Duke Energy Technology Investment Impact Report

Prepared by Naftiko March 2026

Executive Summary

This report presents a comprehensive analysis of Duke Energy’s technology investment posture, derived from Naftiko’s signal-based methodology. By examining the services deployed, tools adopted, concepts discussed, and standards followed across Duke Energy’s technology workforce, the analysis produces a multidimensional portrait of the company’s technology commitment across infrastructure, data platforms, operational systems, and governance frameworks.

Duke Energy’s technology profile reveals a major utility company with substantial and growing technology investment across cloud, data analytics, security, and operational monitoring. The highest scoring area is Services at 116, followed by Data at 50, Cloud at 45, Operations at 32, and Security at 31. Duke Energy’s defining characteristics are its strong data analytics platform built on Power BI, MATLAB, and Qlik; its multi-cloud infrastructure spanning Amazon Web Services, Microsoft Azure, and Google Cloud Platform; and its deep security and governance investment reflecting the critical infrastructure protection requirements of the energy sector. Duke Energy demonstrates the technology maturity expected of a regulated utility managing essential infrastructure across multiple states.

Layer 1: Foundational Layer

Evaluating Duke Energy’s foundational capabilities across Artificial Intelligence, Cloud, Open-Source, Languages, and Code.

Cloud leads at 45, with Languages at 22, AI at 20, Open-Source at 19, and Code at 14. This is a mature foundational layer for a utility company, reflecting significant technology modernization.

Artificial Intelligence — Score: 20

Hugging Face, Azure Databricks, and Azure Machine Learning anchor the AI services, supported by Pandas, NumPy, TensorFlow, Kubeflow, Matplotlib, Hugging Face Transformers, and Semantic Kernel. AI concepts include agents, model development, deep learning, and computer vision — indicating exploration of AI for grid management, predictive maintenance, and operational optimization.

Cloud — Score: 45

Amazon Web Services, Microsoft Azure, and Google Cloud Platform with CloudFormation, Azure Active Directory, Azure Functions, Oracle Cloud, Red Hat, Azure Databricks, Azure Kubernetes Service, Azure Machine Learning, Azure DevOps, Google Apps Script, Red Hat Ansible Automation Platform, Azure Log Analytics, and Google Cloud represent deep multi-cloud penetration. Terraform, Kubernetes Operators, and Buildpacks automate infrastructure.

Cloud environment and cloud technologies concepts confirm active cloud transformation for a company traditionally reliant on on-premise infrastructure.

Relevant Waves: Large Language Models (LLMs), Generative Pre-trained Transformer (GPT), Open-Source LLMs

Key Takeaway: Duke Energy’s Cloud score of 45 across three major providers signals an aggressive cloud transformation strategy for a regulated utility.

Open-Source — Score: 19

GitHub, GitLab, Red Hat, and Red Hat Ansible Automation Platform with Apache Spark, Apache Kafka, Terraform, Spring, PostgreSQL, Prometheus, Elasticsearch, React, and Apache NiFi. Community standards (CONTRIBUTING.md, LICENSE.md, CODE_OF_CONDUCT.md) suggest open-source participation.

Languages — Score: 22

Go, Perl, React, Rust, SQL, Scala, and XML compose a modern language portfolio with systems programming capabilities.

Code — Score: 14

GitHub, GitLab, Azure DevOps, IntelliJ IDEA, and TeamCity with Git, PowerShell, SonarQube, and Vitess.

Layer 2: Retrieval & Grounding

Evaluating Duke Energy’s data retrieval capabilities across Data, Databases, Virtualization, Specifications, and Context Engineering.

Data dominates at 50, with Databases at 14.

Data — Score: 50

Power BI, Power Query, MATLAB, Teradata, Azure Databricks, QlikSense, Qlik Sense, and Crystal Reports compose a deep data services portfolio. Apache Spark, Apache Kafka, PostgreSQL, Prometheus, Apache Cassandra, and 30+ additional tools provide the data engineering infrastructure. Data-driven decision making, data sciences, business intelligence, data management, and enterprise data concepts confirm a mature data-driven culture.

Key Takeaway: Duke Energy’s Data score of 50 with MATLAB, Power BI, and Qlik reflects sophisticated analytical capabilities for grid optimization, demand forecasting, and asset management.

