When my team first tested three “top-rated” AI summarizers, we expected efficiency — not chaos.

One skipped paragraphs. Another invented quotes with alarming confidence.

Only one delivered consistent results, and that wasn’t by chance.

The difference came down to process.

A structured, [data-driven analysis of AI tools](https://skywork.ai/skypage) turned random experiments into measurable insight.

Instead of arguing about which tool *felt* better, we tracked clear metrics: accuracy, speed, stability, and cost per task.

Within a week, patterns appeared — the flashiest demos often hid the most fragile systems.

These days, with hundreds of new AI products launching every month, guesswork simply doesn’t scale.

**According to Gartner, many enterprise AI projects struggle or fail due to weak evaluation practices and unclear metrics.**

If your team still chooses tools based on hype or instinct, you’re betting against the odds.

Only data can separate signal from noise.

![](https://cdn-images-1.medium.com/max/1600/1*NUIRg71UW6ocDnEFnnlT5w.png)

### Stop Guessing: What to Measure When Comparing AI Tools

Most teams test AI tools the lazy way — they watch one demo, poll a few colleagues, and declare a winner.

But first impressions are deceptive.

If you want consistent results, treat evaluation like a small-scale experiment, not a product review.

### Performance Accuracy

How close do the results come to what you expect?

Feed the same input to multiple tools and manually review the output.
You’ll be surprised how differently “good” tools behave when asked to do the same thing.

### Response Speed and Stability

Speed counts — until reliability breaks.

Measure average response time and track whether the tool crashes, stalls, or changes tone under load.

### Output Quality and Consistency

Metrics can’t judge nuance.

Look for factual accuracy, logical flow, and tone consistency.
If one output sounds brilliant but the next makes no sense, that inconsistency is a warning sign.

### Ease of Integration

Even the best model is useless if it doesn’t fit into your workflow.

Check that it works smoothly with your data stack, permission system, and automation setup.

### Cost Efficiency

Calculate total cost per finished task, not just per token or subscription.

A “cheap” plan often costs more once errors and retries pile up.

When you line up these five dimensions, patterns start to emerge.

Sometimes the “average” tool outperforms the industry darling simply because it’s predictable and steady.

That’s the real difference between testing a product and benchmarking a workflow.

But good metrics aren’t enough — you still need a system that filters what matters.

![](https://cdn-images-1.medium.com/max/1600/1*AV_u_wepe-NlYEHxRPUZsw.png)

### Apply a Curation Approach to Build a Data-Driven Test Plan

Just as the [curation approach of ChatGPT Pulse](https://skywork.ai/blog/chatgpt-pulse-best-practices-informationsueberflutung-2025/) sorts signal from overload, your evaluation plan should strip away noise and keep only actionable data.

You don’t need code or dashboards — just a clear process.

### Step 1 — Pick three real-world tasks

Summarize a report, draft an email, analyze a dataset — choose what your team actually does.

Practical beats theoretical.

### Step 2 — Define what “good” means

Decide success metrics in advance: accuracy rate, completion time, or a 1-to-5 quality score.

It prevents bias later.

### Step 3 — Run identical prompts across tools

Keep inputs fixed so the tool is the only variable.

Run each test three times to catch variance.

### Step 4 — Record the results

Use a spreadsheet, Notion table, or — if supported — your workspace in Skywork Skypage to track data. 

According to Skywork’s documentation, its platform supports document and sheet-style outputs that can be repurposed for evaluations.

Columns for speed, accuracy, and notes are enough to spot trends fast.

### Step 5 — Compare, visualize, decide

Plot a quick radar or bar chart.

Don’t hunt perfection — look for balance.

Stability beats flash; repeatability beats luck.

The magic isn’t in fancy analytics.

It’s in the discipline of testing, recording, and deciding with evidence.

### Why Data Alone Isn’t Enough — What ChatGPT Pulse Gets Right

Raw data tells you what happened.

Curation tells you why it matters.

That’s what makes ChatGPT Pulse valuable — it doesn’t chase every update; it organizes insights so readers see patterns, not clutter.

Apply that mindset to your evaluations.

More data rarely means better data.

Here’s what Pulse gets right — and what’s worth copying:

### Focus beats volume

Pulse curates selectively, ranking updates by relevance.

Do the same: ask whether each metric truly helps you decide.
If not, drop it.

### Context gives numbers meaning

A score of 0.8 means little without knowing the task or cost.

Add notes, not just numbers.

### Human judgment closes the loop

Pulse mixes automation with editorial sense — you should too.

Metrics reveal patterns, but people interpret trade-offs: what’s good enough, what scales, what fits your tone.

Curation is structured intuition.

It’s how raw data turns into confident decisions.

### A 20-Minute DIY Benchmark — Try It Yourself

You don’t need a lab or an engineer to benchmark AI tools.

Twenty minutes, a real task, and fairness are enough.

![](https://cdn-images-1.medium.com/max/1600/1*V00Lhfb4KrPKkWbv-vtlxw.png)

![](https://cdn-images-1.medium.com/max/1600/1*S6mPFbQjYen3PZ8TPQz9jw.png)

The best tool isn’t the fastest or the cheapest — it’s the one that stays reliable under pressure.

Ask yourself:

* Which metric matters most for this task?
* Does the performance hold over time?
* Is the cost proportional to value?

Run this once or twice, and you’ll never rely on gut feeling again.

### From Data to Decision — Lessons from Pulse and Practice

Data doesn’t make decisions; people do.

But when information is organized and contextualized, decisions get better.

Both AI benchmarking and ChatGPT Pulse share the same truth:

* Focus on meaningful signals. Track only the metrics that truly matter.
* Let patterns guide, not dictate. Use data as a compass, not a verdict.
* Keep humans in the loop. Numbers inform, but judgment decides.

A mature evaluation process isn’t a project — it’s a habit.

Teams that measure, compare, and refine continuously outperform those that rely on instinct.

That’s the quiet advantage of data-driven curation: learning not to collect more data, but to interpret it better.

### Closing Thoughts — Building an Evaluation Culture

Being data-driven isn’t about spreadsheets.

It’s about trust — in your process, in your evidence, in your decisions.

Every organization faces the same paradox: too many tools, too little clarity.

The real edge lies in learning faster than the noise.

That starts by creating an evaluation rhythm — testing, reviewing, and improving until it becomes second nature.

Start small: one task, one metric, one sheet.

Run a test, note the outcome, discuss what surprised you, repeat next week.

Soon you’ll have your own internal “AI Pulse” — a living record of what works, what doesn’t, and why.

From our perspective at Skywork, the difference between chaos and clarity isn’t luck — it’s process.

A repeatable, transparent process that turns experimentation into confidence.

The same mindset that powers ChatGPT Pulse can guide your team too: filter, measure, and decide with intention.

So start small — but start.

Because the smartest AI strategy isn’t built on more tools; it’s built on better judgment.

