Generative AI has taken the enterprise world by storm throughout 2023 – and for good reason. From a digital transformation perspective, it has the potential to amplify efficiency to new heights at a time when it’s needed most.

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.