The Complete Guide to AI Transcription for Charities and Social Workers

AI transcription — the use of artificial intelligence to convert spoken words into written text — has matured rapidly in recent years. What was once expensive, slow, and error-prone is now affordable, near-instant, and remarkably accurate. For charities and social workers, this technology addresses one of the most persistent challenges in the sector: the time consumed by documentation. This guide covers how AI transcription works, where it applies in charity settings, and how to implement it effectively.

TL;DR: AI transcription converts speech to text with 90–95% accuracy, processes audio in near real time, and costs a fraction of human transcription. For charities and social workers, the most impactful application is AI case notes — recording support conversations and generating structured documentation automatically, saving 50–70% of documentation time and improving record quality.

What you'll learn: How AI transcription technology works, its current capabilities and limitations, and practical applications across charity operations.

Why it matters: Charities spend a substantial proportion of front-line staff time on administration — BASW found social workers spend roughly 64% of their week on computer-based and paperwork tasks, with documentation being the largest single component. AI transcription directly reduces this burden.

Practical focus: Guidance for evaluating, implementing, and getting the most from AI transcription in your organisation.

Who this is for: IT leads, operations managers, and charity directors evaluating transcription technology for case work or meetings.

How AI Transcription Works

Understanding the technology helps you set realistic expectations and make informed implementation decisions.

Speech Recognition Fundamentals

AI transcription uses automatic speech recognition (ASR) to convert audio signals into text. Modern ASR systems use deep learning models — specifically, neural networks trained on vast quantities of speech data — to recognise spoken words.

Acoustic Model: Converts raw audio (sound waves) into phonetic representations, identifying the speech sounds present in the audio.

Language Model: Determines the most likely sequence of words given the phonetic representations, using knowledge of language structure and common word sequences to improve accuracy.

End-to-End Models: The most advanced systems use end-to-end models that combine acoustic and language processing in a single neural network, achieving higher accuracy than traditional two-stage approaches.

Training Data: Accuracy depends heavily on the quality and diversity of training data. Models trained on diverse accents, speaking styles, and vocabulary perform better across different contexts.

The technology has improved dramatically since 2020, with word error rates falling by approximately 50% over five years due to advances in transformer-based models.

Real-Time vs Batch Processing

AI transcription can operate in two modes, each suited to different use cases.

Real-Time Transcription: Audio is processed as it is spoken, with text appearing within 1–3 seconds of speech. This is ideal for live captioning, real-time note generation, and situations where the case worker wants to see the transcription building during the conversation.

Batch Processing: A complete audio recording is processed after the conversation ends. This can achieve slightly higher accuracy because the model can use the full context of the conversation. Processing time is typically 10–30% of the recording length — a 30-minute recording processes in 3–10 minutes.

Hybrid Approach: Some systems offer real-time transcription during the conversation followed by a batch re-processing pass that corrects errors and improves formatting. This provides immediate visibility with improved final accuracy.

For AI case notes, batch processing after the conversation is usually preferable because it produces higher accuracy and allows the case worker to focus fully on the individual during the conversation.

Speaker Diarisation

Identifying who said what — speaker diarisation — is essential for producing useful case notes from recorded conversations.

How It Works: The AI analyses voice characteristics (pitch, tone, speaking pace) to distinguish between speakers and label each segment of speech with a speaker identity.

Accuracy: Modern diarisation systems correctly identify speakers in approximately 85–92% of segments, with accuracy decreasing when speakers have similar voice characteristics or speak simultaneously.

Practical Impact: In case notes, diarisation enables the AI to attribute statements correctly — distinguishing what the case worker said from what the individual said. This is important for accurate records and for extracting actions and concerns.

Speaker identification transforms a transcription from a wall of undifferentiated text into a structured conversation record.

Current Accuracy Levels

AI transcription accuracy is typically measured by Word Error Rate (WER) — the percentage of words that are incorrectly transcribed.

Improving Over Time: Accuracy continues to improve as models are trained on more diverse data. Some providers offer custom models that can be fine-tuned to specific vocabulary, accents, or contexts.