When my team first tested three “top-rated” AI summarizers, we expected efficiency — not chaos.
One skipped paragraphs. Another invented quotes with alarming confidence.
Only one delivered consistent results, and that wasn’t by chance.
The difference came down to process.
A structured, <a href="https://skywork.ai/skypage">data-driven analysis of AI tools</a> turned random experiments into measurable insight.
Instead of arguing about which tool felt better, we tracked clear metrics: accuracy, speed, stability, and cost per task.
Within a week, patterns appeared — the flashiest demos often hid the most fragile systems.
These days, with hundreds of new AI products launching every month, guesswork simply doesn’t scale.
According to Gartner, many enterprise AI projects struggle or fail due to weak evaluation practices and unclear metrics.
If your team still chooses tools based on hype or instinct, you’re betting against the odds.
Only data can separate signal from noise.
<img src="https://cdn-images-1.medium.com/max/1600/1*NUIRg71UW6ocDnEFnnlT5w.png" alt="">
<h3 id="stop-guessing-what-to-measure-when-comparing-aitools">Stop Guessing: What to Measure When Comparing AI Tools</h3>
Most teams test AI tools the lazy way — they watch one demo, poll a few colleagues, and declare a winner.
But first impressions are deceptive.
If you want consistent results, treat evaluation like a small-scale experiment, not a product review.
<h3 id="performance-accuracy">Performance Accuracy</h3>
How close do the results come to what you expect?
Feed the same input to multiple tools and manually review the output.
You’ll be surprised how differently “good” tools behave when asked to do the same thing.
<h3 id="response-speed-and-stability">Response Speed and Stability</h3>
Speed counts — until reliability breaks.
Measure average response time and track whether the tool crashes, stalls, or changes tone under load.
<h3 id="output-quality-and-consistency">Output Quality and Consistency</h3>
Metrics can’t judge nuance.
Look for factual accuracy, logical flow, and tone consistency.
If one output sounds brilliant but the next makes no sense, that inconsistency is a warning sign.
<h3 id="ease-of-integration">Ease of Integration</h3>
Even the best model is useless if it doesn’t fit into your workflow.
Check that it works smoothly with your data stack, permission system, and automation setup.
<h3 id="cost-efficiency">Cost Efficiency</h3>
Calculate total cost per finished task, not just per token or subscription.
A “cheap” plan often costs more once errors and retries pile up.
When you line up these five dimensions, patterns start to emerge.
Sometimes the “average” tool outperforms the industry darling simply because it’s predictable and steady.
That’s the real difference between testing a product and benchmarking a workflow.
But good metrics aren’t enough — you still need a system that filters what matters.
<img src="https://cdn-images-1.medium.com/max/1600/1*AV_u_wepe-NlYEHxRPUZsw.png" alt="">
<h3 id="apply-a-curation-approach-to-build-a-data-driven-testplan">Apply a Curation Approach to Build a Data-Driven Test Plan</h3>
Just as the <a href="https://skywork.ai/blog/chatgpt-pulse-best-practices-informationsueberflutung-2025/">curation approach of ChatGPT Pulse</a> sorts signal from overload, your evaluation plan should strip away noise and keep only actionable data.
You don’t need code or dashboards — just a clear process.
<h3 id="step-1pick-three-real-world-tasks">Step 1 — Pick three real-world tasks</h3>
Summarize a report, draft an email, analyze a dataset — choose what your team actually does.
Practical beats theoretical.
<h3 id="step-2define-what-goodmeans">Step 2 — Define what “good” means</h3>
Decide success metrics in advance: accuracy rate, completion time, or a 1-to-5 quality score.
It prevents bias later.
<h3 id="step-3run-identical-prompts-acrosstools">Step 3 — Run identical prompts across tools</h3>
Keep inputs fixed so the tool is the only variable.
Run each test three times to catch variance.
<h3 id="step-4record-theresults">Step 4 — Record the results</h3>
Use a spreadsheet, Notion table, or — if supported — your workspace in Skywork Skypage to track data. 
According to Skywork’s documentation, its platform supports document and sheet-style outputs that can be repurposed for evaluations.
Columns for speed, accuracy, and notes are enough to spot trends fast.
<h3 id="step-5compare-visualize-decide">Step 5 — Compare, visualize, decide</h3>
Plot a quick radar or bar chart.
Don’t hunt perfection — look for balance.
Stability beats flash; repeatability beats luck.
The magic isn’t in fancy analytics.
It’s in the discipline of testing, recording, and deciding with evidence.
<h3 id="why-data-alone-isnt-enoughwhat-chatgpt-pulse-getsright">Why Data Alone Isn’t Enough — What ChatGPT Pulse Gets Right</h3>
Raw data tells you what happened.
Curation tells you why it matters.
That’s what makes ChatGPT Pulse valuable — it doesn’t chase every update; it organizes insights so readers see patterns, not clutter.
Apply that mindset to your evaluations.
More data rarely means better data.
Here’s what Pulse gets right — and what’s worth copying:
<h3 id="focus-beatsvolume">Focus beats volume</h3>
Pulse curates selectively, ranking updates by relevance.
Do the same: ask whether each metric truly helps you decide.
If not, drop it.
<h3 id="context-gives-numbersmeaning">Context gives numbers meaning</h3>
A score of 0.8 means little without knowing the task or cost.
Add notes, not just numbers.
<h3 id="human-judgment-closes-theloop">Human judgment closes the loop</h3>
Pulse mixes automation with editorial sense — you should too.
Metrics reveal patterns, but people interpret trade-offs: what’s good enough, what scales, what fits your tone.
Curation is structured intuition.
It’s how raw data turns into confident decisions.
<h3 id="a-20-minute-diy-benchmarktry-ityourself">A 20-Minute DIY Benchmark — Try It Yourself</h3>
You don’t need a lab or an engineer to benchmark AI tools.
Twenty minutes, a real task, and fairness are enough.
<img src="https://cdn-images-1.medium.com/max/1600/1*V00Lhfb4KrPKkWbv-vtlxw.png" alt="">
<img src="https://cdn-images-1.medium.com/max/1600/1*S6mPFbQjYen3PZ8TPQz9jw.png" alt="">
The best tool isn’t the fastest or the cheapest — it’s the one that stays reliable under pressure.
Ask yourself:
<ul>
<li>Which metric matters most for this task?</li>
<li>Does the performance hold over time?</li>
<li>Is the cost proportional to value?</li>
</ul>
Run this once or twice, and you’ll never rely on gut feeling again.
<h3 id="from-data-to-decisionlessons-from-pulse-andpractice">From Data to Decision — Lessons from Pulse and Practice</h3>
Data doesn’t make decisions; people do.
But when information is organized and contextualized, decisions get better.
Both AI benchmarking and ChatGPT Pulse share the same truth:
<ul>
<li>Focus on meaningful signals. Track only the metrics that truly matter.</li>
<li>Let patterns guide, not dictate. Use data as a compass, not a verdict.</li>
<li>Keep humans in the loop. Numbers inform, but judgment decides.</li>
</ul>
A mature evaluation process isn’t a project — it’s a habit.
Teams that measure, compare, and refine continuously outperform those that rely on instinct.
That’s the quiet advantage of data-driven curation: learning not to collect more data, but to interpret it better.
<h3 id="closing-thoughtsbuilding-an-evaluation-culture">Closing Thoughts — Building an Evaluation Culture</h3>
Being data-driven isn’t about spreadsheets.
It’s about trust — in your process, in your evidence, in your decisions.
Every organization faces the same paradox: too many tools, too little clarity.
The real edge lies in learning faster than the noise.
That starts by creating an evaluation rhythm — testing, reviewing, and improving until it becomes second nature.
Start small: one task, one metric, one sheet.
Run a test, note the outcome, discuss what surprised you, repeat next week.
Soon you’ll have your own internal “AI Pulse” — a living record of what works, what doesn’t, and why.
From our perspective at Skywork, the difference between chaos and clarity isn’t luck — it’s process.
A repeatable, transparent process that turns experimentation into confidence.
The same mindset that powers ChatGPT Pulse can guide your team too: filter, measure, and decide with intention.
So start small — but start.
Because the smartest AI strategy isn’t built on more tools; it’s built on better judgment.

Data-Driven Analysis of AI Tools: Insights from ChatGPT Pulse

![](https://cdn-images-1.medium.com/max/1600/1*lGjjWu0xCQbtQG7MH4SsNg.png)

(Image: Oleh Shkinder | [LinkedIn](https://www.linkedin.com/in/oleh-shkinder/))

Startups rarely begin with certainty. They start with curiosity, risk, and often, no playbook.

For [Oleh Shkinder](http://linkedin.com/in/oleh-shkinder-38228315b), that beginning came during a turbulent time, building a fintech platform from Ukraine as war loomed, and resources were stretched thin. What started as a side project eventually evolved into *Investables*, a platform for fractional ownership of physical collectibles, and ultimately became a startup acquired by U.S.-based fintech company *Stockpile*.

Today, Oleh serves as the Senior Product Manager at Stockpile, where he leads product development efforts for cryptotrading, membership billing, and platform features, impacting over 1 million families across the United States. His story is one of resourcefulness, resilience, and a sharp eye for scalable, user-centric product design.

### From Ukraine to 0–1: Building Investables

In 2022, amid political uncertainty and personal risk, Oleh co-founded Investables, an investing platform giving users access to physical assets through fractional ownership. Initially launched as a retail platform, it quickly evolved into a B2B API, enabling partners to embed alternative investing into their own apps.

Working remotely under crisis, Oleh juggled multiple roles: product strategist, architect, vendor integrator, and investor liaison. The product’s value crystallized after several pivots. Oleh evolved it into a B2B infrastructure solution that enabled fintechs to introduce a new asset class to their end users seamlessly. Early pilot integrations verified the demand, leading to multiple active contract negotiations with established brokerages.

The startup’s growth caught the attention of Stockpile, a Series B fintech serving U.S. families. In 2023, Stockpile acquired Investables, a defining moment that validated not just the product but Oleh’s ability to lead under pressure.

### Making an Enterprise Impact at Stockpile

After the acquisition, Oleh took the lead on key product areas at Stockpile, directly impacting user growth, engagement, and revenue.

In his first year, he delivered trading, crypto, and portfolio capabilities that boosted retention by over 23% and increased user satisfaction by 30%. Through disciplined, data-driven experimentation aligned with the company’s North Star Metric, he helped drive sustained increases in MAU and contributed materially to growing AUM.

He also introduced wallet-based billing and optimized the membership lifecycle, leading to a 23% increase in recurring revenue and stronger conversion rates across user cohorts.

By aligning engineering, design, and data teams, he consistently shipped on time through clear roadmaps and OKRs aligned with the company’s strategy.

### Strength in Structure: The Power of Process

Despite his creative energy, Oleh’s approach to product management is rooted in discipline and clarity.

*“Discipline and structure,” he says, “are the real foundations of creativity and progress.”*

This philosophy sharpened during his time at Investables, where every day required fast decisions, constant adaptation, and focused execution under uncertainty.

This mindset now influences his approach at Stockpile, guided by three key principles. First, he maintains a strong user focus, beginning with customer problems and making decisions based on data and real behavior. Second, he clarifies ambiguity by breaking down complex issues and aligning teams around the most impactful goals. Third, he influences without formal authority, communicating effectively and building trust to ensure that teams work together in the same direction.

His work reflects a structured, fast-moving approach to building products with depth and intention.

### Guiding the Next Generation at Beta University

Since mid-2025, Oleh has mentored early-stage founders at Beta University, a Palo Alto pre-acceleration program that focuses on validating ideas and preparing for fundraising.

![](https://cdn-images-1.medium.com/max/1600/1*7CKRv84kBr9WK98BiqK2RA.png)

(Image: [Freepik](https://www.freepik.com/free-photo/man-working-with-infographics-indoors_11938182.htm#fromView=search\&page=1\&position=32\&uuid=121a9489-67c0-4a12-aeb9-6bffe8df157a\&query=+investor-ready+metrics))

His hands-on, data-driven approach helps teams define go-to-market strategies, spot product-market fit, and structure investor-ready metrics, grounded not in theory, but in his own journey from concept to acquisition.

### Earlier Roles: Pattern Recognition Through Experience

Before founding Investables, Oleh built a deep foundation across fintech, SaaS, crypto, and AI.

At Changeblock, he worked as a product consultant, shipping an MVP in close collaboration with the Cardano Foundation to help tokenize carbon credits globally.

At Heureka, he led a logistics SaaS (Software as a Service) used by enterprise clients, including Siemens and DB Schenker. His focus was on AI-driven optimizations that significantly reduced operational inefficiencies. He recognized that to move faster, the organization needed a new operating model, which led him to drive an agile transformation across various teams.

Subsequently, he held roles at SoftServe, where he contributed to large-scale digital transformation projects, and at Magnise, where he helped fintech clients secure significant early funding by collaborating closely with engineering to deliver high-quality products.

Each of these roles enhanced his systems thinking, data intuition, and user empathy, foundations that continue to define his approach to product leadership today.

### AI as a Strategic Advantage

Today, Oleh is leading the way in integrating AI into product development, not simply as a tool for automation, but as a creative and technical partner. He utilizes platforms like Lovable, Supabase, ChatGPT, and custom agent workflows to rapidly validate ideas, prototype comprehensive experiences, and explore new product directions. For him, AI isn’t just a shortcut; it’s a means to work more efficiently, discover new possibilities, and communicate solutions with much greater clarity.

