Accedia AI Delivery Report 2026
This report draws on Accedia’s AI Capability Center delivery data from 2020 to mid-2026. It examines where AI is used, how engagements evolve, and what they deliver, covering common use cases, legacy-system AI, repeat work, pricing, governance, and verified outcomes.
6
Years of AI Delivery Data
12+
Sectors Covered
7+
Countries Served
Six Key Findings From Accedia's AI Delivery
An Agentic Pipeline Cut a 6-month Project to 10 Days
Twenty user stories passed automated quality checks, security scanning, and human review before merging, with no reduction in the standards applied to hand-written code.
AI-freed Time Go Back Into Client's Next Release
On the 70% of Accedia projects using AI tooling, cycle time has dropped 20 to 40% and defect escape 25 to 35%, with those gains going into the next MVP.
AI Delivers Gains in Risk, Retention, and Speed
Across industries, Accedia’s AI work has reduced fraud incidents by 35%, operational losses by 30%, and customer churn by 80%, while making delivery up to 14x faster.
AI Engagements Compound
63% of documented AI projects came from clients who had already worked with Accedia, each moving into a different AI category than the one they started with.
AI Now Runs in 30% of Development Projects
Almost all of it goes into legacy systems, where AI reads existing codebases, supports refactoring, and guides migration off older technology.
AI Governance is Assessed During Procurement
Security due diligence decides which suppliers reach the shortlist. Certification and validation controls are assessed before engineering quality.
What Six Years of AI Delivery Produced
4-8 weeks
Working MVP Delivery
Production-quality release from Accedia's AI-Augmented Delivery model.
35%
Faster Deployment
Measured through Accedia's AI Development Accelerator.
5 years
Length of AI Client Relationships
AI engagements typically run for five years or longer.
80%
Fewer False Positives
Result from the AI Fraud Detector proof of concept.
See What the Delivery Data Shows
If you are trying to work out what AI costs, how long it takes, and what it takes to run, this report draws on six years of Accedia’s AI delivery work. It also looks at how these projects are scoped, priced, and staffed.
Eight Common AI Use Cases in the Report
Fraud and Anomaly Detection
-Scores transactions by probability to catch the patterns fixed rules miss, running alongside the rule-based system already in production rather than replacing it.
Computer Vision Inspection
-Assesses damage type, failure mode, or image quality from photographs. Work that previously required a trained specialist on site. Cloud deployment standardizes it across locations.
Predictive Maintenance and Forecasting
-Predicts equipment failure or a demand peak before it happens, scored against a cost the client already measures, such as penalties for missed delivery commitments.
Retrieval-augmented Generation (RAG)
-Makes years of accumulated internal material answerable in plain language, so finding an answer doesn't depend on knowing which folder it was filed in.
Intelligent Document Processing
-Reads, classifies, and routes incoming documents without a person opening them, pairing optical character recognition (OCR) with classification models trained on the document set.
Legacy Application Modernization
-Reads an existing codebase to map dependencies and unreachable code. Then guides a phased refactor to microservices, web, and cloud while keeping the application running.
Automated Case Prioritization
-Prioritizes a queue of incoming cases by likely value, adjusting as conditions change and volumes shift, without anyone rewriting the underlying rules.
Recommendation and Personalization
-Serves matches in real time while a second system captures what users actually did and retrains on it, so results keep pace as behavior shifts.
About the author
Peter Ivanov
AI Capability Center Lead at Accedia, with 18 years of experience delivering AI systems across a wide range of industries, from banking and manufacturing to media and the public sector. His work spans machine learning, generative AI, computer vision, retrieval-augmented systems, and agentic AI.
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Everything in this report comes from Accedia's own AI projects. Learn:
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AI Delivery Report FAQs
What is the Accedia AI Delivery Report based on?
The report draws on Accedia's own AI project records from 2020 through mid-2026, covering client engagements and internal AI work across more than a dozen sectors and seven countries. Every figure in it is a proportion of a stated set of projects, so the reader can see what each percentage was measured against.
Does Accedia have a dedicated AI capability center?
Learn moreYes. Accedia's AI Capability Center opened in 2020. Its work covers generative AI and language systems, computer vision, predictive machine learning, agentic systems, AI security, and the data layers underneath all of them. It builds on the Innovation Development Center, which Accedia has run since 2017.
How does Accedia reduce AI hallucination risk in production?
Accedia has measured a 15% to 30% reduction in hallucination rates on generative AI systems in financial services and automotive engagements. Model answers are grounded in retrieved source documents. Where confidence falls below a set threshold, the system returns no answer. Those cases go to human review before reaching the user.
What AI governance standards does Accedia work to?
Accedia is certified to ISO 27001 for information security and TISAX for automotive supply chain requirements. Certification to ISO 42001, the AI management systems standard, is in progress, and few IT consultancies globally are pursuing it at this stage. Accedia works across financial services, manufacturing, media, energy, retail, and the public sector, and the requirements set by the most heavily regulated of those shape the controls applied everywhere.