Compared to Human Transcription: Professional human transcribers typically achieve 95–99% accuracy but cost £1–£2 per minute of audio and have turnaround times of hours to days. AI transcription achieves 90–95% accuracy at a fraction of the cost and near-instantly.

For case documentation, 90–95% accuracy with human review produces excellent results — typically better than manual notes written from memory.

Applications in Charity Settings

AI transcription has applications beyond case notes, though case documentation is the highest-impact use case.

Case Notes and Support Documentation

The primary application for charities is transforming recorded conversations into structured case notes.

How It Works: The case worker records a support conversation (with consent), AI transcribes it and generates structured notes with speaker identification, concern flagging, and action extraction.

Impact: Reduces documentation time by 50–70% per conversation, improving both the volume and quality of case records.

Adoption: This is the fastest-growing application of AI transcription in the UK charity sector, with organisations reporting significant improvements in staff satisfaction and documentation quality.

Case notes are the "killer application" for AI transcription in charities — the use case where the technology delivers the most significant and immediate value.

Meeting Minutes and Records

AI transcription can capture and structure meeting records automatically.

Board Meetings: Record board meetings and generate minutes, action items, and decision logs. This is particularly valuable for small charities where the burden of minute-taking falls on already stretched staff.

Team Meetings: Generate records of team meetings, supervision sessions, and case conferences that can be shared and searched.

Multi-Stakeholder Meetings: Record meetings with external partners, funders, or local authorities, creating shared records that reduce disagreement about what was discussed.

Time Saving: Meeting minutes that previously took 1–2 hours to write can be generated in minutes.

Meeting transcription saves less total time than case note transcription (because meetings are less frequent) but eliminates a task that is consistently disliked and often delayed.

Accessibility and Inclusion

AI transcription supports accessibility for people who are deaf or hard of hearing, and for those who process written information more effectively than spoken.

Real-Time Captions: Provide live captions during meetings, events, or one-to-one conversations to support deaf and hard-of-hearing participants.

Written Records: People who find it difficult to process verbal information (including some neurodivergent individuals) benefit from having written records of conversations.

Language Support: Some transcription services include real-time translation, supporting engagement with individuals who speak languages other than English.

Accessibility is a statutory obligation for many organisations and a moral imperative for all. AI transcription makes compliance easier and cheaper.

Research and Evaluation

Charities conducting qualitative research or programme evaluations can use AI transcription to accelerate data collection.

Interview Transcription: Transcribe research interviews rapidly rather than paying for professional transcription or spending hours transcribing manually.

Focus Groups: Record and transcribe focus groups, with speaker diarisation identifying different participants.

Thematic Analysis: Searchable transcriptions enable faster identification of themes and patterns across multiple interviews.

Cost Reduction: Research transcription costs are reduced by 80–90% compared with professional human transcription services.

For charities conducting their own evaluations (which funders increasingly expect), AI transcription removes a significant barrier to qualitative data collection.

Training and Knowledge Management

Capture organisational knowledge and training content through transcription.

Training Sessions: Record and transcribe training sessions to create searchable knowledge resources for staff.

Expert Interviews: Capture the knowledge of experienced staff before they leave, creating institutional memory.

Induction Materials: Use transcribed content to create induction resources for new staff.

Knowledge management is often neglected in charities due to time constraints. AI transcription makes it practical to capture and share knowledge at minimal cost.

Choosing AI Transcription Technology

Key Evaluation Criteria

When selecting AI transcription technology for your charity, evaluate against these criteria.

Accuracy: Test with recordings that reflect your real conditions — accents, background noise levels, and vocabulary typical of your work.

Speaker Diarisation: Essential for case notes and meetings. Test with multi-speaker recordings.

Processing Speed: Real-time or near-real-time processing is needed for case notes. Slower batch processing may be acceptable for meeting minutes.

Integration: Does the transcription integrate with your case management system or require manual copy-paste?

