How can organisations fix AI value remains elusive despite soaring investment and turn it into measurable customer value?

AI value remains elusive despite soaring investment, even as boardrooms funnel capital into generative models and automation pilots. This paradox grabs headlines because companies report large AI budgets yet little customer impact. However, the gap between spending and tangible results grows wider in many organisations. In this article we unpack that gap, explain the barriers and map practical fixes.

First, we will show why AI investment often fails to translate into customer value. We will examine common problems such as fragmented data, costly implementations, integration friction and shadow AI. Next, we will explore how open source, hybrid cloud strategies and stronger software supply chains can restore control and speed adoption. Moreover, we will highlight the role of agentic AI and talent changes in moving projects from experiment to production.

Along the way we will use recent survey findings and real examples to make the case pragmatic and actionable. As a result, readers will gain a clear checklist for shortening time to value. Because uncertainty breeds wasted effort, we focus on governance, measurable outcomes and low friction integration steps. By the end you will understand why customer value lags and what leaders should do to fix it.

Why AI value remains elusive despite soaring investment

Capital pours into AI but customer outcomes remain scarce. However, the headline numbers tell only half the story. Organisations plan to increase AI investment by 32 percent by 2026, yet 89 percent have not seen customer value from their AI work. Because of this gap, leaders worry about wasted spend and missed opportunities.

Key investment trends and data points

• Organisations anticipate a 32 percent increase in AI investment by 2026.
• Eighty nine percent of businesses report no customer value yet from AI.
• Sixty two percent list AI and security as top IT priorities over the next 18 months.
• Thirty four percent cite high implementation and maintenance costs as a major concern.
• Eighty three percent report unauthorised employee use of AI tools, also known as shadow AI.

Moreover, market signals show a mixed outlook. For example, companies lean toward open source to gain control and flexibility. Red Hat provides practical guidance for hybrid cloud and open source approaches that speed AI adoption. Likewise, IBM offers analysis on enterprise AI investment and return on investment. As a result, organisations are balancing heavy investment with governance and operational control.

Finally, this section previews the rest of the article. We will explain why budgets do not convert to customer benefit. Then we will map practical fixes such as stronger data integration, clear metrics, open source strategy and tighter software supply chain controls. Readers will gain an actionable view of how to turn investment into measurable customer value.

Why AI value remains elusive despite soaring investment: challenges and obstacles

The money flows, yet customer outcomes lag. Because of several persistent barriers, organisations often fail to turn AI investment into measurable value. This section breaks down the main challenges. It draws on survey findings, expert views and practical examples to explain why projects stall.

Data quality and readiness

• Poor data ruins AI outcomes. Models need clean, labelled and representative data. However, many teams inherit fragmented and inconsistent sources. As a result, models perform poorly in production.
• Evidence shows data problems are common. Eighty nine percent of organisations report no customer value yet from AI, partly due to data gaps.
• To fix this, focus on data products, feature stores and governance early in the lifecycle.

Integration complexity and legacy systems

• Integrating AI into existing systems is hard, especially with legacy software. Twenty eight percent of respondents cite integration as a top barrier.
• Legacy APIs, brittle ETL pipelines and mismatched data schemas slow deployment. Therefore, proofs of concept often remain isolated pilots.
• Adopt MLOps practices and invest in middleware to bridge new models with enterprise systems.

Skills and talent shortages

• Talent remains scarce, because demand outstrips supply. Organisations lack data engineers, MLOps practitioners and product-minded AI builders.
• The result: teams over-rely on external vendors or siloed specialists, which reduces internal ownership and slows iteration.
• Ramp up reskilling and cross-functional hiring to close the gap, and create clear career paths for AI roles.

Unrealistic expectations and poor governance

• Hype inflates expectations, and leaders expect fast, large returns. However, many projects are undervalued in planning and poorly measured.
• Shadow AI widens the problem. Eighty three percent report unauthorised use of AI tools, which creates compliance and quality risks.
• Therefore, pair ambition with metrics, guardrails and clear KPIs to measure customer outcomes.

Expert perspective and practical signals

• Red Hat stresses openness and hybrid cloud as enablers of sustainable AI adoption, because they improve reuse and transparency Red Hat. Moreover, enterprise analyses from IBM explain the difficulty of converting AI investment into realised ROI IBM. For practical industry commentary, TechForge covers emerging product and integration issues in depth TechForge.

In short, AI value remains elusive despite soaring investment because technical debt, organisational friction and inflated expectations block the path. However, pragmatic investments in data, MLOps, talent and governance can change the outcome.

Note: These figures illustrate the common gap between investment and customer value reported across the industry; 89 percent of businesses report no customer value yet from AI.

Case studies: three stories of elusive AI value

1. A large bank chasing personalization

A major UK bank invested heavily in customer personalization. The firm spent millions on models and data platforms. However, integration with core banking systems proved slow and brittle. Because legacy APIs and fragmentary customer records mismatched model inputs, pilots remained isolated. As a result, customer-facing features saw only marginal uptake. Moreover, internal teams blamed unclear KPIs and unrealistic timetables. The bank learned two hard lessons. First, align measurement to real customer metrics before scaling. Second, prioritise data engineering and MLOps to move models into production.