![](https://cdn-images-1.medium.com/max/1600/1*N0YWdDjXpgsN0zRw9WKfVQ.png)

(Image: AI and human touch | [Freepik](https://www.freepik.com/free-photo/technology-human-touch-background-modern-remake-creation-adam_17851045.htm#fromView=search\&page=1\&position=4\&uuid=aef23def-dc74-4075-b91a-9413ca140898\&query=AI))

For him, AI is not about replacing product intuition; it’s about amplifying it. He envisions a future where product managers operate more like storytellers and strategists, leveraging AI to automate mundane tasks and allocate more time to user insights, vision, and experimentation.

This is part of his broader aspiration: to modernize product management into a craft that blends technology, human understanding, and business impact.

### Build What Matters

Oleh’s personal north star isn’t velocity, it’s clarity.

*“The goal,” he says, “is not to build fast, but to build what truly matters.”*

That focus shows in every phase of his journey, from founder to product leader.

Living in the Bay Area has only reinforced that conviction. Surrounded by builders who think long-term and lead with depth, he’s drawn inspiration not just for work, but for life. As he says, the best builders don’t chase hype; they ship what matters.

One quote that grounds his mindset comes from Andrew Horowitz:

*“The goal isn’t to predict the future. It’s to prepare for it with clarity and discipline.”*

That lens, courage over cleverness, has shaped how he builds, leads, and mentors.

### Looking Ahead

Oleh’s next chapter is already in motion. He’s doubling down on the intersection of AI and product strategy, working to simplify complex systems into user-friendly tools, and rethinking how teams ideate, validate, and scale.

But even as the tech evolves, his foundation holds: build with purpose, lead with clarity, and stay close to the user.

From a founder under fire to a leader shaping fintech for millions, Oleh Shkinder’s story reminds us: the best products start with vision, and survive on courage.

***About the Author***

*Jake Bennett is a tech writer and startup strategist focused on product-led growth and founder storytelling. With a background in UX research and SaaS marketing, J. brings clarity and depth to complex product narratives. Outside of work, you’ll find him experimenting with sourdough recipes on slow weekends.*

<img src="https://cdn-images-1.medium.com/max/1600/1*lGjjWu0xCQbtQG7MH4SsNg.png" alt="">
(Image: Oleh Shkinder | <a href="https://www.linkedin.com/in/oleh-shkinder/">LinkedIn</a>)
Startups rarely begin with certainty. They start with curiosity, risk, and often, no playbook.
For <a href="http://linkedin.com/in/oleh-shkinder-38228315b">Oleh Shkinder</a>, that beginning came during a turbulent time, building a fintech platform from Ukraine as war loomed, and resources were stretched thin. What started as a side project eventually evolved into Investables, a platform for fractional ownership of physical collectibles, and ultimately became a startup acquired by U.S.-based fintech company Stockpile.
Today, Oleh serves as the Senior Product Manager at Stockpile, where he leads product development efforts for cryptotrading, membership billing, and platform features, impacting over 1 million families across the United States. His story is one of resourcefulness, resilience, and a sharp eye for scalable, user-centric product design.
<h3 id="from-ukraine-to-01-building-investables">From Ukraine to 0–1: Building Investables</h3>
In 2022, amid political uncertainty and personal risk, Oleh co-founded Investables, an investing platform giving users access to physical assets through fractional ownership. Initially launched as a retail platform, it quickly evolved into a B2B API, enabling partners to embed alternative investing into their own apps.
Working remotely under crisis, Oleh juggled multiple roles: product strategist, architect, vendor integrator, and investor liaison. The product’s value crystallized after several pivots. Oleh evolved it into a B2B infrastructure solution that enabled fintechs to introduce a new asset class to their end users seamlessly. Early pilot integrations verified the demand, leading to multiple active contract negotiations with established brokerages.
The startup’s growth caught the attention of Stockpile, a Series B fintech serving U.S. families. In 2023, Stockpile acquired Investables, a defining moment that validated not just the product but Oleh’s ability to lead under pressure.
<h3 id="making-an-enterprise-impact-at-stockpile">Making an Enterprise Impact at Stockpile</h3>
After the acquisition, Oleh took the lead on key product areas at Stockpile, directly impacting user growth, engagement, and revenue.
In his first year, he delivered trading, crypto, and portfolio capabilities that boosted retention by over 23% and increased user satisfaction by 30%. Through disciplined, data-driven experimentation aligned with the company’s North Star Metric, he helped drive sustained increases in MAU and contributed materially to growing AUM.
He also introduced wallet-based billing and optimized the membership lifecycle, leading to a 23% increase in recurring revenue and stronger conversion rates across user cohorts.
By aligning engineering, design, and data teams, he consistently shipped on time through clear roadmaps and OKRs aligned with the company’s strategy.
<h3 id="strength-in-structure-the-power-ofprocess">Strength in Structure: The Power of Process</h3>
Despite his creative energy, Oleh’s approach to product management is rooted in discipline and clarity.
“Discipline and structure,” he says, “are the real foundations of creativity and progress.”
This philosophy sharpened during his time at Investables, where every day required fast decisions, constant adaptation, and focused execution under uncertainty.
This mindset now influences his approach at Stockpile, guided by three key principles. First, he maintains a strong user focus, beginning with customer problems and making decisions based on data and real behavior. Second, he clarifies ambiguity by breaking down complex issues and aligning teams around the most impactful goals. Third, he influences without formal authority, communicating effectively and building trust to ensure that teams work together in the same direction.
His work reflects a structured, fast-moving approach to building products with depth and intention.
<h3 id="guiding-the-next-generation-at-beta-university">Guiding the Next Generation at Beta University</h3>
Since mid-2025, Oleh has mentored early-stage founders at Beta University, a Palo Alto pre-acceleration program that focuses on validating ideas and preparing for fundraising.
<img src="https://cdn-images-1.medium.com/max/1600/1*7CKRv84kBr9WK98BiqK2RA.png" alt="">
(Image: <a href="https://www.freepik.com/free-photo/man-working-with-infographics-indoors_11938182.htm#fromView=search&#x26;page=1&#x26;position=32&#x26;uuid=121a9489-67c0-4a12-aeb9-6bffe8df157a&#x26;query=+investor-ready+metrics">Freepik</a>)
His hands-on, data-driven approach helps teams define go-to-market strategies, spot product-market fit, and structure investor-ready metrics, grounded not in theory, but in his own journey from concept to acquisition.
<h3 id="earlier-roles-pattern-recognition-through-experience">Earlier Roles: Pattern Recognition Through Experience</h3>
Before founding Investables, Oleh built a deep foundation across fintech, SaaS, crypto, and AI.
At Changeblock, he worked as a product consultant, shipping an MVP in close collaboration with the Cardano Foundation to help tokenize carbon credits globally.
At Heureka, he led a logistics SaaS (Software as a Service) used by enterprise clients, including Siemens and DB Schenker. His focus was on AI-driven optimizations that significantly reduced operational inefficiencies. He recognized that to move faster, the organization needed a new operating model, which led him to drive an agile transformation across various teams.
Subsequently, he held roles at SoftServe, where he contributed to large-scale digital transformation projects, and at Magnise, where he helped fintech clients secure significant early funding by collaborating closely with engineering to deliver high-quality products.
Each of these roles enhanced his systems thinking, data intuition, and user empathy, foundations that continue to define his approach to product leadership today.
<h3 id="ai-as-a-strategic-advantage">AI as a Strategic Advantage</h3>
Today, Oleh is leading the way in integrating AI into product development, not simply as a tool for automation, but as a creative and technical partner. He utilizes platforms like Lovable, Supabase, ChatGPT, and custom agent workflows to rapidly validate ideas, prototype comprehensive experiences, and explore new product directions. For him, AI isn’t just a shortcut; it’s a means to work more efficiently, discover new possibilities, and communicate solutions with much greater clarity.
<img src="https://cdn-images-1.medium.com/max/1600/1*N0YWdDjXpgsN0zRw9WKfVQ.png" alt="">
(Image: AI and human touch | <a href="https://www.freepik.com/free-photo/technology-human-touch-background-modern-remake-creation-adam_17851045.htm#fromView=search&#x26;page=1&#x26;position=4&#x26;uuid=aef23def-dc74-4075-b91a-9413ca140898&#x26;query=AI">Freepik</a>)
For him, AI is not about replacing product intuition; it’s about amplifying it. He envisions a future where product managers operate more like storytellers and strategists, leveraging AI to automate mundane tasks and allocate more time to user insights, vision, and experimentation.
This is part of his broader aspiration: to modernize product management into a craft that blends technology, human understanding, and business impact.
<h3 id="build-whatmatters">Build What Matters</h3>
Oleh’s personal north star isn’t velocity, it’s clarity.
“The goal,” he says, “is not to build fast, but to build what truly matters.”
That focus shows in every phase of his journey, from founder to product leader.
Living in the Bay Area has only reinforced that conviction. Surrounded by builders who think long-term and lead with depth, he’s drawn inspiration not just for work, but for life. As he says, the best builders don’t chase hype; they ship what matters.
One quote that grounds his mindset comes from Andrew Horowitz:
“The goal isn’t to predict the future. It’s to prepare for it with clarity and discipline.”
That lens, courage over cleverness, has shaped how he builds, leads, and mentors.
<h3 id="looking-ahead">Looking Ahead</h3>
Oleh’s next chapter is already in motion. He’s doubling down on the intersection of AI and product strategy, working to simplify complex systems into user-friendly tools, and rethinking how teams ideate, validate, and scale.
But even as the tech evolves, his foundation holds: build with purpose, lead with clarity, and stay close to the user.
From a founder under fire to a leader shaping fintech for millions, Oleh Shkinder’s story reminds us: the best products start with vision, and survive on courage.
About the Author
Jake Bennett is a tech writer and startup strategist focused on product-led growth and founder storytelling. With a background in UX research and SaaS marketing, J. brings clarity and depth to complex product narratives. Outside of work, you’ll find him experimenting with sourdough recipes on slow weekends.

How Oleh Shkinder Scaled a Side Project Into an Acquired Fintech Powerhouse Impacting 1M+ Users

![](https://cdn-images-1.medium.com/max/1600/0*zmdmEhU288au9mft.jpg)

There was a time when sports betting only occurred in sports arenas and on playing fields. However, nowadays, most bets are placed through web or mobile applications. Thanks to smartphones and the widespread availability of the internet, we can now place bets, regardless of where we are or what we're doing at the moment. What can you do to make your app the most popular and functional? Consider incorporating the best features for sports betting applications.

