AI Value Remains Elusive Despite Soaring Investment
Stakeholders are starting to ask why boards pour millions into models, tools, and cloud capacity. Yet customers rarely see clear returns.
For example, survey findings show 89 percent of businesses report no customer value. Moreover, firms plan a 32 percent increase in AI investment by 2026. Therefore, this gap between spending and ROI raises urgent questions.
Barriers to Value Realization
However, several barriers slow value realization. High implementation costs and maintenance top many lists. Data privacy and security concerns compound the problem. Additionally, shadow AI and legacy systems block smooth integration.
Dissecting the Real Reasons
In this article, we will dissect the real reasons. We will examine strategy, culture, talent, and open-source roles. Finally, we propose practical steps to move from experiments to measurable customer value.
Unpacking Challenges
We will unpack cost drivers, data challenges, and skill gaps. For instance, many cite high costs as the primary barrier. Others worry about data privacy, security, and governance. Also, teams battle with integration and lack of reuse across systems.
As a result, pilots stall and promised ROI slips away. Shadow AI and uncontrolled experimentation introduce risk and inefficiency. Meanwhile, open-source platforms offer a path to reuse and transparency. Therefore, exploring open-source and hybrid cloud strategies matters. We will also highlight governance, tooling, and measurable KPIs.
AI value remains elusive despite soaring investment: The paradox explained
Many firms pour money into models, cloud, and talent. Yet customers often see little benefit.
Several causes explain this mismatch. Below are the main reasons and challenges.
- Strategy and measurement. Organisations lack clear KPIs, so pilots do not translate to customer value. See our guide for fixes: AI Value Guide.
- High costs and maintenance. Thirty four percent cite costs as the top barrier.
- Data privacy and security. Thirty percent worry about data protection and compliance.
- Integration friction. Legacy systems block reuse and deployment across platforms.
- Talent and skills gap. Teams cannot productionise models at scale.
- Shadow AI risk. Eighty three percent report unauthorised tool use by employees.
- Governance and tooling. Weak governance slows deployment and raises supply chain risk.
- Fragmented vendor stacks. Multiple point solutions prevent standardisation and efficiency.
- Missed reuse and open source practices. Open source can improve reuse and transparency, as Red Hat highlights: Red Hat Open Source.
Therefore, closing these gaps requires clearer KPIs, stronger governance, and pragmatic open-source approaches.
As a result, ROI stays hypothetical for many firms. Leaders must measure customer outcomes, not model metrics. In addition, invest in integration and change management. Finally, reuse and open standards cut costs and speed delivery.
Common misconceptions about AI value
AI value remains elusive despite soaring investment, and myths make the gap worse. However, leaders often chase the wrong signals. They focus on model accuracy, not customer outcomes. As a result, projects deliver technical wins with little business impact.
Common misconceptions and reality checks
- Overhyped benefits. Organisations assume AI will instantly transform revenue. In reality, most early projects improve internal metrics, not customer value. For example, a high-accuracy model may still fail in live workflows.
- Unrealistic expectations. Teams expect rapid ROI. Therefore, they skip change management and integration. This leads to stalled pilots and wasted spend.
- Technology-first thinking. Companies buy tools before defining use cases. As a result, technology outpaces strategy and nobody owns delivery.
- Data solves everything. Data matters, but poor governance and fragmentation block reuse. Without clean pipelines, models degrade quickly.
- Human oversight is optional. Some leaders trust models without guardrails. This causes bias, errors, and regulatory risk.
Practical takeaway
Stop treating AI as a magic box. Define measurable customer outcomes. Align KPIs to revenue, retention, or cost-to-serve. Also, invest in integration, governance, and skills.
For pragmatic steps and governance templates, see our guide: AI Value Guide.
Evidence and statistics: the spending versus outcome gap
Recent surveys expose a stark mismatch between AI budgets and customer outcomes. For example, 89 percent of businesses report no customer value from AI. Moreover, firms plan a 32 percent increase in AI investment by 2026. Therefore, spending rises while measurable benefits lag.
Key figures at a glance
- 89 percent of businesses yet to see customer value, per survey findings.
- 32 percent projected increase in AI investment by 2026.
- 34 percent cite high implementation and maintenance costs as the biggest concern.
- 30 percent list data privacy and security as top barriers.
- 28 percent struggle with integrating AI into legacy systems.
- 83 percent report unauthorised employee use of AI tools, also called shadow AI.
- 84 percent rate enterprise open source as important for their AI strategy.
- 68 percent prioritise Agentic AI and 83 percent see the UK as a potential AI powerhouse.
What this data shows
First, organisations fund models and cloud at scale. However, they often omit metrics tied to customers. As a result, pilots remain proofs of concept. Also, costs and security concerns constrain deployment. For instance, Red Hat and other vendors note that open-source can help reuse and reduce vendor lock-in. In addition, leaders such as Joanna Hodgson highlight the gap between ambition and reality. Therefore, governance and integration matter as much as model performance.
Other figures deepen the concern. Sixty two percent of respondents name AI and security as IT priorities over the next 18 months. In addition, 34 percent call high costs the main barrier, and 30 percent flag data privacy issues. Meanwhile, 83 percent worry about shadow AI. These numbers show risk and readiness gaps. Hans Roth and survey leads warn that without clear governance projects stall.
