How to Turn GenAI From a Cost Center Into a Profit Engine | Insights from Anna E. Molosky

Most organizations are investing aggressively in GenAI — yet few can point to material financial impact. According to Anna E. Molosky, this gap isn’t because GenAI underdelivers, but because enterprises often invest in the wrong order, solve the wrong problems, and expect returns on the wrong timeline.

Here’s a pragmatic blueprint for translating GenAI spending into measurable ROI.

1️⃣ Redirect Early GenAI Investment Toward Operational Efficiency

Executives frequently over-index on high-visibility use cases — customer engagement, sales, and marketing — believing these will deliver the fastest returns. But as Anna E. Molosky emphasizes, the earliest, clearest, and most defensible ROI consistently comes from operations, not customer-facing pilots.

Prioritize AI for:

• Repetitive back-office workflows

• Finance and HR process automation

• Data-heavy tasks ripe for structured AI or rules-based automation

• Systems that integrate into existing enterprise architecture

This shift often unlocks scalable efficiency. Just look at AT&T, which reclaimed 16.9 million labor minutes and generated 20x ROI through disciplined enterprise automation — long before flashy GenAI tools entered the picture.

Molosky’s core principle:
If the goal is ROI, start where inefficiency is measurable, repeatable, and costly.

2️⃣ Turn Employee AI Behaviors Into a Strategic Roadmap

Employees aren’t waiting for leadership to roll out AI — they’ve already adopted it:

• 90% use personal AI tools

• Only 40% receive employer-provided access

Anna E. Molosky views this not as a compliance risk, but as an untapped reservoir of insight. Employees have effectively run thousands of micro-tests, proving which tasks are most automatable.

To capitalize:

1. Capture how employees already use AI in the flow of work

2. Identify high-value, high-frequency tasks

3. Formalize these workflows in secure, enterprise-grade systems

This bottom-up signal gives leaders a data-backed way to prioritize AI investments that actually accelerate productivity.

Instead of guessing where AI creates value, let your workforce show you.

3️⃣ Reset Organizational Expectations About AI Deployment Timelines

One of the biggest barriers to AI ROI is unrealistic leadership expectations.
Molosky notes that many enterprises still believe AI should deliver ROI on a pilot-style timeline — 3 to 6 months.

But enterprise transformation doesn’t behave like a prototype.

Most large-scale deployments — AI or otherwise — take 1 to 3 years due to:

• Data readiness

• System integration

• Process redesign

• Security and governance

• Global change management

Short-term ROI studies capture only a fraction of the value curve.

Instead of interpreting the “95% of AI initiatives fail” narrative as a red flag, Molosky reframes it:

The 5% succeeding today represent what disciplined enterprises will achieve tomorrow.

Molosky’s Framework for Real AI ROI

To convert GenAI investment into sustained financial impact:

✔ Shift early spend toward operational automation

✔ Let employee behavior inform use-case prioritization

✔ Align expectations with enterprise deployment realities

When organizations stop treating AI as an experiment and start managing it as a strategic operating capability, GenAI becomes not just innovative — but profitable.