The AI Value Gap Is Getting Dangerous

A few scale AI with results. The rest can’t keep up.

October 02, 2025

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Wednesday Deep Dive

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The Wednesday Deep Dive takes a detailed look at what's new in AI. Each week, we share in-depth insights on new tools, proven prompts, and significant developments - helping tech professionals work smarter and stay ahead.

This week’s stories highlight the growing divide in AI adoption. One shows how a small group of companies is pulling far ahead in measurable results, while most are stuck with little to show for their investments. The other looks at a vendor betting that pre-built AI apps can help close that gap.

This week’s stories show both sides of the divide:
📉 BCG warns of a dangerous “AI value gap” leaving 95% of companies behind
⚙️ Reply launches Prebuilt AI apps to speed up adoption and reduce risk

Let's dive in.

🌐 AI News

 📉BCG: Only 5% of companies are capturing real AI value

A new Boston Consulting Group report finds that just five percent of companies are achieving bottom-line results from AI at scale. Meanwhile, 60 percent report little to no value despite heavy investment.

The winners, which BCG calls “future-built” companies, are already seeing 1.7x more revenue growth and 1.6x higher EBIT margins than their peers. They’re moving beyond automation to reinvent workflows in R&D, sales, marketing, and manufacturing.

Key differences between leaders and laggards:

  • Leadership engagement: Nearly all C-level execs at leading firms are hands-on with AI. Only 8% of laggards can say the same.

  • Investment strategy: Leaders are reinvesting gains, spending 120% more on AI than slower competitors.

  • Deployment: 62% of their AI initiatives are already live, compared to just 12% for laggards.

  • Talent: Leaders are upskilling over half their workforce to work with AI, six times more than lagging firms.

One accelerant is agentic AI. These systems can reason, learn, and act with minimal human input. Already 17% of AI value in 2025 comes from agents, projected to hit 29% by 2028. Leaders are moving fast, especially in customer service, while laggards are barely experimenting.

BCG’s warning: the biggest barriers are not technical. They are organizational. Without decisive leadership and structural change, most firms risk being permanently left behind.

🌐 AI News

⚙️Reply bets on Prebuilt AI apps to close the adoption gap

While most enterprises struggle to scale AI, Reply is pushing a different approach: ready-to-use agentic apps designed to cut through integration headaches and speed up deployment.

The Prebuilt lineup includes:

  • Claim Digital Agent: Automates health insurance claims with OCR, AI-driven extraction, and a knowledge graph that improves with each case.

  • HR Assistant: A multilingual, always-on support bot that integrates with HR systems like ServiceNow and SharePoint.

  • Internal Knowledge Optimiser: Turns unstructured content (videos, webinars, documents) into a searchable knowledge base with natural language queries.

The idea is to give companies a faster on-ramp to AI by reducing the need for custom builds and technical expertise. Each app can still be extended and tailored, but the heavy lifting in data processing, workflow automation, and compliance controls is already built in.

Reply is betting that enterprises need practical, low-risk pathways from experimentation to scaled adoption. Instead of endless pilots, these apps aim to deliver measurable impact from day one.

💡 Why this matters

The contrast between BCG’s findings and Reply’s strategy highlights the same truth: AI success depends less on algorithms and more on execution.

  • Leaders are pulling away because they treat AI as a CEO-level transformation.

  • Laggards are stuck because they delegate, spread resources thin, and fail to scale beyond pilots.

  • Vendors like Reply are stepping in with prebuilt systems to bridge the gap, but without leadership commitment, even the best tools will fall short.

The AI value gap is no longer theoretical. It is showing up in revenue, margins, and market share. For most companies, the question is not whether to adopt AI. The question is whether they can do it fast enough to avoid being left behind.

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