Security: Audio recordings contain sensitive data. Ensure processing and storage meet UK GDPR requirements, ideally within UK or EU data centres.

Language Support: If your communities include speakers of languages other than English, check language support and accuracy for relevant languages.

Cost: Pricing models vary — per minute of audio, per user, or bundled with a broader platform. Calculate total cost including integration and administration.

Offline Capability: For community-based work in areas with poor connectivity, the ability to record offline and process later is important.

Standalone vs Integrated

You can use transcription as a standalone service or as part of an integrated platform.

Standalone Transcription: Services like Otter.ai, Rev, or Microsoft's transcription features provide transcription independently. You then need to manually transfer text into your case management or records system.

Integrated Platform: Tools like Plinth integrate transcription directly into case management, so transcribed and structured notes flow automatically into the case record. This eliminates manual transfer and ensures data stays in one place.

Integrated solutions reduce friction, improve adoption, and ensure data is not fragmented across multiple systems. For case documentation, integration is strongly recommended.

Security and Data Protection

AI transcription of conversations involving vulnerable people requires careful attention to data security.

Data Processing Location: Know where audio is processed and stored. UK or EU-based processing is preferable for GDPR compliance.

Encryption: Audio and text should be encrypted in transit (TLS) and at rest (AES-256 or equivalent).

Access Controls: Restrict access to recordings and transcriptions to authorised staff only, using role-based access controls.

Retention: Define how long audio recordings and transcriptions are retained. Apply data minimisation — delete audio recordings once approved notes are saved if the recording is not needed.

Sub-Processors: If the transcription service uses third-party AI providers for processing, these are sub-processors under GDPR. Ensure appropriate data processing agreements are in place.

DPIA: Conduct a data protection impact assessment before implementing AI transcription, particularly for case notes involving vulnerable individuals.

Security should not be an afterthought. Choose providers who can demonstrate robust security practices and GDPR compliance.

Implementation Guide

Phase 1: Preparation (Weeks 1–2)

Policy Development: Create or update policies covering consent, recording, data protection, and staff responsibilities.

DPIA: Complete a data protection impact assessment for AI transcription.

Staff Communication: Explain what is being introduced, why, and how it will affect their work. Address concerns proactively.

Consent Materials: Develop clear, accessible consent forms and verbal consent scripts for individuals being recorded.

Phase 2: Pilot (Weeks 3–6)

Select Pilot Group: Choose 3–5 willing staff members who represent different roles and settings.

Training: Provide practical training on recording technique, consent processes, and reviewing AI output.

Test Conditions: Pilot across a range of real conditions — different environments, conversation types, and individuals.

Collect Feedback: Gather detailed feedback from pilot staff on accuracy, usability, time savings, and any problems.

Phase 3: Evaluate and Adjust (Week 7)

Analyse Results: Review transcription accuracy, time savings, staff satisfaction, and any consent or quality issues.

Adjust Processes: Modify policies, training, or workflows based on pilot findings.

Build the Case: Document pilot results to support organisation-wide rollout, including time savings and quality improvements.

Phase 4: Rollout (Weeks 8–12)

Phased Rollout: Extend to additional teams in phases rather than all at once, allowing support to be focused.

Training: Provide the same practical training to all new users, with pilot staff available as peer mentors.

Ongoing Support: Maintain a support channel for questions and issues, especially in the first month.

Monitor Adoption: Track usage rates and address any teams or individuals who are not engaging with the technology.

Phase 5: Optimise (Ongoing)

Quality Review: Periodically review AI-generated notes for quality, checking for consistent accuracy and identifying any systematic issues.

Feedback Loop: Continue collecting staff feedback and using it to improve processes.

New Applications: Once case notes are established, explore other applications such as meeting minutes, training recording, or research transcription.

A phased approach reduces risk and builds confidence. Most organisations see positive results from the pilot phase that accelerate broader adoption.

Common Questions and Concerns

"What about accents and dialects?"