## Sports betting applications – what are they?

Sports betting applications, also known as **sportsbook or betting apps**, are mobile or web-based applications that allow users to place bets on various sports events and outcomes.

These applications provide a platform for users to wager money on sports games, matches, or races. They offer a wide range of betting options, such as **predicting the winner, point spreads, over/under totals, and more**. You can use a [sports betting software development company](https://softwaremind.com/services/software-for-sport-betting-industry/) to improve user experience and increase your app's effectiveness.

## Crucial features of sports betting apps for users

**The crucial aspect is the intuitiveness of your app.** Navigation should be fast and easy. The speed of the application is also significant. Only a well-functioning application will provide users with the comfort of placing bets, which is particularly important for live betting. Additionally, **some functionalities can make your application even more attractive to users**

### Quick Login

Users like to log in quickly, which is crucial in the case of live events. A nice feature of apps is the **ability to log in with an email or Facebook account**. The method of logging into the mobile app should also be as fast as possible. A good way to achieve this is by **logging in with the user's fingerprint.**

### Linking a payment card to a user account

**Connecting a credit or debit card to a user account speeds up payments.** This way, users don't have to waste time confirming payments or logging into their bank accounts to transfer funds needed to place a bet. It's quick and convenient.

### Notifications

A seemingly obvious but more significant aspect is what should be covered by notifications. Indeed, users should receive **notifications about the final results of the game**, but they can also be informed about **changes in scores**. Additionally, the app can send notifications about **ongoing events similar to those in which the user participated or those in which they are interested in taking part.**

### Transmissions

**The most thrilling aspect of placing bets is waiting for the win and tracking results.** The best sports betting apps allow users to watch matches or competitions, which is why your software should enable that feature. With **live streaming**, users can experience the excitement of sports, even if they are just commuting back home from work by bus or train. **Live streams provide users with the best experience throughout the entire betting process.**

### Tools helpful in placing bets

Many tools can be helpful in placing bets. **Statistics, results from previous competitions, or comparisons can be helpful in making accurate decisions**. Your sports betting software should allow users to utilize them by sharing it in your app.

### Live chats

Chats can be useful for two reasons. The first reason is to **contact customer service if the user encounters any problems.** Thanks to this, issues can be resolved promptly, allowing users to participate in betting without major disruptions. Another use of the chat is to **communicate with other players to discuss game results and bets.**

Many interesting features engage users of betting apps. Choose the best software functionalities for sports betting companies and implement them in your business.

<img src="https://cdn-images-1.medium.com/max/1600/0*zmdmEhU288au9mft.jpg" alt="">
There was a time when sports betting only occurred in sports arenas and on playing fields. However, nowadays, most bets are placed through web or mobile applications. Thanks to smartphones and the widespread availability of the internet, we can now place bets, regardless of where we are or what we're doing at the moment. What can you do to make your app the most popular and functional? Consider incorporating the best features for sports betting applications.
<h2 id="sports-betting-applications--what-are-they">Sports betting applications – what are they?</h2>
Sports betting applications, also known as sportsbook or betting apps, are mobile or web-based applications that allow users to place bets on various sports events and outcomes.
These applications provide a platform for users to wager money on sports games, matches, or races. They offer a wide range of betting options, such as predicting the winner, point spreads, over/under totals, and more. You can use a <a href="https://softwaremind.com/services/software-for-sport-betting-industry/">sports betting software development company</a> to improve user experience and increase your app's effectiveness.
<h2 id="crucial-features-of-sports-betting-apps-for-users">Crucial features of sports betting apps for users</h2>
The crucial aspect is the intuitiveness of your app. Navigation should be fast and easy. The speed of the application is also significant. Only a well-functioning application will provide users with the comfort of placing bets, which is particularly important for live betting. Additionally, some functionalities can make your application even more attractive to users
<h3 id="quick-login">Quick Login</h3>
Users like to log in quickly, which is crucial in the case of live events. A nice feature of apps is the ability to log in with an email or Facebook account. The method of logging into the mobile app should also be as fast as possible. A good way to achieve this is by logging in with the user's fingerprint.
<h3 id="linking-a-payment-card-to-a-user-account">Linking a payment card to a user account</h3>
Connecting a credit or debit card to a user account speeds up payments. This way, users don't have to waste time confirming payments or logging into their bank accounts to transfer funds needed to place a bet. It's quick and convenient.
<h3 id="notifications">Notifications</h3>
A seemingly obvious but more significant aspect is what should be covered by notifications. Indeed, users should receive notifications about the final results of the game, but they can also be informed about changes in scores. Additionally, the app can send notifications about ongoing events similar to those in which the user participated or those in which they are interested in taking part.
<h3 id="transmissions">Transmissions</h3>
The most thrilling aspect of placing bets is waiting for the win and tracking results. The best sports betting apps allow users to watch matches or competitions, which is why your software should enable that feature. With live streaming, users can experience the excitement of sports, even if they are just commuting back home from work by bus or train. Live streams provide users with the best experience throughout the entire betting process.
<h3 id="tools-helpful-in-placing-bets">Tools helpful in placing bets</h3>
Many tools can be helpful in placing bets. Statistics, results from previous competitions, or comparisons can be helpful in making accurate decisions. Your sports betting software should allow users to utilize them by sharing it in your app.
<h3 id="live-chats">Live chats</h3>
Chats can be useful for two reasons. The first reason is to contact customer service if the user encounters any problems. Thanks to this, issues can be resolved promptly, allowing users to participate in betting without major disruptions. Another use of the chat is to communicate with other players to discuss game results and bets.
Many interesting features engage users of betting apps. Choose the best software functionalities for sports betting companies and implement them in your business.

Software functionalities for sports betting companies – enhance your customer experience

Finding a reliable [AI video generator](https://www.capcut.com/tools/ai-video-generator) that actually saves time feels harder than it should. The majority of tools that I have tried claim to deliver instant videos but produce videos that are difficult to watch, slow, or contain heavy watermarks. That is time-wasting and it takes away the creative momentum of small teams or individual creators like myself. I have tried almost all the options that are available in the market, and the following are some of the most thought out solutions that really work. This list eliminates the hype and narrows down to tools that produce useful outputs in a short period of time. Every pick outlines the presence of actual free-tier functionality, convenient workflows, and actual export options. The guide is perfect when you need quick social videos, voice-over explainers, or short videos without having to spend hours editing ads. My recommendations are pegged on personal experience, direct comparison, and the urge to utilize the tools that would indeed accelerate the process of video production.![](https://cdn-images-1.medium.com/max/1600/1*_vqFiRDONEmv77Z0KmFSJw.png)

By 2025, I have been able to see how AI-powered fast video tools have revolutionized how creators produce and disseminate content. Marketing specialists, independent YouTubers, and every type of people are interested in faster, more mechanism-based workflows that will not potentially compromise the quality. The most significant challenge that I had to resolve is to find free platforms that deliver professional results but are not too complicated. Each of the tools listed below is tried personally regarding its availability, video quality and creative freedom. They are the surest choices in the mind of any individual who wants to take his/her content strategy to the next level through the employment of intelligent time-saving automation.



### Best Free Online AI Video Generators in 2025

1. **CapCut —** Best for [AI avatar](https://www.capcut.com/tools/ai-avatar)-based videos with one-click editing and fast, engaging short-form content
2. **FlexClip —** Best for quick text-to-video editing
3. **HeyGen —** Best for realistic talking video generation
4. **Lumen5 —** Best for turning articles into videos
5. **InVideo —** Best for marketing and social content creation
6. **Simplified —** Best for team-based video collaboration
7. **Animoto —** Best for beginners creating slideshow-style videos

![](https://cdn-images-1.medium.com/max/1600/1*DpBEcEq5tBIiDmOqY3F-Ug.png)

#### **1. CapCut**

CapCut stands out as one of the most versatile and user-friendly AI video generator platforms in 2025. It enables every person to create full video instantly through AI automation. The platform provides AI avatars, AI templates, and AI-powered automation that transform text prompts into professional-grade content. The Instant AI Video tool of CapCut does it all, including the pacing of the scenes and the background image, making the videos look refined within just a few seconds. It’s also the ideal [YouTube Shorts maker](https://www.capcut.com/create/youtube-shorts-maker), letting creators produce social-ready content quickly and efficiently.

**Main Functional Attributes:**

* **AI Avatars:** Offers over 100 digital avatars for instant video generation. Includes custom avatar cloning to create a personalized on-screen representation that matches specific branding or identity needs.
* **AI Workflow Templates:** Comes with 30+ ready-made templates for news, education, and marketing videos. Each template includes pre-structured scenes to maintain professional pacing, tone, and flow throughout the video.
* **Instant AI Video:** Video generation on scripts with one click. Handles the editing process, visual integration and timing changes automatically and without manual video assembly.
* **AI Brainstorming Tool:** A creative assistant that proposes ideas, storyboards, and individual directions. Developed to address creative blocks and boost ideation in the marketing, education, or content development process.
* **Unlimited Media Assets:** Allows unlimited access to a large collection of stock footage, music, fonts and graphics. Ideal to tailor videos and create content that is engaging and will optimize on social media.

![](https://cdn-images-1.medium.com/max/1600/1*d5O_7w5nU_iDdRvCpeZLRw.png)

**Advantages:**

* Simple interface suitable for all skill levels
* Produces professional-quality videos in minutes
* Large selection of avatars and templates
* Strong AI automation saves time and effort
* Free to use with powerful AI-driven workflow

**Disadvantages:**

* Some features require pro account to access

#### **2. FlexClip**

FlexClip is a fast and effective online converter of turning scripts into videos. It simplifies text-to-video using AI-based templates and auto-captioning. The users can input the text quickly, choose the visuals and leave the system to develop the fluent transitions and layouts. It is most effective on social media content, tutorials and explainer videos.

