How AI Is Helping Government Companies in Canada Cut Costs and Improve Efficiency

AI is already reshaping how Canadian governments deliver services - automating high-volume paperwork, speeding decisions and freeing public servants for complex, human-centred work - so a small investment in tooling and training can translate into big back-office savings and faster, more responsive citizen services.

Ottawa's new AI Strategy for the Federal Public Service 2025–2027 and guidance on the Government of Canada guidance on responsible use of AI in government stress transparency, risk assessment and human oversight, while workforce studies show roughly 74% of public-sector roles are highly exposed to AI - so planning for skills and governance is urgent.

Practical steps include piloting low-risk automation, publishing clear AI use notices, and training staff to write prompts and manage models; for teams looking to build those everyday skills, the Nucamp AI Essentials for Work 15-week bootcamp teaches prompt writing and workplace AI application in 15 weeks.

“The thing with AI is it can be such a pervasive technology that there's a lot of places where it could be used depending on the type of AI we're talking about and the types of information it's dealing with.” - Melissa Robertson, CPA Canada

Intelligent automation - where AI meets Robotic Process Automation (RPA) - is already delivering measurable back‑office savings for Canadian public bodies by cutting repetitive work, shrinking error rates and freeing staff for human-centred tasks: Shared Services Canada's pilot with Finance Canada used local, on‑device AI to process and structure more than 4,000 unstructured submissions in seven days, proving modest investments can scale across departments (Shared Services Canada intelligent automation pilot with Finance Canada); elsewhere, ATB Financial reported cumulative efficiency gains equivalent to millions of hours and examples where “something that used to take three days now takes three minutes” after combining RPA with machine learning and document processing (ATB Financial intelligent automation case study - Global Government Forum).

Practical wins in Canada often come from no‑code automation platforms, strong audit trails for compliance, and human‑in‑the‑loop checks that let departments pilot low‑risk use cases, validate ROI and then scale responsibly using shared models and procurement pathways.

A vivid payoff: swap months of backlog for a few minutes of human review, and reclaim time for the complex decisions public servants were hired to make.

“The finance project is a great example of how SSC can work with another department to quickly implement a critical viable intelligent automation solution using the people and application resources of both organizations within a short time period.” Giovanni Savone, RPA developer, Data Science and Artificial Intelligence, SSC

Faster, lower‑cost data processing is becoming realistic for Canadian government teams by combining smarter infrastructure choices with model-level efficiency: Budget 2024's push to create an AI Compute Access Fund and a Canadian AI Sovereign Compute Strategy aims to lower barriers to affordable, compliant compute in Canada (ISED What We Heard report on AI compute in Canada), while breakthroughs in model compression and on‑device inference can shrink the hardware bill and cut latency - Multiverse Computing's CompactifAI claims up to 95% model size reduction and 50–80% inference cost savings, even enabling models to run on tiny devices like a Raspberry Pi (Multiverse Computing CompactifAI model compression announcement).

Practical tactics - hybrid architectures, pruning/quantization, autoscaling and spot-instance automation - mean departments can route heavy workloads to optimised cloud or sovereign resources and keep routine inference local for privacy and speed; the payoff is tangible: fewer cloud invoices, sub‑second responses for citizens, and the ability to process backlogs that once took weeks in minutes.

“The prevailing wisdom is that shrinking LLMs comes at a cost. Multiverse is changing that.” - Enrique Lizaso Olmos, Founder and CEO, Multiverse Computing

Scaling smarter citizen services in Canada increasingly means adding conversational layers - wizards, chatbots and voice agents - that meet people where they already are and handle routine work so staff can focus on complex cases; the success story at Global Affairs Canada shows how two AI chatbots and omnichannel routing now handle 78% of incoming chats and cut live‑chat wait time to about 57 seconds, freeing agents to resolve harder problems (Global Affairs Canada chatbot case study (Comm100)).

At the federal level the Canada Revenue Agency is piloting a GenAI chatbot beta available 24/7 for charities, personal income tax and account help, with clear privacy warnings and a user survey to refine the service (Canada Revenue Agency GenAI chatbot beta for charities and tax support).

Conversation‑design research from Canada's digital teams shows wizards can outperform chat for some tasks while chat and voice shine for others, so a staged approach - start with high‑value, well‑scoped tasks and iterate - lets provinces and departments scale without overspending (Government conversation design research (Design Canada)).

A vivid proof point: when a bot reliably answers common queries, it can convert months of contact-centre backlog into minutes of human review, not guesswork.

“Comm100 does exactly what we needed it to do – integrate multiple channels of communication into a single platform so we don't have to monitor multiple devices and accounts. This improves agent productivity, and most importantly, improves the end user's experience.” - IT Professional, Global Affairs Canada

Cost containment in Canada is increasingly being tackled with shared infrastructure and targeted funding: the AI Compute Access Fund - part of the federal Sovereign AI Compute Strategy - offsets the steep GPU bills that stop many projects by covering two‑thirds of eligible Canadian cloud‑based AI compute (and half for non‑Canadian providers) for projects sized $100,000–$5,000,000, so startups and departments can move from “no budget for training” to a paid pilot; see the AI Compute Access Fund program guide (ISED) for eligibility and terms.

Paired with larger investments to build domestic capacity and a Canadian supercomputing system, these programs let teams route sensitive or latency‑critical workloads to compliant, lower‑cost Canadian providers and use hybrid architectures for routine inference - translating big cloud invoices into predictable, shareable infrastructure costs.