Actionable interpretation
To close the gap, measure customer-facing KPIs. Also, invest in pipelines, governance, and skills. Finally, curb shadow AI and reuse open-source components to lower costs and speed delivery. See practical steps here: practical steps.
| Investment category | Typical spend | Value realization | Common obstacles |
|---|---|---|---|
| Sales automation | Medium often 5 to 15 percent of AI budget | Moderate adoption. Many pilots improve lead scoring but yield small revenue uplift at scale | CRM integration, sales process change, data silos |
| Marketing automation | Medium often 5 to 12 percent of AI budget | Variable. Personalization lifts engagement but attribution is weak | Fragmented data, poor measurement, vendor complexity |
| Customer service AI | High often 10 to 25 percent of AI budget | Mixed. Chatbots reduce simple contacts but fail on complex queries | Legacy systems, escalation workflows, governance gaps |
| Supply chain and logistics | Medium to high 8 to 20 percent of AI budget | Promising but slow. Planning models save cost but require clean data | Data quality, systems integration, change management |
| R and product innovation | Medium 5 to 15 percent of AI budget | Long horizon value. New features appear slowly and require product fit | Long development cycles, unclear KPIs, skills gap |
| IT and operations including MLOps | High 15 to 30 percent of AI budget | Foundational value when done well; reduces run costs and speeds deployment | Tooling debt, vendor lock in, need for open source and governance like Red Hat suggests |
Practical strategies to unlock AI value
To turn AI investment into measurable customer value, leaders must act with focus. Start with outcomes, not tech. Define KPIs tied to revenue, retention, or cost-to-serve. Because pilots often chase novelty, assign a business owner to each initiative.
Key steps and best practices
- Define outcome metrics and success criteria
- Link models to business KPIs and short feedback loops. Measure lift against control groups.
- Prioritise data plumbing and governance
- Clean, documented data reduces model drift and lowers maintenance. Therefore, invest in pipelines, catalogues, and data contracts.
- Build MLOps and reuse patterns
- Automate testing, deployment, and monitoring. As a result, teams move models to production faster and cut run costs.
- Adopt open-source components sensibly
- Open tools increase reuse and reduce vendor lock-in. For practical templates and governance ideas see our guide: AI Value Guide and Red Hat resources for enterprise patterns: Red Hat Resources.
- Tackle shadow AI and security
- Approve sanctioned tools, create guardrails, and monitor usage. Meanwhile, run training on data privacy and acceptable use.
- Align cross functional teams early
- Involve product, IT, legal, and operations from day one. This speeds integration and improves change management.
- Invest in skills and product roles
- Hire platform engineers and product owners. Train line teams to interpret model outputs and act on insights.
Quick pragmatic tips
- Start small with measurable bets. Stop projects that do not show early customer impact.
- Reuse models and components across teams.
- Finally, document outcomes and scale what works.
These steps reduce cost, improve ROI, and make AI deliver real customer value. Consequently, organisations move from experimentation to sustained production and measurable outcomes.
EMP0: Full-stack AI solutions that deliver measurable growth
EMP0 is a full-stack AI solution provider that helps companies multiply revenue by deploying AI-powered growth systems on client infrastructure. We build end-to-end products, from data pipelines to production agents, so return on investment arrives faster. Because EMP0 runs inside customer environments, data sovereignty and compliance remain with the client. This reduces vendor lock-in and eases security audits.
Key offerings include Content Engine, Marketing Funnel, Sales Automation, Retargeting Bot, and proprietary AI tools. Content Engine automates content creation and personalization across channels. Marketing Funnel orchestrates campaigns and measures attribution. Sales Automation streamlines lead scoring and outreach. Retargeting Bot increases conversion through intelligent digital reengagement. Proprietary tools cover model training, monitoring, and custom agent deployment.
EMP0 combines platform engineering, MLOps, and growth product expertise. As a result, teams move models into production rapidly and sustain them. EMP0 emphasises measurable outcomes, not models for their own sake. Therefore, KPIs focus on revenue lift, retention, and cost-to-serve. Furthermore, EMP0 offers integration templates and governance frameworks to reduce shadow AI risk and technical debt.
What makes EMP0 unique is the end-to-end approach plus client-first deployment. In addition, the company balances open-source building blocks with proprietary orchestration. This yields transparency, reuse, and faster time-to-value. Finally, EMP0 supports training, change management, and cross-functional alignment. Consequently, organisations accept AI as a repeatable growth capability, not a one-off experiment.
Clients report measurable uplifts within months because EMP0 ties deployments to existing revenue streams and customer journeys. The team also provides SLA-backed operations and clear ROI reporting and analytics.
Conclusion
AI value remains elusive despite soaring investment for several clear reasons. Boards increase budgets, yet organisations lack outcome-driven KPIs. As a result, pilots multiply without customer impact. Moreover, high implementation costs and data governance issues stall production. Shadow AI and integration friction add risk and inefficiency.
However, the gap is not inevitable. Organisations that tie models to revenue, retention, or cost-to-serve see faster payback. Therefore, focus on measurable outcomes, strong data plumbing, and MLOps. In addition, enforce governance and curb unsanctioned tool use. Finally, align cross-functional teams early to speed adoption and reduce technical debt.
EMP0 helps bridge the divide with full-stack AI systems deployed on client infrastructure. The company delivers Content Engine, Marketing Funnel, Sales Automation, Retargeting Bot, and proprietary orchestration tools. Because EMP0 runs inside customer environments, clients keep sovereignty and reduce vendor lock-in. As a result, teams move from experiments to repeatable revenue streams. EMP0 also provides governance templates, MLOps, and SLA-backed operations to ensure sustained value.
In short, fixing the ROI problem requires discipline, reuse, and outcome-first builds. EMP0 combines those elements into production-ready solutions. To learn more, visit EMP0’s profiles and resources below.