Databases — Score: 14

SQL Server, Teradata, SAP BW, Oracle Integration, and Oracle Enterprise Manager with PostgreSQL, Apache Cassandra, Elasticsearch, and ClickHouse.

Virtualization — Score: 4

Spring, Spring Boot, Spring Framework, and Kubernetes Operators with virtualization concepts.

Specifications — Score: 3

REST, HTTP, WebSockets, HTTP/2, TCP/IP, XML, OpenAPI, and Protocol Buffers.

Context Engineering — Score: 0

No recorded signals.

Relevant Waves: Vector Databases, Retrieval-Augmented Generation (RAG), Prompt Engineering, Context Engineering

Layer 3: Customization & Adaptation

Evaluating Duke Energy’s AI customization capabilities.

Multimodal Infrastructure leads at 5, Model Registry & Versioning at 4. Early-stage AI customization with utility-relevant infrastructure.

Data Pipelines — Score: 2

Apache Spark, Apache Kafka, Apache DolphinScheduler, and Apache NiFi with data flow concepts.

Model Registry & Versioning — Score: 4

Azure Databricks and Azure Machine Learning with TensorFlow and Kubeflow.

Multimodal Infrastructure — Score: 5

Hugging Face and Azure Machine Learning with TensorFlow and Semantic Kernel.

Domain Specialization — Score: 0

No recorded signals.

Layer 4: Efficiency & Specialization

Evaluating Duke Energy’s operational efficiency.

Operations leads at 32, Automation at 25, Platform at 24.

Automation — Score: 25

Microsoft PowerPoint, Ansible Automation Platform, Microsoft Power Automate, Red Hat Ansible Automation Platform, and Make with Terraform and PowerShell. RPA and building automation concepts are notable for a utility company managing physical infrastructure.

Containers — Score: 9

Kubernetes Operators and Buildpacks indicate emerging container adoption.

Platform — Score: 24

Salesforce, Amazon Web Services, Microsoft Azure, Google Cloud Platform, Workday, Oracle Cloud, Salesforce Lightning, and Salesforce Automation.

Operations — Score: 32

Datadog, New Relic, Dynatrace, and SolarWinds with Terraform and Prometheus. Incident response, incident management, system operations, and operational excellence concepts reflect the criticality of operational monitoring for energy infrastructure.

Relevant Waves: Small Language Models (SLMs), Model Routing / Orchestration, Reasoning Models

Key Takeaway: Duke Energy’s Operations score of 32 reflects the monitoring maturity essential for managing critical energy infrastructure.

Layer 5: Productivity

Evaluating Duke Energy’s productivity capabilities.

Services leads at 116.

Software As A Service (SaaS) — Score: 0

SaaS platforms captured within Services.

Code — Score: 14

Mirrors the Foundational Layer.

Services — Score: 116

Over 110 services spanning enterprise operations (ServiceNow, Salesforce, Workday, SAP), cloud (AWS, Azure, GCP), data (Power BI, MATLAB, Qlik), collaboration (Microsoft Teams, Confluence, Microsoft Office), security (Cloudflare, Palo Alto Networks), and analytics (Google Analytics, Adobe Analytics). Utility-specific tools and integration platforms (Conductor, Harness, Merge, Vessel) indicate modern technology procurement practices.

Relevant Waves: Coding Assistants, Copilots

Layer 6: Integration & Interoperability

Evaluating Duke Energy’s integration capabilities.

CNCF leads at 12, Integrations at 9.

API — Score: 6

Application Programming Interfaces and Working Capitals concepts with REST, HTTP, and OpenAPI.

Integrations — Score: 9

Oracle Integration, Conductor, Harness, Merge, and Vessel reflect modern integration platform adoption.

Event-Driven — Score: 2

Apache Kafka and Apache NiFi with Event Sourcing.

Patterns — Score: 6

Spring, Spring Boot, and Spring Framework with Dependency Injection and Reactive Programming.

Specifications — Score: 3

REST, HTTP, WebSockets, HTTP/2, TCP/IP, XML, OpenAPI, and Protocol Buffers.

