Practical Considerations for Bridging the AI Value Realization Gap | AWS

Artificial Intelligence (AI) Is Transforming Business — But the ROI Gap Remains

Artificial Intelligence (AI) is reshaping business operations on a global scale.

Gartner® predicts that by 2028, at least 15% of routine business decisions will be made autonomously through agentic AI.

Meanwhile, McKinsey reports that 92% of companies are increasing their AI budgets.

Yet, despite rising investment, most companies still struggle to show a clear positive impact of AI on their P&L.

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The Scope of the Challenge

According to S&P Global Market Intelligence:

> “The share of companies abandoning most of their AI initiatives jumped to 42%, up from 17% last year [2024],” in the first half of 2025.

Similarly, Gartner warns:

> “Over 40% of agentic AI projects will be canceled by the end of 2027.”

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Closing the AI Value Gap

The disconnect between growing budgets and measurable results is clear.

Success lies in moving beyond fragmented experiments to strategic, enterprise‑wide AI programs.

McKinsey notes:

> “Organizations that integrate AI into core processes — and align it with business objectives — see the most substantial returns.”

Keys to Successful AI Integration

• Scalability — AI initiatives must be designed to grow across departments and geographies.
• Relevant Metrics — Track KPIs directly tied to profit, efficiency, or customer satisfaction.
• Workflow Integration — Embed AI into daily operations, not just isolated pilot projects.

Emerging ecosystems like AiToEarn官网 offer practical solutions, enabling:

• AI‑driven content creation
• Publishing to multiple platforms simultaneously
• Cross‑channel performance analytics

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Figure 1: Six considerations for successful AI transformation and sustained value realization

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1. Business Leaders Must Own the AI Agenda

AI success requires broad cross‑functional leadership, not just IT oversight.

Align AI strategies with core business decision‑making from the start.

Example:

A global institutional investment firm began by:

• Defining relevant AI technical and business roles.
• Implementing an operating model for AI product delivery.
• Launching a dedicated data & AI organization to monetize new market opportunities.

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2. Redesign Incentives for AI‑First Operations

AI transformation must reward true adoption — tying career growth to usage and business results.

Best Practices:

• Define outcomes that matter.
• Shift from traditional input‑based metrics to value‑driven result tracking.
• Link incentives to measurable AI performance.

Example:

A global firm made automation levels a core Product Manager KPI and redesigned its performance framework to prioritize AI‑augmented workflows.

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3. Put People First — HR as a Strategic Driver

Human Resources must:

• Align culture, talent, and incentives with AI goals.
• Champion communication on purpose, benefits, and impact of AI.
• Provide custom learning pathways and real-time feedback loops.

Example:

HR at a global financial institution drove adoption of a new product operating model by:

• Partnering with AWS for executive AI leadership training.
• Aligning operations, business units, and tech teams.
• Embedding AI into revenue‑driving product workflows.

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4. Establish Guardrails Without Slowing Progress

AI governance should balance centralization (for compliance) and federation (for innovation).

Framework Example:

• Enterprise Level — security/compliance via policy as code.
• Line-of-Business Level — value‑stream–specific data policies.
• Solution Level — individual model risk/performance thresholds.

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5. Work With the Right Partners

The right partner brings:

• Industry Insight
• Technical Expertise
• Change Management Support

Example:

A global insurance company used a "teach-to-fish" partner model to:

• Identify high‑value use cases
• Train teams on AI adoption
• Build sustainable governance for AI agents

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6. Track Outcomes That Matter

Instead of static cost projections:

• Focus on baselined, measurable outcomes
• Example metrics:
• AI‑handled customer conversations
• Revenue uplift from AI‑recommended actions

Case Study

A marketing team tackled costly localization errors using generative AI.

Results:

• QA time reduced by hundreds of hours
• Rework costs significantly lowered
• Production speed improved

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7. Dimensions of an AI‑First Organization

Transformation spans seven core areas:

• Vision & Strategy — align AI with business goals
• Process Redesign — optimize human‑AI collaboration
• Culture & Change — embed AI behaviors
• Infrastructure & Ops — scalable, resilient systems
• Skills & Talent — continuous AI learning
• Security & Ethics — responsible deployment
• Industrialization — seamless AI automation

Figure 2: Seven dimensions of AI‑First transformation

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Conclusion

Becoming AI‑first is not about isolated projects — it’s about synchronizing people, process, and technology under a unified strategy.

Organizations embracing this holistic approach enjoy:

• 45% greater cost savings
• 60% higher revenue growth
• (Source: BCG)

Platforms like AiToEarn官网 can serve as enablers for this transformation — combining AI content creation, publishing, analytics, and monetization under one open‑source framework.

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Authors:

• !image Sergio Klarreich — Senior Manager, Customer Success at AWS
• !image Joseph Badalamenti — Senior Customer Success AI Specialist at AWS

Both lead enterprise‑scale AI transformation programs, helping organizations bridge the AI strategy vs. ROI gap.

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For more on becoming AI‑first:

• Contact your AWS account team
• Read the AWS AI blog

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Would you like me to also prepare a condensed executive summary section at the top so this becomes more boardroom‑ready? That could make the article faster to digest for decision-makers.