2. A regional health system aiming for predictive care

A regional health system deployed AI to predict patient deterioration. The promise seemed clear and urgent. Yet regulatory reviews and data privacy checks delayed rollout for months. Meanwhile, data scientists struggled with inconsistent electronic health records. Consequently, the model performed well in lab tests but poorly in clinics. Because clinician workflows did not change, adoption lagged despite improved accuracy scores. The takeaway is practical. Invest in governance and clinician engagement early. Also, design for operational fit rather than for academic benchmarks.

3. A global retailer betting on inventory automation

A global retailer launched an AI initiative to optimise inventory and reduce stockouts. The company bought leading models and integrated edge sensors. Initially, the project reduced manual effort. However, high implementation and maintenance costs eroded returns. Furthermore, shadow AI practices emerged as local teams used unsupported automation scripts. Therefore, ROI faltered despite visible efficiencies. The retailer’s corrective steps were concrete. It consolidated tools, enforced governance, and built a central feature store. Consequently, the initiative began to show steady but slower gains.

Key lessons across cases

• Technical debt and legacy systems delay value realisation. 28 percent cite integration as a barrier.
• Governance, measurable KPIs and operational design matter more than cutting-edge models.
• Finally, invest in talent, MLOps and open practices to convert experiments into customer outcomes.

These stories show why AI value remains elusive despite soaring investment. They also point to practical fixes leaders can adopt.

Why AI value remains elusive despite soaring investment: new approaches and strategies

Organisations are adopting smarter tactics to close the gap between spend and customer impact. Because experimentation alone no longer suffices, companies now pair investment with engineering and governance. The following approaches show how leaders shift from hype to durable value.

Data centric engineering

• Treat data as a product rather than as an afterthought. Clean, labelled and governed data speeds model reuse and reduces drift.
• Use feature stores and versioned datasets to ensure repeatable production behaviour. As a result, teams avoid common data surprises.
• Invest in continuous data validation and bias checks to protect customer outcomes.

Hybrid cloud and open source bedrock

• Organisations embrace hybrid cloud and enterprise open source to retain control and flexibility. For example, Red Hat highlights open practices that improve portability and reuse Red Hat.
• Open source reduces vendor lock in and enables faster experimentation across environments.
• Therefore, this approach supports sovereignty and software supply chain resilience.

MLOps, automation and observability

• MLOps pipelines automate testing, deployment and rollback. Consequently, models move from lab to live faster.
• Observability gives teams signal on drift, latency and real business metrics. Without it, teams cannot prove customer impact.
• IBM provides practical frameworks for measuring AI investment against business outcomes IBM AI Investment.

Human in the loop and governance

• Human oversight improves trust and reduces error in critical flows. Moreover, it helps teams tune models in real settings.
• Strong governance lowers shadow AI risks. Eighty three percent of organisations report unauthorised AI use, so controls matter.
• Define simple KPIs, guardrails and approval flows before scale.

Agentic AI and pragmatic pilots

• Some firms prioritise agentic AI for automation where it fits. However, they deploy in narrow, measurable domains first.
• TechForge and other industry outlets document pilots that focus on operational wins rather than research metrics TechForge.
• Start small, measure customer impact and scale when KPIs prove out.

A practical roadmap

• First, map use cases to clear customer KPIs.
• Next, invest in data engineering and MLOps to create a reliable delivery path.
• Finally, pair open source and hybrid cloud to retain flexibility and control.

These strategies help convert investment into measurable customer value. They reduce risk, speed iteration and make outcomes repeatable. Consequently, leaders can move beyond headline spend to real business impact.

Quick fact

89 percent of organisations report no customer value yet from their AI investments.

However, organisations expect a 32 percent increase in AI investment by 2026.

Therefore, investment growth far outpaces realised returns.

As Joanna Hodgson observed, “The gap between ambition and reality is clear.”

Key takeaway: focus on measurable customer KPIs, data engineering and governance to close the gap.

Conclusion: Turning investment into impact

AI value remains elusive despite soaring investment, but the path forward is clear. Organizations poured capital into models, yet customer ROI lags because of data debt, integration frictions and weak governance. However, new practices bridge the gap and make AI deliverable.

First, prioritise measurable customer outcomes. Start with clear KPIs tied to revenue, retention or cost reduction. Next, invest in data products, feature stores and MLOps so models behave reliably in production.

Moreover, adopt hybrid cloud and enterprise open source to retain control and avoid vendor lock-in. Human-in-the-loop and strong governance reduce risks from shadow AI. Agentic AI can automate tasks, but deploy it in narrow domains that show real value.

For leaders, the roadmap is practical. Map use cases to outcomes. Build cross-functional teams that include product, engineering and compliance. Fund the messy work of data engineering before buying exotic models. Finally, measure, observe and iterate.

EMP0 helps companies follow this playbook. EMP0 provides AI and automation solutions that multiply revenue with secure, production-ready systems. Visit EMP0’s website to learn about products, case studies and deployment options. Also read practical guidance and thought leadership on the EMP0 blog.

If you want to move beyond pilots, start small and scale with discipline. Contact EMP0 to explore pilot programs, integration support and governance packages. Together, you can turn headline spend into measurable customer value. In short, investment alone won’t suffice. However, with the right strategy and partners you can convert AI budgets into real business impact.