Experienced leader in Data Engineering and AI implementation, focused on Accelerating Growth and Operational Performance in the U.S. solar utilities sector. Proven track record of helping utility companies unlock hidden operational value through predictive analytics, grid optimization, yield forecasting, and intelligent asset management. Passionate about enabling renewable energy adoption with data-driven decision systems and AI agent.

Managing Partner and Co-Founder at Dataforest end-to-end custom software development company with a focus on Data engineering and software development. Demonstrated history of working in the Travel, eCommerce/Retail, Fintech, Marketing, Pharma, Informational Technology, and Services industries. Skilled in managing complex and strategic projects in business and IT optimization; performing financial models development, and predictive analysis; performed numerous SME reporting and process optimization projects.

The main focus is on complex Ai adoption, data processing and system integration projects for SMEs.
Strong engineering professional with a Master's degree in Economic cybernetics.

With Dataforest I have helped dozens of companies to scale, and improve operational efficiency by creating and improving digital workflows and automating all business processes by applying AI & Data Science technologies along with expertise in ETL pipelines, API integration, and advanced software engineering.

Shortlisted services that we provide:

📌 Data Science
📌 Gen AI
📌 Data Engineering
📌 Web scraping 
📌 Web applications 
📌 DevOps 

 Feel free to drop me a line or book a 15 min call with me here https://calendly.com/dataforestai/letstalk

#webdevelopment #datascience #bigdata #dataengineering #GenAI

AI isn’t failing in 2026 because of bad models.
It’s failing because of bad sequencing.

Boards are no longer approving “innovation budgets.”
CFOs are demanding ROI clarity.
PE operators want EBITDA visibility — not pilot projects.

And yet most AI initiatives still begin with tooling decisions.
That’s backwards.

Only a small fraction of companies are extracting material value from AI at scale.

The divide isn’t technical capability.

It’s economic discipline.

In the attached piece, I break down the 4-step entry framework we use to de-risk AI investments:

• Start with the P&L line
• Audit data (not tools)
• Build a control layer
• Deploy AI where margin friction exists

AI should compound enterprise value.

Not increase stack complexity.

If you're a CEO, CFO, or CTO planning serious AI investment in 2026 — this is worth 5 minutes.

Read it below.

And if you'd like an external lens on your sequencing before committing capital, my team and I are open to selective advisory conversations: https://lnkd.in/eXNJ9gxJ

Aleksandr Sheremeta

Helping mid-sized companies and startups develop digital products and AI-powered systems that improve operations and drive revenue.

In 2026, AI projects don’t fail because of bad models. They fail because leadership starts in the wrong place. And in a year defined by capital discip

Open article: 
The Most Expensive AI Mistake in 2026 Isn’t Technical — It’s Financial by Aleksandr Sheremeta • 4 min read

The Most Expensive AI Mistake in 2026 Isn’t Technical — It’s Financial

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Aleksandr Sheremeta's Linkedin Analytics