Apache — Score: 1

Apache Spark, Apache Kafka, Apache Hadoop, Apache Cassandra, and 20+ additional Apache tools.

CNCF — Score: 12

Prometheus, Score, Dex, OpenTelemetry, Buildpacks, Pixie, and Vitess represent meaningful cloud-native adoption.

Relevant Waves: MCP (Model Context Protocol), Agents, Skills

Layer 7: Statefulness

Evaluating Duke Energy’s statefulness capabilities.

Data leads at 50, Security at 31, Observability at 23, Governance at 15.

Observability — Score: 23

Datadog, New Relic, Dynatrace, SolarWinds, and Azure Log Analytics with Prometheus, Elasticsearch, and OpenTelemetry.

Governance — Score: 15

Compliance, governance, risk management, regulatory compliance, internal audits, internal controls, compliance frameworks, and policy enforcement concepts with NIST, ISO, RACI, OSHA, CCPA, and GDPR standards. This comprehensive governance framework reflects the regulatory requirements of a major utility.

Security — Score: 31

Cloudflare and Palo Alto Networks with Consul and extensive security concepts (incident response, security controls, vulnerability management, security engineering, threat modeling, security information and event management) with NIST, ISO, OSHA, CCPA, PCI Compliance, GDPR, IAM, SSO, and security standards. The depth of security concepts is exceptional, reflecting critical infrastructure protection requirements.

Key Takeaway: Duke Energy’s Security score of 31 with 20+ security concepts and 14+ security standards reflects the cyber defense maturity required for protecting national energy infrastructure.

Data — Score: 50

Mirrors the Retrieval & Grounding assessment.

Relevant Waves: Memory Systems

Layer 8: Measurement & Accountability

Evaluating Duke Energy’s measurement capabilities.

ROI & Business Metrics leads at 28, Observability at 23.

Testing & Quality — Score: 2

SonarQube with quality assurance, performance testing, and security testing concepts.

Observability — Score: 23

Mirrors the Statefulness layer.

Developer Experience — Score: 10

GitHub, GitLab, Azure DevOps, Pluralsight, and IntelliJ IDEA with Git.

ROI & Business Metrics — Score: 28

Power BI and Crystal Reports with extensive financial concepts including budgeting, business planning, cost controls, financial accounting, financial analysis, financial management, financial planning, financial reporting, forecasting, and performance metrics. This depth reflects the financial rigor of a regulated utility managing large capital investment programs.

Relevant Waves: Evaluation & Benchmarking

Layer 9: Governance & Risk

Evaluating Duke Energy’s governance and risk capabilities.

Security leads at 31, Governance at 15, Regulatory Posture at 9.

Regulatory Posture — Score: 9

Compliance, regulatory compliance, compliance frameworks, regulatory filings, and financial compliance concepts with NIST, ISO, HIPAA, OSHA, CCPA, Internal Control Standards, Cybersecurity Standards, PCI Compliance, and GDPR. The HIPAA inclusion alongside energy-sector standards reflects Duke Energy’s comprehensive regulatory posture.

AI Review & Approval — Score: 6

Azure Machine Learning with TensorFlow and Kubeflow and model development concepts.

Security — Score: 31

Mirrors the Statefulness layer with comprehensive security infrastructure.

Governance — Score: 15

Mirrors the Statefulness layer governance assessment.

Privacy & Data Rights — Score: 5

HIPAA, CCPA, and GDPR standards indicate emerging privacy framework investment.

Relevant Waves: Governance & Compliance

Layer 10: Economics & Sustainability

Evaluating Duke Energy’s economic sustainability.

Partnerships & Ecosystem leads at 12.

AI FinOps — Score: 4

AWS, Azure, and GCP with budgeting and financial planning concepts.

Provider Strategy — Score: 6

Salesforce, Microsoft (10+ products), Amazon Web Services, Google Cloud Platform, SAP, and Oracle ecosystems.

Partnerships & Ecosystem — Score: 12

Broad technology partnership network.

Talent & Organizational Design — Score: 6

LinkedIn, Workday, PeopleSoft, and Pluralsight with talent acquisition, learning and development, and organizational design concepts.

Data Centers — Score: 0

No recorded signals.

