Three Ways to Turn GenAI Investments Into Measurable Enterprise ROI | Anna E Molosky

 Organizations continue to invest heavily in GenAI, yet only a small fraction are realizing quantifiable financial returns. According to Anna E. Molosky, the challenge isn’t GenAI itself — it’s the way enterprises structure, sequence, and scale their AI initiatives.

Below are three actionable strategies leaders can use to translate GenAI investment into enterprise-level ROI.

1️⃣ Begin Where ROI Is Strongest: Automate Back-Office Operations

Many organizations still concentrate early GenAI spending on customer-facing functions, particularly Sales and Marketing. But as Anna E. Molosky emphasizes, the fastest and most defensible ROI consistently comes from operational domains like Finance, HR, shared services, and supply chain.

Redirecting budget toward targeted, process-level automation — including non-GenAI solutions that integrate cleanly with existing systems — drives:

  • Reduced manual effort
  • Lower outsourcing costs
  • Higher process reliability
  • Measurable productivity gains

One example: AT&T’s enterprise automation program reclaimed 16.9 million minutes of manual work, delivered 20x ROI, and created hundreds of millions in annualized value.

Molosky’s viewpoint: Treat early GenAI investment like capital allocation. Fund the operational engine first.

2️⃣ Let Employee Behavior Guide AI Prioritization

Anna E. Molosky notes a critical disconnect:

  • 90% of employees use personal AI tools to automate their work
  • Only 40% of companies provide enterprise-grade AI access

Your workforce is already demonstrating which tasks are ripe for automation — often without formal visibility. Instead of guessing where productivity gains lie:

  1. Identify the tasks employees already automate with personal AI.
  2. Quantify the value impact of those tasks.
  3. Build or procure tools that institutionalize those high-value workflows securely and at scale.

Molosky argues that this “shadow AI” activity is a strategic asset. It’s a crowdsourced map of your highest-impact use cases.

3️⃣ Set AI Timelines That Reflect Enterprise Reality

A common misconception across the C-suite: AI should show ROI within months.
Reality — and one that Anna E. Molosky stresses — is that enterprise-wide digital transformation typically takes 1–3 years, depending on complexity and integration depth.

Short-term studies and pilot windows cannot capture the full value curve of enterprise AI. Many projects deemed “failures” are simply early in their lifecycle.

Rather than interpreting the “95% failure” narrative as a deterrent, Molosky reframes it as a maturity signal. The 5% generating positive ROI represent what disciplined, properly sequenced AI programs can achieve.

Molosky’s Bottom Line: AI ROI Is Achievable — With the Right Sequence

To transform GenAI investments into P&L impact, enterprises must:

  • Reallocate early spend toward back-office automation
  • Build on employee-driven AI behavior to prioritize use cases
  • Adopt realistic timelines for global-scale deployment

When organizations treat AI as a long-term operational capability — not a short-term experiment — ROI becomes repeatable and scalable.

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