It is tough these days to get attention for anything without addressing the ability of how it can help with the adoption of AI. Nevertheless, it is not too far a stretch to ponder how technology business management software can contribute to successfully managing AI-driven business innovation.
IBM Apptio is, of course, known for its IT financial management and strategic portfolio planning software, which is typically applied to the use of cloud services in projects. However, as there is a looming shadow AI problem developing in organizations on the one hand, and trouble in execs gleaning the value of AI adoption on the other, Apptio’s tools can help here too.
The visibility gap is growing
At a recent webinar, Roden Zadeh, Director Product Marketing for IBM Targetprocess (Apptio’s portfolio planning software) acknowledged that while all organizations want to innovate, innovation frequently does not take shape in the way senior leaders want it to. According to webinar polling, gaining real-time visibility on where the organisation as a whole is in terms of innovation is the biggest problem practitioners face.
This is the “visibility gap” where the daily work of coders means that they are focusing on a range of different things and leaders do not know what each team is actually working on, what should be accelerated, delayed or stopped and how these activities align with overall objectives. As Zadeh expressed it, “are we working on what really matters or are we just keeping busy?”
At the moment innovation is largely about investment in AI. However, IBM’s Value Institute’s 2025 CEO Outlook found that only 25% of surveyed CEOs reported that AI initiatives have delivered expected ROI, and only 16% have scaled such initiatives enterprise wide. Nearly two-thirds of CEOs surveyed said that the risk of falling behind, drives investment in some technologies before they have a clear understanding of the value they bring to the organization. Furthermore, 59% of CEOs surveyed for the report admitted that their organization struggles to balance funding for existing operations and investment in innovation when unexpected change occurs.
Such data suggests that the visibility gap makes it difficult in a fast-changing world for organizations to ensure that AI models and Large Language Model (LLM) constructs are remaining aligned with organizational goals.
Keeping AI innovation on track
Zadeh suggests that the innovation development portfolio, the resources working on it and the financial data surrounding it needs to be combined into one source made accessible to managers. In this way strategy and execution are more likely to remain connected because visual project management software enables senior execs to be aware of lack of resources, funding problems and delays.
This means that the way a strategy is executed can be steered between different planned scenarios rather than getting lost. Using the analogy of planning software as a GPS navigational aid, Zadeh claims that “Targetprocess can get you back on track when you miss your exit.”
Focusing on strategic planning Zadeh explains:
We need to think about capabilities in a continuous fashion and ask ourselves, what can we change if things are not aligning?
To do this, execs need up-to-date operational information. However, teams frequently do provide this information but it turns out not to be that helpful to senior managers. This is because everyone is working to their own metrics which leads to inconsistency in understanding where organizations are in executing their strategy. And because management does not have coherent visibility it is very difficult to assess what impact each action will have on the strategic outcome. It is also virtually impossible to think about longer term (three to five year) strategy impacts on issues such as software capitalisation, for example.
Zadeh asserts that strategic planning is “really workflow”. Targetprocess creates a connected process closed loop combining organizational strategy themes with planned results and objectives, budget allocation, progress and resource tracking and reporting to generate ROI metrics. In this way executives get a bird’s eye view of the organization’s AI investment strategy via dashboard results presenting strategic impact, customer value and required resources in terms of people, time and finance.
To provide a more granular understanding of AI innovation, Apptio has also recently launched its AI TCO & Usage functionality so that organizations can monitor the lifecycle of their AI investments, tracking ongoing AI costs and usage across AI models and apps. This software surfaces AI usage and user adoption across business units to support more informed AI scaling decisions by assessing unit economics and consumption. For example, the tool can provide visibility into teams paying for a new AI app that has not been reviewed by IT, or one that duplicates an app already being used elsewhere in the organization.
My take
Technology visibility challenges still exist around cloud transformation projects, where cost control and ROI continues to prove problematic for many organizations. Because this experience is still current, organizations are trying to get a jump on stopping the same thing happening with innovation around AI adoption. This is why technology business management software vendors are waking up to the requirement very early in the enterprise AI adoption cycle. Expect more tools to launch/be repositioned in this area over the next year or two.
