2026 Trend Alert Across Industries: Decision Systems Beat Technology

Cloud, integration, automation, and analytics are required to keep management quality and change velocity on track, but deploying tools doesn’t create advantage by itself. Most companies already have plenty of data; the issue is turning fragmented signals into action.

Executive Snapshot

Technology is table stakes. Decisions win. Decision Intelligence turns data into repeatable, day-to-day decision-making at scale across teams, not just in pockets of excellence.

In practice, the blockers are familiar: data fragmentation, organizational silos, legacy stacks, and slow-moving processes shaped by caution, regulation, and operational risk. The balance varies by industry: in some sectors governance, auditability, and security are non-negotiable; in others speed and time-to-market dominate. Either way, technology is a lever, not the answer. Advantage comes from consistently translating data into better decisions in day-to-day operations.

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Energy: Decision-making in a more dynamic system

Energy used to run on predictability – stable demand, planned generation, long investment cycles. Today it’s a high-frequency decision environment driven by decentralized renewables, local grid constraints, short-term market volatility, and shifting regulation with real cost and reliability impact when decisions are late or wrong.

Dispatchers and grid operators now decide in minutes: manage load, switch flows, activate flexibility. Decision cycles are shrinking; data volumes are rising, yet insight is still fragmented across systems.

Example from the field:

“At 17:30, a local overload appears, short-term prices spike, and the control room must react, often without knowing whether it’s a one-off fluctuation or the start of a sustained shift.”

Many organizations have plenty of data on grid status, generation, and consumption, but struggle to connect it to the context that explains what’s happening – customer behavior, industrial patterns, and regional specifics. The result is reactive operations, with limited ability to anticipate and manage situations systematically.

What to focus on:

Bring operational signals and consumption patterns into one decision framework so teams can assess impact in real time, activate available flexibility, and learn from outcomes. This is the shift from ad-hoc dispatching to a structured decisioning system, designed for uncertainty as the default.

• Decisions: flexibility management (load shifting), redispatch, resource allocation, regional congestion mitigation

• Signals: grid operations data + consumption patterns + local context (weather, events, tariff behavior)

• Closed loop: real-time decision → action (flex activation/control) → impact measurement (stability, cost, response)

See how Trask builds real-time flexibility decisioning from grid signals to measurable impact.  

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Telco: Stability is the new battleground

Telecom is technologically mature: networks are fast, available, and stable, and customer-perceived differences between operators keep shrinking. But stability is non-negotiable; it’s critical for emergency calls, not just customer convenience. And the mix is shifting – voice, data connectivity, and IoT demand always-on availability. Architecture also keeps getting more decomposed and dynamic, raising integration, security complexity, and pressure on efficiency.

Example from the field:

“A customer doesn’t churn with a cancellation. They churn earlier, when the network quietly degrades rather than goes down. Usage shifts and micro-frustrations build up, and customers feel it before dashboards do. Telco often sees it only when it’s already too late.”

Operators are under pressure to cut network run costs, improve stability, and prevent customers from noticing incidents. That’s why the operating model is moving toward autonomous, self-healing networks: rapid detection and response, proactive prediction, and automation, not more rules.

Telcos sit on massive volumes of network and operational data, yet many still miss early shifts in customer behavior, expectations, and churn risk.

The problem isn’t data scarcity, it’s fragmentation. Network, marketing, care, and product optimize within their own worlds, with separate metrics and decision logic. The result is inconsistent actions and local wins that don’t translate into system-level outcomes.

What to focus on:

Unify customer decisioning across the lifecycle, from acquisition to retention, so the business can detect change early and respond consistently across channels. Value doesn’t come from one-off campaigns; it comes from a repeatable, end-to-end decisioning system. Done well, it protects availability where it matters most (including emergency calling), reduces avoidable support load through automation, and safeguards experience and churn.

• Decisions: churn prevention, offer/next best action, experience quality prioritization

• Signals: network KPIs + digital behavior + frustration signals (care, complaints, usage shifts) + operational events that indicate deteriorating service

• Closed loop: unified decisioning across channels → consistent response → measurement: churn, NPS/CSAT, cost-to-serve

See how Trask unifies churn and experience decisioning across network, care, and digital channels.  

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Insurance: When risk signals live outside core policy systems

Insurance is often seen as a data-mature sector: strong models, deep history, and decades of underwriting and pricing expertise. The shift is that the most valuable risk and customer-need signals increasingly sit outside core policy systems, long before a claim is filed, or a contract is changed. That creates both an opportunity (earlier action) and a constraint (higher expectations on fairness and explainability).

Example from the field:

“Risk doesn’t show up in a claim – it shows up in subtle behavior changes. And insurers often learn about it only in hindsight, after something has already happened.”

Risk evolves in everyday behavior – movement patterns, financial behavior, changes in routine. At the same time, regulators and customers are raising the bar on fairness, transparency, and explain ability, tightening the requirements on which data can be used and how decisions must be justified.

For example, Trask helps insurers enrich their decision context with external signals via partnerships with Mastercard, Škoda and Credolab, supporting use-cases from improving risk models to understanding individual customer patterns and preferences, while keeping decisions auditable and explainable.

