Artificial intelligence (AI) has left a mark on every industry, from healthcare to clinical social work. Many caseworkers now integrate AI for social work into their caseloads, streamlining previously time-consuming tasks and allocating the best resources for each unique client. This has changed how social workers manage cases and support families and individuals in need.
Beyond administrative tasks, AI helps social workers predict and assess risks based on various client data, which includes their background, behavior, and similar cases. It also helps social work teams immediately access key information about at-risk individuals to support mental health calls and other time-sensitive scenarios.
AI for social work is quite multifaceted, so there’s a lot to understand. Keep reading to learn how AI improves efficiency, supports decision-making, and addresses ethical concerns — and how Casebook helps teams use technology responsibly in social services.
What AI for Social Work Really Means
Artificial intelligence has revolutionized various aspects of social work, from streamlined documentation to early interventions. AI-powered tools simplify and eliminate administrative tasks, such as writing referrals, agendas, schedules, and meeting notes. This cuts down on the number of work hours needed to support each case, letting social workers focus on the clients themselves.
Beyond streamlining and simplifying your caseload, AI tools for social work drastically expand your typical case management capabilities. Many tools use available client information to recommend the ideal resources and highlight key data you may otherwise overlook. This can optimize clients’ treatment plans to give each individual everything they need to succeed.
Will AI Take Over Social Work?
It’s important to emphasize that artificial intelligence is here to support social workers — not replace them. AI has several ethical considerations, including privacy issues and bias, and relying too heavily on it can limit the human impact that makes social work so important. Balancing AI and social work practices requires time and practice to perfect, stressing the need for gradual and informed implementation.
Benefits of Using AI for Social Work
The advantages of AI in social work include:
- Greater efficiency: AI tools streamline essential yet mundane social work tasks, such as double-entering paperwork and writing referrals, maximizing the time you can focus on the clients themselves. Additionally, by summarizing client assessments and meeting notes, AI reduces the time you spend reviewing case information while still equipping you with everything you need to know.
- Higher caseloads: Better efficiency and streamlining can enable your nonprofit to manage higher caseloads than before, scaling your community engagement and your real-world impacts. Predictive models can also help you prepare for potential setbacks without impairing client experiences.
- Improved data accuracy: AI-powered tools limit the risks of human error, such as a misformatted date of birth, when double-entering and documenting client information. This better data accuracy can boost client trust and improve your insights into operations and client performance, especially during early case assessments (ECAs).
- Better client outcomes: AI-powered insights let you consider all available data when evaluating clients’ cases and when making decisions, ensuring you don’t overlook anything important in their treatment plan. Many AI social work tools also recommend treatment goals and resources based on clients’ needs, including options you may not have initially considered.
Important Ethical and Privacy Considerations
Ethical concerns around the use of AI in social work include:
- Privacy: Using AI tools potentially puts clients’ personally identifiable information (PII) and other confidential data at risk of being compromised or improperly shared. You can mitigate these risks by using AI tools with dependable cybersecurity and data encryption. However, even the best cybersecurity isn’t necessarily foolproof, and some clients may distrust AI regardless of which tools you use.
- Informed consent: As part of standard social work practices, you must disclose what clients should expect from their treatment plan, including benefits, risks, specific practices, and alternative services. You must also disclose AI usage, including the potential risks it could have on clients’ services. This disclosure can impair trust with clients who don’t want AI involved in their care plans.
- Algorithmic bias: AI analytics consider various types of information, such as clients’ needs and demographics, when summarizing information and recommending next steps. However, it doesn’t necessarily consider everything, and algorithmic biases can limit what data the AI considers relevant, which can cause you to overlook essential details.
- Environmental impact: AI servers use excessive amounts of electricity, contributing to higher water usage, more greenhouse gas emissions, and unsustainable power generation methods. This has caused eco-conscious organizations to reconsider how they integrate artificial intelligence into their operations.
How AI Is Changing the Way Social Workers Work
AI has changed how social workers approach various aspects of their job, from data entry to client engagement. AI tools for nonprofits now automate and streamline most documentation processes, including transcribing client sessions, taking meeting notes, structuring reports, and writing out care plans.
This efficiency gives you more opportunities for client interaction. For instance, by automating session transcriptions, you can focus all of your energy during intake on actually talking to your client rather than writing everything down. Additionally, by eliminating the time spent entering data or completing other administrative tasks, you may be able to fit more sessions and clients into your caseload.
AI tools can also be directed to specific cases and types of social work, maximizing your available data and resources. For example, predictive analytics and other insights support decision-making processes for child welfare, mental health support, and other sensitive cases that require intense attention to detail. Predictive models have also recommended early interventions for complex substance abuse cases after identifying patterns between clients’ behaviors.
Practical Ways AI Supports Social Work Practice
Artificial intelligence has changed various aspects of social workers’ workflows, from early interventions to documentation. Understanding all the different ways to adopt AI for social work will help you identify the best way to implement it into your unique nonprofit.
1. Predictive Analytics for Early Intervention
Many AI social work tools analyze present and past client data using machine learning (ML) and statistical techniques to develop risk assessments and other predictive analytics. This data forecasting identifies at-risk individuals so you can prioritize time-sensitive cases, initiate early interventions, or adjust treatment plans that simply aren’t working.