![](https://cdn-images-1.medium.com/max/1600/1*ZI3CnFFnNnDVBLQxkZfTsA.png)

**Advantages:**

* Auto-subtitles improve accessibility
* Easy drag-and-drop timeline
* Offers hundreds of stock clips and music tracks
* Ideal for beginners needing fast results

**Disadvantages:**

* Limited advanced editing features
* Free plan includes watermark on exports

#### **3. HeyGen**

HeyGen does a superb job at creating a realistic talking-head video on the basis of a text. Its AI renders facial expressions, lipsync, and expressions which accompany custom voiceovers. It is extremely frequent with educational videos, product demonstrations and corporate explanations. The interface also has a strong background, language, and tone control that makes HeyGen a professional yet easy to use platform.

![](https://cdn-images-1.medium.com/max/1600/1*ebA9soR6u4G4pY-H3u3cqg.png)

**Advantages:**

* Highly realistic facial animation
* Supports multiple languages and voices
* Perfect for business and training presentations
* Produces natural speech and expressions

**Disadvantages:**

* Limited design customization options
* Slower rendering time on free accounts

#### **4. Lumen5**

Lumen5 is a firm that specializes in transforming text contents and blog posts into a trending video. It splits up written text, highlights critical sentences and automatically aligns images, animation and background music. This AI video generator is designed for marketers and bloggers who want to repurpose written content into video form quickly.

![](https://cdn-images-1.medium.com/max/1600/1*2sTLrr9j7owDa2yiouCssA.png)

**Advantages:**

* Automatic summarization of articles
* Large library of visuals and template
* Custom branding options for social media
* Generates videos from URLs and text input

**Disadvantages:**

* Customization is limited in free version
* Watermark appears on free exports

#### **5. InVideo**

InVideo is created to be utilized by marketers who want to make social videos on the large scale. Its AI platform automatically converts text into already shared clips that have professional transitions and animations. It possesses a massive media library and customizable scenes. Best in making advertisement, brand promotion and event videos with bare minimum effort.

![](https://cdn-images-1.medium.com/max/1600/1*PYlPiDlP2glDftxIZ6Fxkg.png)

**Advantages:**

* AI converts text scripts to videos quickly
* Offers numerous social media presets
* Built-in voiceover and subtitle tools
* Excellent marketing templates for quick deployment

**Disadvantages:**

* Interface can feel cluttered
* Export quality slightly reduced in free plan

#### **6. Simplified**

Simplified is a combination of AI-based video generation and team collaboration tools. It facilitates real-time editing, sharing and comments. Users are capable of producing AI-powered videos to market a product, provide tutorials, or advertisements. The platform is also connected with other creative tools to ensure brand consistency throughout all assets.

![](https://cdn-images-1.medium.com/max/1600/1*jtyK9zzaAwg5DUp03sPB_g.png)

**Advantages:**

* Real-time team editing and commenting
* Intuitive dashboard with AI assistance
* Integrates with marketing workflows
* Offers automated resizing for different platforms

**Disadvantages:**

* Performance can lag during collaboration
* Limited video duration in free plan

#### **7. Animoto**

Animoto is a simple application that is meant to be utilized in the process of creating simple videos that are in the form of slideshows. It turns images, texts and short videos into seamless and beautiful videos. The AI assists in transitions, rhythm and background music selection. It can best be used in delivering quick content, event highlights, and simple social updates.

![](https://cdn-images-1.medium.com/max/1600/1*aeRtvc3lgU7WLd2MNgPlxA.png)

**Advantages:**

* Extremely simple drag-and-drop interface
* Offers auto-timing and music sync
* Ideal for educators, beginners, and small businesses
* Supports direct sharing to social media platforms

**Disadvantages:**

* Limited customization options
* Output looks basic compared to advanced tools

### Comparison Table: Best Free Online AI Video Generators

![](https://cdn-images-1.medium.com/max/1600/1*zugeStvg_SH8rMpnp5dWdw.png)

### Conclusion

Choosing the right AI video generator depends on personal goals, workflow, and the kind of content being produced. Some are more of a storyteller and some are more of an automator or collaborator. They have been tested and compared, and one platform was of the same quality, creative, and easy to use at all times CapCut.

CapCut remains the most helpful one in 2025. Its AI avatar and YouTube Shorts maker capabilities make it ideal for both individuals and brands. The most excellent overall experience is the automation, creative flexibility and the intuitive design in terms of free video generation.

The needs of all people are diverse, but in the case of generating a quality AI-driven video quickly, CapCut will be the first tool.

### FAQs

**Which free online AI video generator is best for beginners in 2025?**

CapCut is the best appropriate one. It automates video creation through AI avatars, templates, and instant video generation. The friendly interface allows full projects even without technical expertise.

**Can I use CapCut’s AI avatar feature to make talking videos?**

Yes. CapCut offers over 100 AI avatars for text-to-speech and talking video generation. The users are even able to produce their own avatars to ensure that they receive voiceover content that is customized.

**Are these AI video generators truly free?**

Most platforms, including CapCut, FlexClip, and Lumen5 offer free plans. However, these advanced functions as 4K export or high-quality templates often require upgrade.

**Which AI video generator works best for social media and YouTube Shorts?**

CapCut is the most appropriate choice in terms of social and short-form content. Its YouTube Shorts maker and instant AI editing tools make it ideal for quick, visually engaging posts.

