AI Energy Tech IPOs: What Kraken Signals for Kazakhstan

London wants to prove it can still finance big, modern energy technology companies—and Kraken’s rumoured £7bn IPO is turning into a public stress test. Octopus Energy chief Greg Jackson has hinted that a London listing would be a “no brainer” if UK markets keep their momentum and show they can attract new capital in 2025.

That sentence matters far beyond the City. A tech platform built for energy operations—billing, forecasting, grid flexibility, customer service—only gets valued at IPO scale when investors believe software can reshape the economics of electricity and gas. And if global markets are willing to price that belief at billions, Kazakhstan’s energy and oil‑gas leaders should treat it as a signal, not a headline.

In this post (part of our series «Қазақстандағы энергия және мұнай-газ саласын жасанды интеллект қалай түрлендіріп жатыр») I’ll use the Kraken story as a practical lens: what investors are rewarding, why “where you list” is a strategy decision, and what Kazakhstan-based companies can do in the next 6–18 months to capture real value from AI in energy and AI in oil and gas.

Why Kraken’s IPO chatter is really about AI and energy software

The core point: Markets are paying for repeatable energy software, not one-off projects. Kraken isn’t being discussed as “a utility tool.” It’s being framed as a scalable tech business—exactly the profile that attracts premium multiples.

Energy companies have always used IT. The difference now is that modern platforms embed machine learning, automation, and real-time decisioning into daily operations. When that becomes a product—sold to multiple markets—investors see compounding growth.

What Kraken represents (even from limited public info)

We only have the RSS summary here, but the positioning is familiar across energy tech:

• Operational platforms that automate high-volume processes (metering, settlement, billing)
• Demand forecasting and optimization that reduce imbalance costs
• Customer and field workflows (tickets, outages, maintenance scheduling)
• Increasingly, flexibility and distributed energy orchestration (EV charging, batteries, dynamic tariffs)

The AI angle isn’t “a chatbot.” The AI value is in decisions made thousands of times per day—pricing, routing, predicting, preventing errors—where even a small percentage improvement becomes serious money.

A useful rule: if a process happens daily at scale, AI doesn’t need to be perfect to pay for itself.

London vs New York: the listing venue is a strategy choice

A listing isn’t just a fundraising event. It’s a narrative lock-in: your investors, analyst coverage, governance expectations, and future capital costs are shaped by where you list.

Jackson’s comment—London is a “no brainer” if performance holds and fresh capital comes in—highlights two investor realities:

1. Liquidity and valuation: founders care about whether the market will pay for growth
2. Depth of specialist capital: energy tech often needs investors who understand both software margins and energy regulation

What this teaches Kazakhstan’s energy leaders

Kazakhstan’s energy and oil‑gas ecosystem doesn’t need to copy London or New York. But it does need to internalize the underlying lesson:

• Capital follows clarity. If you can explain how AI reduces costs, improves reliability, and scales across assets, capital shows up.
• Markets reward repeatability. Investors prefer platforms and playbooks over bespoke “digitalization” programs.
• Governance matters. AI systems touch billing accuracy, dispatch decisions, and safety workflows—governance becomes part of valuation.

If you’re a Kazakhstani operator, the practical question becomes: Are we building internal tools that stay internal, or capability we can standardize across fields, plants, and subsidiaries?

What “AI transformation” looks like in Kazakhstan’s energy and oil‑gas—when it’s real

The fastest way to waste money is to call everything AI. The fastest way to create value is to pick a handful of operational pain points where data exists and the economics are obvious.

Below are the AI use cases I see producing the clearest ROI in energy and oil‑gas—globally—and they map well to Kazakhstan’s asset base.

AI in oil and gas: production, reliability, and safety

Answer first: In upstream and midstream, AI pays back when it reduces unplanned downtime, stabilizes production, and prevents safety incidents.

High-impact areas include:

1. Predictive maintenance for rotating equipment (pumps, compressors)
   • Inputs: vibration, temperature, pressure, maintenance logs
   • Output: risk scores and recommended interventions
2. Production optimization
   • Detects water breakthrough, gas lift inefficiencies, choke optimization opportunities
3. Leak detection and corrosion risk
   • Combining sensor data with inspection imagery and operating conditions
4. HSE analytics
   • Near-miss trend detection, permit-to-work anomaly alerts, fatigue risk signals

A stance I’ll defend: Start with reliability before you chase advanced optimization. If you can’t trust your downtime data and work order history, your “optimization AI” turns into a dashboard that nobody uses.

