actual productivity. With ValueOps, teams can capture strategic themes and detailed objectives with supporting KPIs. These KPIs can then be linked to specific delivery items, fostering full alignment. If teams want to take a slightly different approach, they can detail benefit plans as well.
AI benefit plans
Benefit plans are essential for managing and tracking ROI. It's important to remember that not all ROI is financial. This is especially true in the early stages of AI maturation, when defining a detailed financial benefit plan might not be feasible. Instead, by defining targeted organizational benefits, you can ensure your teams are aligned with key outcomes and continue to work in the right direction. Initial benefits might include solution innovation, enhanced competitive advantages, optimization, scalability, and talent development. These are considered non-financial benefits, and ValueOps offers the functionality to capture them. This allows project teams to update progress, enabling the business to evaluate benefit realization.
Once the R&D phase is complete and research outcomes are clear, you can then apply financial benefit plans. As AI applications are implemented across the organization, benefits will shift towards cost savings and revenue increases, including due to enhanced organizational efficiency, productivity, customer response, and strategic positioning.
3. AI R&D
The R&D phase is crucial for realizing the true benefits an organization can achieve from its AI projects. Given the diverse approaches to AI and the varying costs and results, it's essential to keep R&D focused. Clearly defined objectives, key results, and expected benefits will help enthusiastic teams stay on track and prevent the phase from spiraling out of control.
To effectively manage R&D, it will be necessary to bring in talented individuals who can advance the organization's key areas of interest. To ensure that the R&D phase can be properly measured and accounted for, and its benefits realized, I recommend tracking R&D phases and use cases within ValueOps. This enables teams to capture the cost of AI research by tracking initiatives and resource time. Defining budgets for each phase will also help rationalize costs. ValueOps' cost plans can capture both initial budgets and actuals, whether they are fixed assets or ongoing resources.
4. Data readiness and measurement
Getting data ready for AI can be a significant investment, depending on the quality of existing data and governance practices within an organization. Regardless, accurate data is the essential foundation for AI and crucial for current operational productivity.
I recently had a conversation with the head of governance at a leading financial institution, who shared that the Clarity implementation they completed 15 years ago remains a core data source for them.
However, in our interactions with potential new customers, we frequently observe that their fundamental data structures are often disrupted, leading to disorder. Data is crucial for all organizations, and its quality significantly influences productivity. In today's landscape, inaccurate data can lead to the failure of AI initiatives.
For teams who are seeking to implement a tool that fully structures their data and data flow, ValueOps offers a solution. ValueOps delivers comprehensive data that can help teams effectively manage the return on their AI investments.
During the AI R&D phase, a separate data management stream can be initiated to identify system use cases and evaluate data’s readiness for AI. Scope out the AI priority systems and the type of data that is captured within each. An initiative may be required to review each system and the quality of the data. In addition, a second phase may be required to rectify and improve data and tools so they are aligned with planned AI scenarios.
ValueOps data cost management
As teams are scoping and planning these initiatives, the ValueOps solution will calculate the cost involved to complete such initiatives by applying resource day rates to planned work. ValueOps provides complete capabilities for cost planning, including capturing budget and estimating costs based on plans. As the work is completed, actuals will be collected. The cost plan can also manage non-labor costs, such as infrastructure and hardware. This provides full transparency of costs and enables leaders to manage and adapt as required. The same approach for activity and cost management can be applied to the R&D phase and then ongoing transformations.
ValueOps measuring activities and effort
To effectively demonstrate the benefits of AI, it's crucial to first measure an organization's current activity and productivity.
ValueOps offers a robust solution for this effort by capturing and measuring various initiatives, including the time taken for delivery, the team or resource effort involved, and associated fixed costs. It also provides detailed insights into release efficiency, specifying capacity and velocity. Delivery data activities within ValueOps seamlessly create frictionless capture of time, financials, and capital expenditures. This comprehensive data is essential for accurately realizing the benefits of AI enhancements.
5. AI implementation and continuous improvement
Implementing AI within an organization requires a significant culture change. It's crucial that staff not only understand, but also fully support, the implementation and application of AI technologies.
To ensure success, staff will need to validate the outcomes of automated AI tasks and initiatives. Therefore, you should roll out AI use cases in a phased manner, allowing for continuous evolution and verification.
It's clear that many businesses are currently focusing on mastering prompt engineering to maximize AI's utility for daily tasks. The next significant step in this evolution will be the increased trust in AI agents to perform multiple unsupervised tasks. This will undoubtedly require time and careful development.
Whether teams are using internally developed or pre-trained AI models, they will require proper governance and quality approval before wider application. A continuous improvement cycle will be essential to ensure accuracy and to further refine AI solutions. When implementing AI, it's crucial to revisit the business case and then create more detailed business use cases with measurable value. These well-defined cases will help you establish detailed AI objectives, key results, and overall business benefits.
While AI offers industry-wide benefits like reduced operational tasks, improved work quality, and quicker turnaround times, your organization must precisely define which tasks will be measured for cost efficiency. Ultimately, the true investment should be measured by improvements in organizational efficiency, productivity, revenue growth, and enhanced quality.
6. AI cost management and ROI
To measure ROI, you must be able to compare the organization's before-and-after costs. To accurately calculate the ROI, you need to use the data captured within ValueOps prior to AI. This includes information on timelines, effort, budget/costs, infrastructure, R&D, and productivity. You can then compare this data against the performance and operational data captured after AI runs in production.
To calculate the ROI of an AI implementation, you will need to consider the net gain or benefit derived from the AI solution and weigh it against all the expenses incurred prior to the production phase. Without a comprehensive toolset such as ValueOps capturing these activities, efforts, and initiatives, it is not possible to calculate the real ROI for AI. ROI cannot be an afterthought but must be a strategic input planned and measured continually with clear business drivers to ensure successful AI revenue growth. ValueOps will support your business so you can strategize, define, execute, measure, and then finally evaluate your actual ROI. (Find out more about why the success of AI hinges on financial discipline.)
Turning AI ROI from aspiration into evidence
AI does not fail because of a lack of ambition or investment. It fails when organizations cannot see, govern, or measure what they are building. Proving AI’s value requires more than innovation. It requires structure, discipline, and continuous measurement across strategy, delivery, cost, and outcomes. ValueOps provides the foundation to turn AI from an experimental cost center into a measurable, value-driven capability. If you are serious about moving AI from promise to profit, now is the time to put ROI at the center of your AI strategy. Explore how ValueOps can help you plan, govern, and prove the real return on your AI investments, and start building AI initiatives that deliver outcomes you can stand behind.