Modern AI transcription handles most UK accents well, including Scottish, Welsh, Northern English, and London accents. Very strong regional dialects may reduce accuracy by 5–10 percentage points. The human review step catches significant errors. Over time, as AI models are trained on more diverse speech data, accent handling continues to improve.

"What happens if the technology fails?"

Technology failures are rare but possible. If AI transcription is unavailable, case workers revert to manual notes. Organisations should maintain manual note-writing as a fallback capability. Audio recordings can usually be re-processed later if real-time transcription fails.

"Is this legal?"

Recording conversations with informed consent is legal in the UK. The key requirements are: inform all participants that recording is taking place; obtain consent freely (not under pressure); comply with UK GDPR for processing and storing the data; and respect withdrawal of consent immediately. Check whether any specific regulatory requirements apply to your context (e.g., social work regulation, care standards).

"Will staff resist it?"

Some initial resistance is natural with any technology change. The most effective counter is hands-on experience — once staff use AI case notes and experience the time saving, resistance typically evaporates. Research on technology adoption in social care suggests that early adopters become powerful advocates who influence colleagues more effectively than management directives.

Frequently Asked Questions

How much does AI transcription cost for a charity?

Costs vary by provider and usage model. Standalone transcription services typically charge £0.01–£0.05 per minute of audio. Integrated platforms like Plinth include transcription as part of a case management subscription, which is more cost-effective for organisations using it regularly. For a team of 10 case workers transcribing an average of 15 conversations per week (30 minutes each), standalone transcription costs approximately £150–£750 per month, while integrated platform costs include this functionality within the overall subscription.

Can AI transcription handle group conversations?

Yes, but with caveats. Speaker diarisation works well with 2–4 speakers in clear conditions. As the number of speakers increases, accuracy for both transcription and speaker identification decreases. Overlapping speech (multiple people talking simultaneously) is the biggest challenge. For group settings like team meetings, accuracy of 80–90% is typical, which is generally sufficient for meeting minutes but may require more editing for formal records.

What audio quality is needed?

For best results, record in a quiet environment with the recording device (phone or tablet) placed between participants, ideally within 1–2 metres. Built-in smartphone microphones are sufficient for one-to-one conversations in quiet settings. For noisier environments or larger groups, an external microphone improves results significantly. Avoid recording in spaces with hard surfaces that create echo, or near sources of constant background noise.

How long can a recording be?

There is no practical limit for batch processing — recordings of several hours can be transcribed. Real-time transcription is typically limited by the platform's session duration, commonly 1–4 hours. For case notes, most conversations are 15–60 minutes, well within any platform's capabilities. Longer recordings (training sessions, conferences) may need to be split into segments for optimal processing.

Is AI transcription GDPR-compliant?

AI transcription can be GDPR-compliant, but compliance depends on implementation rather than the technology itself. Key requirements include: lawful basis for processing (typically consent or legitimate interests); privacy notice informing individuals about AI processing; data processing agreements with transcription providers; appropriate security measures; defined retention periods; and the ability to fulfil subject access requests and erasure requests. Conduct a DPIA before implementation and involve your data protection officer if you have one.

Can transcriptions be corrected after approval?

This depends on your system and policies. Most platforms allow editing of transcriptions, but you should consider whether corrections should replace the original or be recorded as amendments. For case notes, it is good practice to allow corrections to factual errors (mis-transcriptions) while maintaining an audit trail of changes. Professional interpretations or assessments added after the fact should be clearly dated and attributed.

Recommended Next Pages

• What Are AI Case Notes? How Speech-to-Text Is Transforming Case Work – The primary application of AI transcription for charities.

• AI Case Notes vs Manual Note-Taking: Which Is Better for Charities? – Detailed comparison to support your decision-making.

• How to Write Effective Case Notes: Best Practices for Support Workers – Documentation standards that apply with or without AI.

• AI Case Notes Feature – How Plinth's AI transcription and case notes work in practice.

• Case Management for Charities – The broader context for AI transcription in case work.

Last updated: February 2026

For more information about AI transcription for your charity, contact our team or schedule a demo.