Finding a reliable <a href="https://www.capcut.com/tools/ai-video-generator">AI video generator</a> that actually saves time feels harder than it should. The majority of tools that I have tried claim to deliver instant videos but produce videos that are difficult to watch, slow, or contain heavy watermarks. That is time-wasting and it takes away the creative momentum of small teams or individual creators like myself. I have tried almost all the options that are available in the market, and the following are some of the most thought out solutions that really work. This list eliminates the hype and narrows down to tools that produce useful outputs in a short period of time. Every pick outlines the presence of actual free-tier functionality, convenient workflows, and actual export options. The guide is perfect when you need quick social videos, voice-over explainers, or short videos without having to spend hours editing ads. My recommendations are pegged on personal experience, direct comparison, and the urge to utilize the tools that would indeed accelerate the process of video production.<img src="https://cdn-images-1.medium.com/max/1600/1*_vqFiRDONEmv77Z0KmFSJw.png" alt="">
By 2025, I have been able to see how AI-powered fast video tools have revolutionized how creators produce and disseminate content. Marketing specialists, independent YouTubers, and every type of people are interested in faster, more mechanism-based workflows that will not potentially compromise the quality. The most significant challenge that I had to resolve is to find free platforms that deliver professional results but are not too complicated. Each of the tools listed below is tried personally regarding its availability, video quality and creative freedom. They are the surest choices in the mind of any individual who wants to take his/her content strategy to the next level through the employment of intelligent time-saving automation.
<h3 id="best-free-online-ai-video-generators-in2025">Best Free Online AI Video Generators in 2025</h3>
<ol>
<li>CapCut — Best for <a href="https://www.capcut.com/tools/ai-avatar">AI avatar</a>-based videos with one-click editing and fast, engaging short-form content</li>
<li>FlexClip — Best for quick text-to-video editing</li>
<li>HeyGen — Best for realistic talking video generation</li>
<li>Lumen5 — Best for turning articles into videos</li>
<li>InVideo — Best for marketing and social content creation</li>
<li>Simplified — Best for team-based video collaboration</li>
<li>Animoto — Best for beginners creating slideshow-style videos</li>
</ol>
<img src="https://cdn-images-1.medium.com/max/1600/1*DpBEcEq5tBIiDmOqY3F-Ug.png" alt="">
<h4 id="1-capcut">1. CapCut</h4>
CapCut stands out as one of the most versatile and user-friendly AI video generator platforms in 2025. It enables every person to create full video instantly through AI automation. The platform provides AI avatars, AI templates, and AI-powered automation that transform text prompts into professional-grade content. The Instant AI Video tool of CapCut does it all, including the pacing of the scenes and the background image, making the videos look refined within just a few seconds. It’s also the ideal <a href="https://www.capcut.com/create/youtube-shorts-maker">YouTube Shorts maker</a>, letting creators produce social-ready content quickly and efficiently.
Main Functional Attributes:
<ul>
<li>AI Avatars: Offers over 100 digital avatars for instant video generation. Includes custom avatar cloning to create a personalized on-screen representation that matches specific branding or identity needs.</li>
<li>AI Workflow Templates: Comes with 30+ ready-made templates for news, education, and marketing videos. Each template includes pre-structured scenes to maintain professional pacing, tone, and flow throughout the video.</li>
<li>Instant AI Video: Video generation on scripts with one click. Handles the editing process, visual integration and timing changes automatically and without manual video assembly.</li>
<li>AI Brainstorming Tool: A creative assistant that proposes ideas, storyboards, and individual directions. Developed to address creative blocks and boost ideation in the marketing, education, or content development process.</li>
<li>Unlimited Media Assets: Allows unlimited access to a large collection of stock footage, music, fonts and graphics. Ideal to tailor videos and create content that is engaging and will optimize on social media.</li>
</ul>
<img src="https://cdn-images-1.medium.com/max/1600/1*d5O_7w5nU_iDdRvCpeZLRw.png" alt="">
Advantages:
<ul>
<li>Simple interface suitable for all skill levels</li>
<li>Produces professional-quality videos in minutes</li>
<li>Large selection of avatars and templates</li>
<li>Strong AI automation saves time and effort</li>
<li>Free to use with powerful AI-driven workflow</li>
</ul>
Disadvantages:
<ul>
<li>Some features require pro account to access</li>
</ul>
<h4 id="2-flexclip">2. FlexClip</h4>
FlexClip is a fast and effective online converter of turning scripts into videos. It simplifies text-to-video using AI-based templates and auto-captioning. The users can input the text quickly, choose the visuals and leave the system to develop the fluent transitions and layouts. It is most effective on social media content, tutorials and explainer videos.
<img src="https://cdn-images-1.medium.com/max/1600/1*ZI3CnFFnNnDVBLQxkZfTsA.png" alt="">
Advantages:
<ul>
<li>Auto-subtitles improve accessibility</li>
<li>Easy drag-and-drop timeline</li>
<li>Offers hundreds of stock clips and music tracks</li>
<li>Ideal for beginners needing fast results</li>
</ul>
Disadvantages:
<ul>
<li>Limited advanced editing features</li>
<li>Free plan includes watermark on exports</li>
</ul>
<h4 id="3-heygen">3. HeyGen</h4>
HeyGen does a superb job at creating a realistic talking-head video on the basis of a text. Its AI renders facial expressions, lipsync, and expressions which accompany custom voiceovers. It is extremely frequent with educational videos, product demonstrations and corporate explanations. The interface also has a strong background, language, and tone control that makes HeyGen a professional yet easy to use platform.
<img src="https://cdn-images-1.medium.com/max/1600/1*ebA9soR6u4G4pY-H3u3cqg.png" alt="">
Advantages:
<ul>
<li>Highly realistic facial animation</li>
<li>Supports multiple languages and voices</li>
<li>Perfect for business and training presentations</li>
<li>Produces natural speech and expressions</li>
</ul>
Disadvantages:
<ul>
<li>Limited design customization options</li>
<li>Slower rendering time on free accounts</li>
</ul>
<h4 id="4-lumen5">4. Lumen5</h4>
Lumen5 is a firm that specializes in transforming text contents and blog posts into a trending video. It splits up written text, highlights critical sentences and automatically aligns images, animation and background music. This AI video generator is designed for marketers and bloggers who want to repurpose written content into video form quickly.
<img src="https://cdn-images-1.medium.com/max/1600/1*2sTLrr9j7owDa2yiouCssA.png" alt="">
Advantages:
<ul>
<li>Automatic summarization of articles</li>
<li>Large library of visuals and template</li>
<li>Custom branding options for social media</li>
<li>Generates videos from URLs and text input</li>
</ul>
Disadvantages:
<ul>
<li>Customization is limited in free version</li>
<li>Watermark appears on free exports</li>
</ul>
<h4 id="5-invideo">5. InVideo</h4>
InVideo is created to be utilized by marketers who want to make social videos on the large scale. Its AI platform automatically converts text into already shared clips that have professional transitions and animations. It possesses a massive media library and customizable scenes. Best in making advertisement, brand promotion and event videos with bare minimum effort.
<img src="https://cdn-images-1.medium.com/max/1600/1*PYlPiDlP2glDftxIZ6Fxkg.png" alt="">
Advantages:
<ul>
<li>AI converts text scripts to videos quickly</li>
<li>Offers numerous social media presets</li>
<li>Built-in voiceover and subtitle tools</li>
<li>Excellent marketing templates for quick deployment</li>
</ul>
Disadvantages:
<ul>
<li>Interface can feel cluttered</li>
<li>Export quality slightly reduced in free plan</li>
</ul>
<h4 id="6-simplified">6. Simplified</h4>
Simplified is a combination of AI-based video generation and team collaboration tools. It facilitates real-time editing, sharing and comments. Users are capable of producing AI-powered videos to market a product, provide tutorials, or advertisements. The platform is also connected with other creative tools to ensure brand consistency throughout all assets.
<img src="https://cdn-images-1.medium.com/max/1600/1*jtyK9zzaAwg5DUp03sPB_g.png" alt="">
Advantages:
<ul>
<li>Real-time team editing and commenting</li>
<li>Intuitive dashboard with AI assistance</li>
<li>Integrates with marketing workflows</li>
<li>Offers automated resizing for different platforms</li>
</ul>
Disadvantages:
<ul>
<li>Performance can lag during collaboration</li>
<li>Limited video duration in free plan</li>
</ul>
<h4 id="7-animoto">7. Animoto</h4>
Animoto is a simple application that is meant to be utilized in the process of creating simple videos that are in the form of slideshows. It turns images, texts and short videos into seamless and beautiful videos. The AI assists in transitions, rhythm and background music selection. It can best be used in delivering quick content, event highlights, and simple social updates.
<img src="https://cdn-images-1.medium.com/max/1600/1*aeRtvc3lgU7WLd2MNgPlxA.png" alt="">
Advantages:
<ul>
<li>Extremely simple drag-and-drop interface</li>
<li>Offers auto-timing and music sync</li>
<li>Ideal for educators, beginners, and small businesses</li>
<li>Supports direct sharing to social media platforms</li>
</ul>
Disadvantages:
<ul>
<li>Limited customization options</li>
<li>Output looks basic compared to advanced tools</li>
</ul>
<h3 id="comparison-table-best-free-online-ai-video-generators">Comparison Table: Best Free Online AI Video Generators</h3>
<img src="https://cdn-images-1.medium.com/max/1600/1*zugeStvg_SH8rMpnp5dWdw.png" alt="">
<h3 id="conclusion">Conclusion</h3>
Choosing the right AI video generator depends on personal goals, workflow, and the kind of content being produced. Some are more of a storyteller and some are more of an automator or collaborator. They have been tested and compared, and one platform was of the same quality, creative, and easy to use at all times CapCut.
CapCut remains the most helpful one in 2025. Its AI avatar and YouTube Shorts maker capabilities make it ideal for both individuals and brands. The most excellent overall experience is the automation, creative flexibility and the intuitive design in terms of free video generation.
The needs of all people are diverse, but in the case of generating a quality AI-driven video quickly, CapCut will be the first tool.
<h3 id="faqs">FAQs</h3>
Which free online AI video generator is best for beginners in 2025?
CapCut is the best appropriate one. It automates video creation through AI avatars, templates, and instant video generation. The friendly interface allows full projects even without technical expertise.
Can I use CapCut’s AI avatar feature to make talking videos?
Yes. CapCut offers over 100 AI avatars for text-to-speech and talking video generation. The users are even able to produce their own avatars to ensure that they receive voiceover content that is customized.
Are these AI video generators truly free?
Most platforms, including CapCut, FlexClip, and Lumen5 offer free plans. However, these advanced functions as 4K export or high-quality templates often require upgrade.
Which AI video generator works best for social media and YouTube Shorts?
CapCut is the most appropriate choice in terms of social and short-form content. Its YouTube Shorts maker and instant AI editing tools make it ideal for quick, visually engaging posts.

How to Make Your Tech Gadget the Star of TikTok

As everywhere else, Artificial Intelligence came to the energy industry, which had been operating with minimal changes for years, and decided to completely change everything.

AI has come to help, improve, and make more convenient an industry that has long needed it. Picture a dispatcher who needs to monitor thousands of parameters at once: everything from weather patterns to load in every city district. No human can physically process that much information in real time. That’s where artificial intelligence steps in — it spots patterns where we only see chaotic numbers.

This article looks at companies that aren’t just experimenting with technology — they’re getting real results. From giants optimizing entire power grids to specialized firms teaching algorithms to predict the future. You’ll learn who’s doing the most interesting work in AI in energy industry, and how their solutions are changing what we thought was possible.

### What’s Happening in the Market: From Experiments to Mass Adoption

Five years ago, artificial intelligence in energy was just buzzwords at conferences. Now it’s a working tool that saves money and prevents accidents every single day. The market is worth tens of billions of dollars and growing exponentially.

Energy companies use generative models to **simulate scenarios**: what happens to the grid if temperature suddenly drops 15 degrees? Or how does the system behave when one substation goes offline? These simulations used to take engineers weeks. Now algorithms do it in hours.

Another technology gaining momentum — **digital twins**. These are virtual copies of real objects: turbines, transformers, entire power plants. AI feeds these twins with sensor data and learns to predict problems. When a turbine bearing starts overheating, the system notices it from vibration changes, weeks before actual failure.

**Computer vision** is finding applications too. Drones with cameras and AI fly along power lines, spotting damaged insulation or dangerous trees near wires. What used to take months of inspections now takes days.

**Big data** stopped being a problem — it became the solution. Every smart meter, every sensor, every weather station sends information to cloud systems. AI digests this flow and gives recommendations: where to strengthen the grid, when to fire up backup generators, how to distribute load between energy sources.

**Blockchain** and AI are combining to create decentralized energy markets where every solar panel owner can automatically sell excess power to neighbors. Quantum computing is still in labs but promises breakthroughs in optimizing complex networks.

### TOP-5 Global Companies Implementing AI in the Energy Industry

### DXC Technology

![](https://cdn-images-1.medium.com/max/1600/1*S5lRYDyrmzTpe_vKMovSWg.png)

Website: [https://dxc.com/us/en/industries/energy](https://dxc.com/us/en/industries/energy)

DXC Technology works with energy companies worldwide, helping them transition from legacy systems to intelligent infrastructure.

Their approach centers on modernizing the entire chain: from extraction to end consumers. They implement cloud platforms that unite data from different sources — from wells to smart meters in apartments. AI analyzes this data in real time, detecting anomalies and suggesting optimizations.

One interesting direction — working with digital twins for complex oil, gas, and electrical infrastructure. Imagine a virtual copy of an oil refinery that lets you test process changes without risking actual production. Or a grid model where you can “play out” different load scenarios before a major cold snap or heat wave.

DXC also handles cybersecurity for energy facilities — critically important in an era when hacker attacks on infrastructure have become routine. Their AI systems monitor network traffic and detect suspicious activity that might signal penetration attempts.

Interestingly, the company bets on partnerships with leading cloud and AI platform providers, creating an ecosystem of solutions rather than monolithic products. This approach gives energy companies flexibility: they can implement changes gradually without needing to overhaul their entire IT infrastructure at once.

### Schneider Electric

![](https://cdn-images-1.medium.com/max/1600/1*TBCzVrVkgaiHjaml7UHWbA.png)

French giant Schneider Electric is better known for its equipment — from circuit breakers to building management systems. Recent years, the company has invested heavily in software, turning hardware into smart systems.

Their EcoStruxure platform is an ecosystem where physical devices talk to each other and to cloud AI. Sensors collect electricity consumption data, algorithms hunt for waste.

Schneider offers generative AI use cases in energy sector for industrial enterprises. Their models simulate different production line configurations and show where you can save energy without losing productivity. For a factory, this can mean millions of dollars per year.

In renewable energy, Schneider created solutions for managing microgrids — local energy systems with their own generation. AI optimizes use of solar panels, wind turbines, batteries, and connection to the main grid. When electricity market price is high — you sell surplus, when low — you store in batteries.

The company also works on systems for EV charging stations. With mass adoption of electric vehicles, a problem emerged: how do you charge thousands of cars simultaneously without overloading the grid? Schneider’s AI distributes load, considering tariffs, owner priorities (someone needs urgent charging, someone can wait) and grid condition.