AI in power and grids: forecasting and flexibility

Answer first: In power systems, AI value comes from better forecasts and faster operational decisions.

Practical examples:

• Load forecasting for dispatch planning and procurement
• Renewables forecasting (wind/solar) to reduce balancing costs
• Outage prediction using weather + asset condition + historical failures
• Loss detection (technical and non-technical) in distribution networks

Kazakhstan’s grid modernization discussions often focus on hardware. Hardware is necessary—but software is what makes expensive assets behave intelligently.

Customer operations: where platforms quietly save millions

Even traditional energy businesses have “tech-company problems” in customer operations: high volume, strict accuracy requirements, and regulatory pressure.

AI and automation typically improve:

• Billing accuracy (anomaly detection on meter reads and invoices)
• Collections prioritization (risk scoring and outreach timing)
• Contact center productivity (agent assist, document automation)

This is where Kraken-like platforms often shine. When investors value an energy software business, they’re valuing these repeatable operational gains.

If investors are paying for platforms, what should Kazakhstan build?

Answer first: Build an “AI operating system” for your assets: data foundations, a small set of reusable models, and governance that keeps it safe.

Most companies get stuck because they treat AI as a pilot factory. They run 10 proofs of concept, get 2 demos, and end up with 0 scaled deployments.

Here’s a more bankable approach.

1) Pick 3 use cases with measurable unit economics

Good use cases have:

• A clear cost baseline (downtime hours, energy losses, truck rolls, penalty fees)
• Data available at reasonable quality
• An operational owner who will change the workflow

A simple filter I like:

• Impact: Can this save or earn ≥ 1% of OPEX for that unit?
• Speed: Can we ship an MVP in 8–12 weeks?
• Scale: Can we replicate it across multiple sites?

2) Fix data reliability like it’s a safety issue

AI isn’t blocked by a lack of algorithms. It’s blocked by:

• Missing tags and sensor drift
• Conflicting asset hierarchies (ERP vs SCADA vs CMMS)
• Unstructured maintenance notes that nobody standardizes

Treat data quality as operational risk. Put owners on critical datasets (production, downtime, maintenance, metering) and track data SLAs.

3) Choose architecture that supports scaling

For energy and oil‑gas, scaling usually means:

• A lakehouse pattern (structured + unstructured data)
• Real-time ingestion for SCADA/IoT where needed
• Model monitoring (drift, bias, false positives)
• Integration back into CMMS/ERP so actions actually happen

If AI outputs don’t trigger work orders, dispatch actions, or operator prompts, the value won’t land.

4) Put AI governance in the same room as operations

Answer first: Governance is what turns AI from a demo into a production tool.

Minimum governance that works in industrial environments:

• Model approval workflow (who signs off and why)
• Audit trails (inputs, outputs, versions)
• Role-based access to sensitive operational data
• Incident process for wrong recommendations

This sounds bureaucratic until the first time an automated decision causes a billing error or an unsafe maintenance prioritization.

People Also Ask: practical questions Kazakhstan teams raise

“Do we need our own ‘Kraken’ platform?”

Not necessarily. Many companies win by combining strong commercial platforms (SCADA historians, CMMS, ERP, data platforms) with targeted AI services and tight integration. Building a full platform is expensive; building repeatable AI capabilities is realistic.

“Where does Generative AI fit—beyond chat?”

The best GenAI applications in energy are document-heavy and compliance-heavy:

• Converting inspection reports into structured findings
• Drafting and validating permits, procedures, and shift handover summaries
• Searching engineering standards and incident databases with citations

GenAI becomes valuable when it’s grounded in trusted internal content and has guardrails.

“What’s the first KPI to track?”

Pick one KPI that finance trusts and operations can influence, such as:

• Unplanned downtime hours per month
• Maintenance backlog aging
• Distribution losses percentage
• Billing exception rate

Then tie AI performance to that KPI, not to “model accuracy.”

What Kraken’s London decision signals for 2026 budgets in Kazakhstan

If Kraken can command IPO-scale attention, it reinforces one clear trend: energy is being re-rated as a software-and-data business as much as an infrastructure business. That changes how boards should allocate digital budgets in 2026.

For Kazakhstan’s energy and oil‑gas companies, the opportunity is concrete: AI can reduce downtime, improve safety performance, cut losses, and accelerate decision cycles. But it only shows up in results when you build for scale—clean data, tight integration, and governance that operations respects.

If you’re planning your 2026 initiatives now, ask yourself: Which three operational decisions should we make faster and better with AI by mid-year—and what data would make that possible?