Layer 11: Storytelling & Entertainment & Theater

Evaluating Duke Energy’s strategic alignment capabilities.

Alignment leads at 20, M&A at 12.

Alignment — Score: 20

Architecture, digital transformation, security architecture, enterprise architecture, and strategic planning concepts with Agile, SAFe Agile, Lean Management, Lean Manufacturing, and Scaled Agile standards. The enterprise architecture and strategic planning concepts reflect utility-scale technology governance.

Standardization — Score: 7

NIST, ISO, REST, SQL, Standard Operating Procedures, and Technical Specifications.

Mergers & Acquisitions — Score: 12

Due diligence, data acquisition, and talent acquisition concepts.

Experimentation & Prototyping — Score: 0

No recorded signals.

Relevant Waves: Moltbook, Gastown, Ralph Wiggum, OpenClaw / Clawdbot, Artificial General Intelligence (AGI)

Strategic Assessment

Duke Energy’s technology investment profile reveals a major utility company with substantial and growing technology capabilities. The company’s strongest signals — Services (116), Data (50), Cloud (45), Operations (32), Security (31) — form a coherent pattern of an organization investing in modern technology infrastructure while maintaining the governance and security posture required for critical energy infrastructure. The multi-cloud deployment across AWS, Azure, and GCP is notable for a regulated utility, as is the depth of data analytics investment with MATLAB, Power BI, and Qlik. Duke Energy’s technology profile suggests an energy company in active digital transformation.

Strengths

Duke Energy’s strengths reflect the technology capabilities of a forward-leaning utility company managing critical infrastructure.

Area Evidence
Data Analytics Platform Data score of 50 with Power BI, MATLAB, Qlik, Apache Spark, and Apache Kafka
Multi-Cloud Infrastructure Cloud score of 45 across AWS, Azure, and GCP
Operational Monitoring Operations score of 32 with Datadog, New Relic, Dynatrace, and SolarWinds
Security & Compliance Security score of 31 with 20+ security concepts and 14+ standards including NIST and NERC
Governance Framework Governance score of 15 with comprehensive regulatory compliance and internal controls
Enterprise Services Services score of 116 covering all enterprise technology dimensions

The most significant pattern is the convergence of data analytics, operational monitoring, and security — the three pillars of modern utility technology management. Duke Energy’s ability to analyze grid performance data, monitor operational health, and protect critical infrastructure creates the foundation for the AI-powered grid management that will define next-generation utility operations.

Growth Opportunities

Area Current State Opportunity
AI for Grid Management Score: 20 Deploying ML models for predictive maintenance, demand forecasting, and renewable integration
Context Engineering Score: 0 Building AI systems that understand grid topology and operational context for intelligent automation
Data Pipelines Score: 2 Scaling real-time streaming from grid sensors to analytics platforms using existing Kafka and Spark
Containers Score: 9 Modernizing utility application deployment with Kubernetes
Domain Specialization Score: 0 Developing energy-sector-specific AI models for generation, transmission, and distribution

The highest-leverage opportunity is AI for Grid Management. Duke Energy’s existing data analytics platform (MATLAB, Power BI, Apache Spark), AI tooling (TensorFlow, Kubeflow, Azure ML), and operational monitoring infrastructure provide the technical foundation for predictive maintenance and demand forecasting models. The company’s renewable energy transition creates additional urgency for AI-optimized grid management.

Wave Alignment

The most consequential wave alignment for Duke Energy is SLMs combined with Agents. Small language models deployable at the edge could enable intelligent monitoring and automated response at individual substations and generation facilities. The existing OpenTelemetry, Prometheus, and Datadog observability stack provides the data stream, while Apache Kafka and Spark deliver the processing pipeline these agents would need.

Methodology

This impact report is generated from Naftiko’s signal-based investment analysis framework. Scores are derived from the density and diversity of technology signals detected across four dimensions:

Each signal is scored and aggregated within strategic layers that map the full technology stack from foundational infrastructure through productivity and governance. Higher scores indicate greater investment depth and breadth within a given dimension.

This report is based on signal data available as of March 2026. Investment signals are dynamic and may change as Duke Energy’s technology strategy evolves. For questions about methodology or to request an updated analysis, contact Naftiko.