### Siemens Energy

![](https://cdn-images-1.medium.com/max/1600/1*gdB6gyxUApkXCFOwWedI3w.png)

German Siemens Energy split from its parent corporation to focus on energy solutions. They build power plants, produce turbines, create electricity transmission systems. And they’re actively implementing AI in all these processes.

Flagship — a program for monitoring gas turbines. These huge machines cost millions of dollars and are the heart of many power plants. Siemens installs hundreds of sensors on them and transmits data to a cloud AI in energy management system. Algorithms study each turbine’s “behavior” and create an individual profile. When something goes outside the norm — the system signals.

Results are striking: equipment downtime dropped 20–25%, and maintenance costs fell almost in half. Instead of regular checks “just in case,” technicians only come when actually needed, and already know what the exact problem is.

Siemens Energy actively works with hydrogen energy — one of promising directions for decarbonization. AI helps optimize green hydrogen production processes (from renewable electricity) and manages storage systems. Hydrogen is tricky: high explosion risk, storage complexity, transportation losses. Smart systems monitor every stage of the chain and prevent problems.

The company also developed a solution for integrating renewable sources into large power grids. Main problem with wind and solar farms — instability. AI forecasts generation and coordinates traditional power plant operations that compensate for gaps. The system learns from historical data and constantly improves forecast accuracy.

### SparkCognition (Avathon)

![](https://cdn-images-1.medium.com/max/1600/1*Hon-eqUehQbP1DmUCr5JVQ.png)

American company SparkCognition shows how a young team can compete with giants through specialization. They focus on AI for critical infrastructure, with energy taking the lead.

Their SparkPredict product uses machine learning to predict equipment failures. In reality, old power plants often don’t have hundreds of sensors, just basic indicators. SparkPredict squeezes maximum information from minimum data, finding non-obvious correlations.

The company developed a separate solution for wind energy — DeepArmor Wind. Wind turbines stand in hard-to-reach places, often offshore. Every technician visit means a helicopter or special vessel, huge expenses. SparkCognition’s AI monitors blade condition, generators, bearings and plans maintenance as efficiently as possible: one visit, ten turbines, all problems solved.

SparkCognition also works with oil and gas, where AI helps optimize drilling operations. The system analyzes geological data, drilling parameters and recommends optimal settings. This increases speed and reduces accident risk.

Interestingly, the company actively uses computer vision. Their algorithms analyze photos and videos from drones and cameras, detecting infrastructure damage. A crack in a power line tower, pipeline corrosion, wind turbine foundation shift — AI sees what the human eye easily misses.

### Uplight

![](https://cdn-images-1.medium.com/max/1600/1*IvlXtaAwbdQklyQBqMHpTw.png)

American Uplight chose a niche many ignore: not energy production but managing its consumption. The idea is simple: the cheapest energy is the energy you don’t need to produce. The company’s AI helps energy suppliers influence consumer behavior.

Their platform collects data from smart meters and devices in client homes. AI builds consumption profiles and personalized recommendations: turn off your air conditioner for an hour during peak time, get a discount on your bill. Or let the system automatically manage your electric vehicle charging — it’ll charge at night when rates are low.

Uplight works with dozens of energy companies in the US and Canada, covering millions of households. According to their data, demand management programs reduce peak grid load by 10–15%. This lets energy companies postpone or avoid building expensive new power plants altogether.

The company also developed a solution for prosumers — people who simultaneously consume and produce energy (solar panel owners, home battery owners). AI optimizes their behavior: when to consume their own electricity, when to sell to the grid, when to buy from the market. The system considers weather forecasts, tariffs, battery state and self-learning skills.

Uplight actively uses behavioral psychology. Their algorithms don’t just give recommendations but phrase them so people actually act. Gamification, social comparisons (your neighbors save more), reminders — everything is integrated into the platform.

Another direction — electrification programs. Uplight helps energy companies incentivize clients to switch from gas to electricity (heat pumps instead of gas boilers, induction stoves instead of gas). AI in energy sector here analyzes which households are most likely to switch and offers them personalized incentives.

### 5 Generative AI Use Cases in Energy Sector

Let’s look at concrete examples where AI didn’t just optimize processes but fundamentally changed how energy companies operate. These aren’t theoretical possibilities — they’re implementations that already delivered results.

### Duke Energy’s Grid Load Forecasting

Duke Energy, one of America’s largest utilities, implemented generative AI for load forecasting across their service territory covering multiple states. Traditional models struggled with the increasing complexity — electric vehicles, home solar panels, heat pumps all adding unpredictability.

Their generative AI creates thousands of possible demand scenarios for different time horizons: next hour, next day, next week. Instead of one forecast, grid operators now see a range of likely outcomes with confidence intervals. This helps them make better decisions about which generators to keep spinning and when to buy power from neighbors.

The accuracy improvement was dramatic — forecast errors dropped 40% during critical peak periods.

### Ørsted’s Wind Farm Optimization

Danish renewable energy giant Ørsted used generative AI to revolutionize their offshore wind farm operations. The system generates optimized maintenance schedules by simulating weather patterns, equipment wear, and vessel availability months in advance.

For instance, it discovered that deliberately running some turbines at slightly reduced capacity during certain wind conditions actually increases total farm output by reducing turbulence effects on neighboring turbines.

This counterintuitive finding came from the AI generating and testing millions of operational scenarios. Human operators would never have tested this because it contradicts conventional wisdom. The result: 7% increase in annual energy production from existing assets, no new hardware needed.

### Exelon’s Equipment Failure Prevention

Exelon, operating one of America’s largest nuclear fleets, deployed generative AI for component failure prediction. Nuclear plants have thousands of components, each critical to safety. The AI generates failure mode scenarios based on component age, operating conditions, and subtle pattern changes in sensor data.

When it flags a pump bearing for attention, it generates a detailed scenario description. This transparency helped engineers trust and act on AI recommendations. Since implementation, unplanned outages decreased 45%, and the plant achieved its longest continuous operation record.

### National Grid’s Scenario Planning

UK’s National Grid uses generative AI for long-term infrastructure planning. The system generates detailed scenarios of future energy landscapes: different adoption rates of EVs, heat pumps, distributed solar, and hydrogen.

For each scenario, the AI generates complete grid upgrade plans — where to reinforce lines, where to add substations, how to integrate storage.

One generated scenario suggested a radically different approach to grid stability: instead of building massive new transmission lines, deploy thousands of small battery installations at strategic points. Engineers were skeptical but ran the numbers — turned out cheaper and faster to implement than traditional approaches.

### BP’s Refinery Optimization

BP implemented generative AI at several refineries to optimize complex chemical processes. The AI generates process configurations that maximize desired products while minimizing energy consumption and emissions.

The system discovered process settings that produce more jet fuel from the same crude input without additional equipment. The implementation increased valuable product yield by 4% while cutting energy use 6%. For a large refinery processing hundreds of thousands of barrels daily, this means tens of millions in additional annual profit.

These cases share common threads: generative AI found entirely new ways to solve problems. It generated solutions outside human experience and conventional wisdom.

### Conclusions: Energy Through AI’s Eyes

Pull all these stories together, and you get a picture of a future that’s almost here. The energy system is transforming into a huge intelligent organism where millions of devices communicate and make decisions faster than humans.

The companies we discussed show different approaches. Some focus on equipment, some on software, some on changing consumer behavior. But all of them use AI to solve real problems: how to make energy cheaper, more reliable, cleaner.

The most interesting part lies ahead. Generative AI use cases in energy sector are just starting to unfold.

AI helps us move toward a world where energy is clean, accessible, and reliable.

