AI adoption is accelerating across industries and regions - but many enterprise AI initiatives still get stuck in pilots. The result is small, isolated successes instead of measurable business outcomes, scalable business impact, and repeatable AI ROI.
AI value realization starts with outcomes. Build a value case per use case, assign accountable owners, and define KPIs and baselines early. Then align the enterprise operating model - governance, processes, data foundations, and people - so you can scale from one team to enterprise-wide AI transformation.
(1) “Four Futures for Jobs in the New Economy: AI and Talent in 2030”, World Economic Forum White Paper, 2026.
(2) IDC FutureScape 2026 Predictions: AI to Drive 50% of New Economic Value from Digital Businesses in APJ* by 2030
Episode 6 of the Fujitsu impact series shows how to move from standalone AI use cases to enterprise AI outcomes you can measure. You’ll learn how to create an enterprise AI playbook, engineer value upfront, and apply value management to prove and sustain AI ROI at scale - across regions and business units. Watch the recording to hear the details from Kartik Ravel, Head of Business Value, Fujitsu Consulting Americas, and IDC guest speaker Stephanie Krishnan, Associate Vice President, Research, Manufacturing and Energy Insights, IDC Asia/Pacific.
Access the recording below for the full episode to learn how to realize AI value.
This session is for leaders responsible for enterprise AI value realization, including CIOs and IT leaders, CDOs and data leaders, and C-suite transformation executives in global and multi-national organizations. If you need to move from experimentation to measurable business impact - clear outcomes, KPIs, and AI ROI - this episode is for you.
“Value must be defined upfront, not discovered later. The 'value gap' we see in the market usually comes from skipping the baseline; then value becomes subjective.”
Kartik Ravel, Head of Business Value, Fujitsu Consulting Americas
Unlock deeper insights into the strategies, structures, and real-world practices that help organizations turn AI ambition into measurable business impact. Explore the extended white paper for a more comprehensive view of what it takes to build enterprise-ready AI across industries and regions - with an accompanying infographic capturing the key insights at a glance.
Align to business outcomes: Set clear goals, track the right KPIs, and improve continuously.
Prove AI ROI: IDC forecasts that by 2027, 50% of CIOs will need enterprise AI value playbooks to measure business impact.
Get AI-ready: Ensure the right AI governance, operating models, and readiness across data, processes, and people.
Build the 'AI Flywheel': Transition from isolated, expensive pilots to a self-funding ecosystem of continuous AI value.
Head of Business Value, Fujitsu Consulting Americas
Associate Vice President, Research, Manufacturing and Energy Insights, IDC Asia/Pacific
MODERATOR
Global Thought Leadership Evangelist
Enterprise AI adoption is accelerating, yet AI value realization is lagging behind. Despite widespread AI and GenAI deployment, many organizations struggle to translate AI activity into measurable business impact and ROI. This Fujitsu impact series white paper provides a practical executive playbook to close the gap between experimentation and enterprise value.
Written for C-suite leaders, CIOs, and digital executives, the paper provides:
With real-world insights and a proven AI value framework, this white paper helps leaders govern enterprise AI as a portfolio and turn AI investments into sustained business outcomes.
Many enterprise AI initiatives fail to deliver measurable business outcomes. This Fujitsu impact series infographic explains why AI value realization is still hard to achieve and how executives can change that.
It highlights the core causes of the AI value gap, from
The infographic shows how AI value must be designed upfront, aligned to operational KPIs such as throughput, quality, and time-to-market. It also outlines the enterprise capabilities required for success, spanning process readiness, data readiness, and organizational readiness, supported by proven value management tools and IDC insights.
The Oizumi Plant of SUBARU’s Gunma Manufacturing Division has greatly streamlined the quality assurance process for engine camshafts by utilizing artificial intelligence.
AI heightens the accuracy of camshaft grinding and enables optimum quality and production throughput, avoiding time-consuming manual inspection.
AI value realization is the discipline of turning AI activity into measurable business outcomes. It links AI initiatives to clear objectives, defined KPIs and baselines, accountable owners, and a value management approach that proves and sustains AI ROI over time.
Many pilots stall because value is not defined in advance: the business case is weak, KPIs and baselines are missing, and ownership is unclear. At enterprise scale, teams also need the right AI operating model - governance, processes, data foundations, and change management - to move from pilots to production consistently across regions and business units.
A practical enterprise AI playbook typically includes: value case templates for priority use cases, KPI definitions and baseline methods, governance and decision rights, data readiness requirements, delivery patterns to industrialize AI/GenAI, and a portfolio view for prioritization. The goal is repeatable execution - so impact can be measured, proven, and scaled.
Start by agreeing what “value” means for each use case (cost, revenue, risk, customer experience, productivity, quality). Set a baseline, define KPIs, and assign accountable owners. Then use value management tools - such as Benefits Registers, Results ChainsTM, dashboards, and AI agents - to trace outcomes end-to-end and monitor results after deployment.
An enterprise AI operating model aligns strategy to execution. It typically covers governance, portfolio management, delivery processes, data and platform standards, roles and skills, and the ways of working required to scale across regions, business units, and functions - while keeping KPIs and accountability consistent.
Focus on a small set of high-impact use cases, build the value case for each, and standardize KPI and baseline definitions early. Establish governance and delivery patterns that take solutions from pilots to production and invest in data readiness and organizational change management. This creates an enterprise-ready foundation for sustained business impact.
Manufacturing and supply chain teams often struggle to move from promising pilots to production because data is fragmented across plants and partners, KPI baselines are inconsistent, and ownership spans multiple functions. Start by prioritizing a small set of use cases tied to operational outcomes, define baselines and KPIs (for example throughput, quality, and time-to-market), and set clear accountability. Then standardize governance, data readiness, and delivery patterns so improvements can be measured, proven, and scaled across sites and business units.
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