What to focus on:

Extend the decision context beyond internal insurance data while strengthening governance and explain ability. The future isn’t more aggressive about scoring, it’s the ability to decide earlier, fairly, and clearly, in a tightly regulated environment.  

• Decisions: context-aware underwriting/pricing, loss prevention, proactive prevention and impact reduction of losses

• Signals: internal insurance data + behavioral signals from transactional data + telematics / device-generated behavioral signals + governance & explain ability constraints

• Closed loop: explainable decision → action/product change → measurement: fairness + loss ratio + retention

Discover how Trask enriches underwriting with external signals while keeping decisions auditable and explainable.  

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Finance: The right decision, but too late

Banks and financial institutions have strong data platforms and sophisticated models. The issue today is often not accurate, but speed and timing. Many decisions are still made in periodic cycles, while customer behavior and risk evolve continuously, and delays quickly turn into losses.

Example from the field:

“Typically: the fraud model is right, just 20 minutes too late, when the transaction is already gone and the ‘right’ decision becomes expensive.”

This pattern shows credit risk, fraud, and personalization. Models work, but response times lag. In volatile conditions, even the correct decision becomes the wrong one if it arrives too late.

What to focus on:

Move from batch decisioning to a continuous, operationally owned process where data, analytics, and regulatory constraints sit in one framework. It’s not about “more AI”, it’s about making decisioning part of daily banking operations.

• Decisions: fraud intervention, credit limits, personalization/next best offer, risk early warning

• Signals: transaction flow + device/behavior + external risk signals (where relevant)

• Closed loop: batch → flow → operational ownership → measurement: decision-to-action time, fraud loss, approval rate, compliance

Learn how Trask shifts fraud and risk decisioning from batch cycles to operational, real-time flows.

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Manufacturing: When optimization meets the real world

Manufacturers have long relied on efficiency, standardization, and process optimization. But that approach assumes a stable environment, and that is no longer the case. Supply chains are fragile, input availability fluctuates, and demand often changes faster than production plans can keep up.

Example from practice:

“Planning across production, procurement, and sales used to be an optimized, forward-looking process that enabled us to deliver manufacturing and customer requirements with ideal lead times. Now one supplier drops out, demand shifts under our hands, and the optimum collapses completely within a short time. And that costs us money and customer trust!”

Internal data used to secure production is still often inaccurate and lacks granularity. On top of that, it is isolated from external contexts such as market dynamics, geopolitics, or supplier conditions. This limits the ability to respond in time and increases the risk of strategic mistakes.

What to focus on:

Expand decision-making with external signals and connect strategy with operations. Resilience is becoming the key manufacturing capability, the ability to respond to change without losing control over performance, quality, and cost. Understanding the broader context, in other words, contextualizing data supported by AI, is turning into a major competitive advantage.

• Decisions: production planning, inventory balancing, resource and capacity allocation, supplier management

• Signals: internal production + supplier risk + market demand signals + regulatory requirements + geopolitical factors

• Closed loop: connect strategy and operations (including R&D) → scenarios → measurement: OTIF, cost, quality, Overall Equipment Efficiency (OEE), customer satisfaction

Discover how Trask links strategy and shop floor execution through closed loop decisioning and measurable outcomes.

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Automotive: Value is moving into the lifecycle

Automotive is undergoing a transformation that is changing the very nature of the industry. E-mobility, software-defined vehicles, and connectivity are shifting the car from a one-off product into a long-term service.  

European automakers also face competition from Asian brands that manufacture at lower costs, under fewer regulations, bring new models to market faster, and are building manufacturing bases directly within the European Union.

Example from practice:

“The vehicle itself, in principle, provides enough data, but the subsequent customer relationship gets fragmented across the manufacturer, service, leasing, insurance, and applications (apps), even though a key benefit for the automaker, the dealer, and the end customer is to view the entire lifecycle as one story, leading to customer satisfaction and optimization of production and vehicle sales costs.”

Vehicles generate enormous amounts of data throughout their lifecycle, but this data is often used in isolation. Without connecting data from operations, service, finance, insurance, and digital services, the value remains fragmented. The customer relationship then breaks apart among different players in the ecosystem.

What to focus on:

Create an end-to-end view of the vehicle and customer lifecycle and make decisions over it continuously. The future of automotive will not be about a better product, but about the ability to manage the customer relationship long-term using data.

• Decisions: service interventions, service monetisation, retention across the lifecycle, cross-sell in the ecosystem

• Signals: telemetry + service + finance/leasing + digital services + insurance (where relevant)

• Closed loop: end-to-end view of the customer/vehicle → consistent offer → measurement: ARPU, retention, lifetime value

Learn how Trask turns vehicle and customer data into end-to-end retention and monetization decisions.

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The real differentiator: Decision systems

Across industries, the leaders are shifting focus from building capabilities to running them. The question is no longer “Do we have the data and technology?” but “Can we operationalize them into decisions that move the business?” The companies that pull ahead are the ones that industrialize decision-making: clear ownership, consistent logic, and a feedback loop that improves outcomes over time.

That’s the difference between delivering projects and building momentum. Trask has been investing in this approach for years and in today’s environment, it’s what turns complexity into execution speed.

Make decision-to-action time your next competitive metric for your business. Let’s talk →