As everywhere else, Artificial Intelligence came to the energy industry, which had been operating with minimal changes for years, and decided to completely change everything.
AI has come to help, improve, and make more convenient an industry that has long needed it. Picture a dispatcher who needs to monitor thousands of parameters at once: everything from weather patterns to load in every city district. No human can physically process that much information in real time. That’s where artificial intelligence steps in — it spots patterns where we only see chaotic numbers.
This article looks at companies that aren’t just experimenting with technology — they’re getting real results. From giants optimizing entire power grids to specialized firms teaching algorithms to predict the future. You’ll learn who’s doing the most interesting work in AI in energy industry, and how their solutions are changing what we thought was possible.
<h3 id="whats-happening-in-the-market-from-experiments-to-massadoption">What’s Happening in the Market: From Experiments to Mass Adoption</h3>
Five years ago, artificial intelligence in energy was just buzzwords at conferences. Now it’s a working tool that saves money and prevents accidents every single day. The market is worth tens of billions of dollars and growing exponentially.
Energy companies use generative models to simulate scenarios: what happens to the grid if temperature suddenly drops 15 degrees? Or how does the system behave when one substation goes offline? These simulations used to take engineers weeks. Now algorithms do it in hours.
Another technology gaining momentum — digital twins. These are virtual copies of real objects: turbines, transformers, entire power plants. AI feeds these twins with sensor data and learns to predict problems. When a turbine bearing starts overheating, the system notices it from vibration changes, weeks before actual failure.
Computer vision is finding applications too. Drones with cameras and AI fly along power lines, spotting damaged insulation or dangerous trees near wires. What used to take months of inspections now takes days.
Big data stopped being a problem — it became the solution. Every smart meter, every sensor, every weather station sends information to cloud systems. AI digests this flow and gives recommendations: where to strengthen the grid, when to fire up backup generators, how to distribute load between energy sources.
Blockchain and AI are combining to create decentralized energy markets where every solar panel owner can automatically sell excess power to neighbors. Quantum computing is still in labs but promises breakthroughs in optimizing complex networks.
<h3 id="top-5-global-companies-implementing-ai-in-the-energyindustry">TOP-5 Global Companies Implementing AI in the Energy Industry</h3>
<h3 id="dxc-technology">DXC Technology</h3>
<img src="https://cdn-images-1.medium.com/max/1600/1*S5lRYDyrmzTpe_vKMovSWg.png" alt="">
Website: <a href="https://dxc.com/us/en/industries/energy">https://dxc.com/us/en/industries/energy</a>
DXC Technology works with energy companies worldwide, helping them transition from legacy systems to intelligent infrastructure.
Their approach centers on modernizing the entire chain: from extraction to end consumers. They implement cloud platforms that unite data from different sources — from wells to smart meters in apartments. AI analyzes this data in real time, detecting anomalies and suggesting optimizations.
One interesting direction — working with digital twins for complex oil, gas, and electrical infrastructure. Imagine a virtual copy of an oil refinery that lets you test process changes without risking actual production. Or a grid model where you can “play out” different load scenarios before a major cold snap or heat wave.
DXC also handles cybersecurity for energy facilities — critically important in an era when hacker attacks on infrastructure have become routine. Their AI systems monitor network traffic and detect suspicious activity that might signal penetration attempts.
Interestingly, the company bets on partnerships with leading cloud and AI platform providers, creating an ecosystem of solutions rather than monolithic products. This approach gives energy companies flexibility: they can implement changes gradually without needing to overhaul their entire IT infrastructure at once.
<h3 id="schneider-electric">Schneider Electric</h3>
<img src="https://cdn-images-1.medium.com/max/1600/1*TBCzVrVkgaiHjaml7UHWbA.png" alt="">
French giant Schneider Electric is better known for its equipment — from circuit breakers to building management systems. Recent years, the company has invested heavily in software, turning hardware into smart systems.
Their EcoStruxure platform is an ecosystem where physical devices talk to each other and to cloud AI. Sensors collect electricity consumption data, algorithms hunt for waste.
Schneider offers generative AI use cases in energy sector for industrial enterprises. Their models simulate different production line configurations and show where you can save energy without losing productivity. For a factory, this can mean millions of dollars per year.
In renewable energy, Schneider created solutions for managing microgrids — local energy systems with their own generation. AI optimizes use of solar panels, wind turbines, batteries, and connection to the main grid. When electricity market price is high — you sell surplus, when low — you store in batteries.
The company also works on systems for EV charging stations. With mass adoption of electric vehicles, a problem emerged: how do you charge thousands of cars simultaneously without overloading the grid? Schneider’s AI distributes load, considering tariffs, owner priorities (someone needs urgent charging, someone can wait) and grid condition.
<h3 id="siemens-energy">Siemens Energy</h3>
<img src="https://cdn-images-1.medium.com/max/1600/1*gdB6gyxUApkXCFOwWedI3w.png" alt="">
German Siemens Energy split from its parent corporation to focus on energy solutions. They build power plants, produce turbines, create electricity transmission systems. And they’re actively implementing AI in all these processes.
Flagship — a program for monitoring gas turbines. These huge machines cost millions of dollars and are the heart of many power plants. Siemens installs hundreds of sensors on them and transmits data to a cloud AI in energy management system. Algorithms study each turbine’s “behavior” and create an individual profile. When something goes outside the norm — the system signals.
Results are striking: equipment downtime dropped 20–25%, and maintenance costs fell almost in half. Instead of regular checks “just in case,” technicians only come when actually needed, and already know what the exact problem is.
Siemens Energy actively works with hydrogen energy — one of promising directions for decarbonization. AI helps optimize green hydrogen production processes (from renewable electricity) and manages storage systems. Hydrogen is tricky: high explosion risk, storage complexity, transportation losses. Smart systems monitor every stage of the chain and prevent problems.
The company also developed a solution for integrating renewable sources into large power grids. Main problem with wind and solar farms — instability. AI forecasts generation and coordinates traditional power plant operations that compensate for gaps. The system learns from historical data and constantly improves forecast accuracy.
<h3 id="sparkcognition-avathon">SparkCognition (Avathon)</h3>
<img src="https://cdn-images-1.medium.com/max/1600/1*Hon-eqUehQbP1DmUCr5JVQ.png" alt="">
American company SparkCognition shows how a young team can compete with giants through specialization. They focus on AI for critical infrastructure, with energy taking the lead.
Their SparkPredict product uses machine learning to predict equipment failures. In reality, old power plants often don’t have hundreds of sensors, just basic indicators. SparkPredict squeezes maximum information from minimum data, finding non-obvious correlations.
The company developed a separate solution for wind energy — DeepArmor Wind. Wind turbines stand in hard-to-reach places, often offshore. Every technician visit means a helicopter or special vessel, huge expenses. SparkCognition’s AI monitors blade condition, generators, bearings and plans maintenance as efficiently as possible: one visit, ten turbines, all problems solved.
SparkCognition also works with oil and gas, where AI helps optimize drilling operations. The system analyzes geological data, drilling parameters and recommends optimal settings. This increases speed and reduces accident risk.
Interestingly, the company actively uses computer vision. Their algorithms analyze photos and videos from drones and cameras, detecting infrastructure damage. A crack in a power line tower, pipeline corrosion, wind turbine foundation shift — AI sees what the human eye easily misses.
<h3 id="uplight">Uplight</h3>
<img src="https://cdn-images-1.medium.com/max/1600/1*IvlXtaAwbdQklyQBqMHpTw.png" alt="">
American Uplight chose a niche many ignore: not energy production but managing its consumption. The idea is simple: the cheapest energy is the energy you don’t need to produce. The company’s AI helps energy suppliers influence consumer behavior.
Their platform collects data from smart meters and devices in client homes. AI builds consumption profiles and personalized recommendations: turn off your air conditioner for an hour during peak time, get a discount on your bill. Or let the system automatically manage your electric vehicle charging — it’ll charge at night when rates are low.
Uplight works with dozens of energy companies in the US and Canada, covering millions of households. According to their data, demand management programs reduce peak grid load by 10–15%. This lets energy companies postpone or avoid building expensive new power plants altogether.
The company also developed a solution for prosumers — people who simultaneously consume and produce energy (solar panel owners, home battery owners). AI optimizes their behavior: when to consume their own electricity, when to sell to the grid, when to buy from the market. The system considers weather forecasts, tariffs, battery state and self-learning skills.
Uplight actively uses behavioral psychology. Their algorithms don’t just give recommendations but phrase them so people actually act. Gamification, social comparisons (your neighbors save more), reminders — everything is integrated into the platform.
Another direction — electrification programs. Uplight helps energy companies incentivize clients to switch from gas to electricity (heat pumps instead of gas boilers, induction stoves instead of gas). AI in energy sector here analyzes which households are most likely to switch and offers them personalized incentives.
<h3 id="5-generative-ai-use-cases-in-energysector">5 Generative AI Use Cases in Energy Sector</h3>
Let’s look at concrete examples where AI didn’t just optimize processes but fundamentally changed how energy companies operate. These aren’t theoretical possibilities — they’re implementations that already delivered results.
<h3 id="duke-energys-grid-load-forecasting">Duke Energy’s Grid Load Forecasting</h3>
Duke Energy, one of America’s largest utilities, implemented generative AI for load forecasting across their service territory covering multiple states. Traditional models struggled with the increasing complexity — electric vehicles, home solar panels, heat pumps all adding unpredictability.
Their generative AI creates thousands of possible demand scenarios for different time horizons: next hour, next day, next week. Instead of one forecast, grid operators now see a range of likely outcomes with confidence intervals. This helps them make better decisions about which generators to keep spinning and when to buy power from neighbors.
The accuracy improvement was dramatic — forecast errors dropped 40% during critical peak periods.
<h3 id="ørsteds-wind-farm-optimization">Ørsted’s Wind Farm Optimization</h3>
Danish renewable energy giant Ørsted used generative AI to revolutionize their offshore wind farm operations. The system generates optimized maintenance schedules by simulating weather patterns, equipment wear, and vessel availability months in advance.
For instance, it discovered that deliberately running some turbines at slightly reduced capacity during certain wind conditions actually increases total farm output by reducing turbulence effects on neighboring turbines.
This counterintuitive finding came from the AI generating and testing millions of operational scenarios. Human operators would never have tested this because it contradicts conventional wisdom. The result: 7% increase in annual energy production from existing assets, no new hardware needed.
<h3 id="exelons-equipment-failure-prevention">Exelon’s Equipment Failure Prevention</h3>
Exelon, operating one of America’s largest nuclear fleets, deployed generative AI for component failure prediction. Nuclear plants have thousands of components, each critical to safety. The AI generates failure mode scenarios based on component age, operating conditions, and subtle pattern changes in sensor data.
When it flags a pump bearing for attention, it generates a detailed scenario description. This transparency helped engineers trust and act on AI recommendations. Since implementation, unplanned outages decreased 45%, and the plant achieved its longest continuous operation record.
<h3 id="national-grids-scenarioplanning">National Grid’s Scenario Planning</h3>
UK’s National Grid uses generative AI for long-term infrastructure planning. The system generates detailed scenarios of future energy landscapes: different adoption rates of EVs, heat pumps, distributed solar, and hydrogen.
For each scenario, the AI generates complete grid upgrade plans — where to reinforce lines, where to add substations, how to integrate storage.
One generated scenario suggested a radically different approach to grid stability: instead of building massive new transmission lines, deploy thousands of small battery installations at strategic points. Engineers were skeptical but ran the numbers — turned out cheaper and faster to implement than traditional approaches.
<h3 id="bps-refinery-optimization">BP’s Refinery Optimization</h3>
BP implemented generative AI at several refineries to optimize complex chemical processes. The AI generates process configurations that maximize desired products while minimizing energy consumption and emissions.
The system discovered process settings that produce more jet fuel from the same crude input without additional equipment. The implementation increased valuable product yield by 4% while cutting energy use 6%. For a large refinery processing hundreds of thousands of barrels daily, this means tens of millions in additional annual profit.
These cases share common threads: generative AI found entirely new ways to solve problems. It generated solutions outside human experience and conventional wisdom.
<h3 id="conclusions-energy-through-aiseyes">Conclusions: Energy Through AI’s Eyes</h3>
Pull all these stories together, and you get a picture of a future that’s almost here. The energy system is transforming into a huge intelligent organism where millions of devices communicate and make decisions faster than humans.
The companies we discussed show different approaches. Some focus on equipment, some on software, some on changing consumer behavior. But all of them use AI to solve real problems: how to make energy cheaper, more reliable, cleaner.
The most interesting part lies ahead. Generative AI use cases in energy sector are just starting to unfold.
AI helps us move toward a world where energy is clean, accessible, and reliable.

5 Best Companies Implementing AI in the Energy Industry

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Top 7 Free Online AI Video Generators Worth Trying in 2025

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