Predictive models can also identify underserved populations based on demographics, previous client data, local trends, and other sources. Many nonprofits now use these AI social work insights when refining their program design and policy development so they can stay true to their original mission statements.
2. Smarter Resource Allocation
Managing and allocating client resources is perhaps one of the most complex challenges of social work, especially in today’s shifting economic climate. AI-powered social work tools streamline these processes by analyzing previous data, such as healthcare eligibility and housing application acceptance rates. From here, the AI tools can recommend the statistically best solutions to allocate funding, staff, and other key resources for each unique client.
For example, AI tools have helped social services organizations identify and distribute resources for refugees. This helped the organizations avoid the ongoing challenges of the global refugee crisis, including delays and discrimination. As a result, families immediately received the support they needed, such as housing, employment, healthcare, and social support.
3. Streamlined Case Management
As discussed earlier, using AI for social work can streamline many of your most time-consuming case management processes, such as documentation and transcription. Many tools connect and sync with other programs, letting you securely and immediately transfer client data for real-time updates. Generative AI also improves data and reporting accuracy by eliminating double-entry and checking for inconsistencies and other errors. These simplified workloads give you more time for client interaction, community organizing, and higher caseloads.
4. Better Intersectional Communication
Cloud-based workflows keep all essential case information in one, easy-to-use interface. This way, different teams, agencies, and service providers can quickly access client data to maximize the level of care clients receive.
Real Examples of AI in Social Work Settings
Many real-world agencies and organizations actively use AI social work tools for case assessments, progress tracking, and other essential tasks. Check out the different ways social services organizations have adopted AI, including with micro- and macro-level applications.
Optimizing Social Work in Police Settings
The Lansing Police Department (LPD) adopted Casebook’s AI-powered case management systems to streamline responses and outcomes for in-need residents. The LPD’s social work program initially struggled as it expanded from a single social worker with spreadsheets and file folders into a larger team with a growing caseload.
The Casebook platform gave the LPD social workers a way to streamline intake and reporting, optimizing help for residents in need. It also lets them recommend the safest way to respond to potentially dangerous or risky calls, such as domestic disputes or mental health concerns.
Raising YWCA Client Satisfaction and Reducing Reporting Time
The Young Women’s Christian Association (YWCA) Greater Harrisburg allocates safe housing and other resources for women, children, and families. However, its previous, ineffective documentation processes contributed to inaccurate data and limited its tracking capabilities.
AI-powered social work tools decreased the time spent reporting by 25% and significantly increased client satisfaction rates. Immediate AI insights have also helped the nonprofit’s eight departments identify the best practices and resources for each client’s unique needs.
Scaling Operations at the Philadelphia Community Youth Court
The Philadelphia Community Youth Court is dedicated to eliminating the school-to-prison pipeline, and it honors this mission by providing training for developing youth courts. The organization adopted AI social work resources to digitize its caseloads, simplifying referrals, reducing administrative time by 65%, and giving teams instant access to case information.
This macro-level application significantly expanded the organization’s scalability, letting it offer services to new schools within multiple districts. The Philadelphia Community Youth Court now handles nearly 900 cases per year, effectively using AI tools to reduce recidivism on a greater scale.
How To Prepare Social Workers for AI Adoption
Artificial intelligence can be an incredible tool for social workers and case managers, but even the firmest hammer is only as strong as whoever’s swinging it. Understanding how to properly use AI for social work is essential for maximizing your impact, maintaining data accuracy, and supporting intersectional and interdisciplinary collaboration. This includes knowing how to use your specific AI-powered software — otherwise, you may miss key tools and features that streamline operations.
Best practices for implementing AI for social workers include:
- Training staff: Dedicate time to train social workers and staff to use specific AI platforms and processes, such as with group training sessions, individual training modules, and new onboarding steps. This ensures everyone uses your AI tools the same way, supporting better collaboration and client data accuracy. Many AI-powered case management systems, such as Casebook’s Platform Overview, offer easy-to-follow training videos and customized solutions to help your team adapt to AI as quickly as possible.
- Building digital literacy: Digital literacy describes how well social workers can use AI and other technology to streamline processes, create reports, and analyze information. Providing training for digital communication, data handling, cybersecurity awareness, and other soft skills can improve how effectively social workers adopt new AI technology.
- Creating internal guidelines for responsible AI use: Establishing clear expectations for AI usage keeps your team aligned. This way, everyone knows how to support ethical practices, maintain client privacy, and reduce the impacts of algorithmic bias.
- Balancing human and AI processes: AI tools lack human empathy and other real-world insights that are crucial for social services, stressing that AI will not take over social work entirely. Rather than introducing everything at once, gradually implement AI solutions into your operations to see how it impacts your workflow. This will make it easier to balance human empathy with technological efficiency.
How Casebook Bridges AI and Human Connection in Social Work
Using AI for social work can revolutionize your workflow and your real-world impacts, especially with the right tools. Casebook’s AI-powered case management software integrates data and technology into a secure, easy-to-use interface. This streamlined dashboard supports smarter and more confident decision-making without replacing human expertise, maximizing the help that you can give individuals, families, and groups in need.
Get started tracking, reporting, and coordinating services by learning more about Casebook.