Eligibility rules (Canadian‑registered, <500 FTE, Canada‑based R&D, revenue or Series A support) and competitive contribution models nudge projects toward commercialization, making compute a lever for both cost control and national AI scale rather than a blocker to innovation; read a practical summary of the strategy and funding context in the Accelerate AI Innovation in Canada: AI Compute Access Fund summary (Funding.Ryan).

“The AI Compute Access Fund will help break down barriers and empower businesses and entrepreneurs to develop made-in-Canada solutions. By supporting Canadians across the country in accessing world-class computing infrastructure, we will boost productivity, drive economic growth, and ensure that Canada's digital future is secure and innovative.” - Evan Solomon, Minister of Artificial Intelligence and Digital Innovation

Risk mitigation, governance and compliance in Canada aren't optional checkboxes - they're the operational backbone that lets governments reap AI's efficiency gains without sacrificing fairness or legal rights.

Treasury Board rules require departments to complete and publish an Algorithmic Impact Assessment (AIA) before putting any automated decision system into production, give clear plain‑language notices and

meaningful explanations

to affected clients, and scale safeguards (peer review, Gender‑based Analysis Plus, recurring training and monitoring) to the system's impact level; higher‑risk systems must keep a human in the loop and hold stronger approvals and audits (Government of Canada Directive on Automated Decision‑Making).

The federal guide on generative AI adds practical FASTER principles - Fair, Accountable, Secure, Transparent, Educated, Relevant - and warns staff not to feed personal or sensitive data into public tools without privacy controls (Government of Canada guide on the use of generative AI).

These safeguards exist because real harms can follow opaque automation - an algorithm that reduced medical care for patients is a stark reminder that explainability, legal review and ongoing outcome monitoring matter as much as the tech itself (Statistics Canada guidance on responsible use of automated decision systems).

Workforce enablement is the linchpin of productivity gains in Canada's public sector: the Government's AI Strategy for the Federal Public Service 2025–2027 calls for coordinated training and an AI Centre of Expertise to make sure staff can use AI responsibly and effectively (AI Strategy for the Federal Public Service 2025–2027), while long‑standing talent frameworks stress that leadership and skills are built “in the workplace” and supported by managers who must identify and develop talent (Leadership Development Framework for the Public Service).

The Canada School of Public Service is scaling exactly this mix - new Digital Academy modules, an Executive AI Learning Accelerator and AI‑enabled search and accessibility tools - to give public servants practical, on‑duty skills so routine admin work can be streamlined and people regain time for complex casework (Canada School of Public Service 2025–26 Departmental Plan).

Picture a manager using an AI‑powered search to pull the right policy brief in seconds rather than sifting folders - small changes like that compound into real productivity and better citizen service.

“The School will continue to advance the Government of Canada's priorities and work towards its vision of being the school of choice for public servants by providing the highest quality learning experience.” - The Honourable Shafqat Ali, President of the Treasury Board

Concrete, Canada‑specific implementation starts with a short, practical checklist: inventory candidate processes, risk‑tier them against the Directive on Automated Decision‑Making, and pilot only low‑risk automations while you learn - follow the Government of Canada's guidance on experimentation and risk mitigation in the Government of Canada guide on the use of generative AI; engage legal, privacy, security, bargaining agents and your CIO early, document decisions and, where a system informs administrative outcomes, complete an Algorithmic Impact Assessment before deployment.

Align pilots with the Government of Canada AI Strategy for the Federal Public Service 2025–2027 overview, choose secure or government‑controlled tools (never paste personal data into public models), enable opt‑out features where possible, and bake monitoring, human‑in‑the‑loop checks and periodic audits into every rollout.

Finally, invest in focused staff upskilling (workshops and short courses such as public‑sector AI training) so teams can write effective prompts, validate outputs and turn minutes‑long AI reviews into sustained efficiency gains without compromising trust (Government of Canada AI Strategy overview, IPAC AI Productivity Skills Series for public sector employees).

Canada's AI story in government is best told through practical pilots and measurable systems-level wins: Shared Services Canada's 2022–23 Departmental Results Report documents concrete improvements - 34 of 45 partner departments migrated to a cloud-based M365 email solution, 52 small and medium legacy data centres closed (61% of legacy closures to date), RPA pilots that “improve the accuracy of data…increase the number of requests processed and save dollars and hours,” and enterprise cyber work that blocked over 10 billion threats to GC networks (Shared Services Canada 2022–23 Departmental Results Report).

Looking forward, SSC's 2025–26 Departmental Plan names AI-centred initiatives - CANchat, a hub-and-spoke AI program, and infrastructure work with NRC and ISED to host GC models - that aim to move labs and pilots into repeatable, low-risk services that cut costs and scale capability (Shared Services Canada 2025–26 Departmental Plan).

Even talent pipelines matter: national competitions and conference case studies help train the next wave of analysts and data scientists who turn experimental models into operational efficiency (Shared Services Canada 2025 Case Studies in Data Analysis Competition).

“Climate change will have broad economic and financial impacts, so the Bank has committed to develop new models and methods to better understand physical and transition effects on the Canadian economy. This pilot illustrates how the public and private sectors need to work together to ensure our economy and financial system are adequately prepared to handle the transition to a low‑carbon economy.” - Toni Gravelle, Deputy Governor, Bank of Canada

Canada's path from promising pilots to widescale savings depends on three clear moves: adopt the Government of Canada's AI Strategy as a governance baseline so transparency, Algorithmic Impact Assessments and human oversight are baked into every rollout (Government of Canada AI Strategy overview (AI Strategy for the Federal Public Service 2025–2027)); pair that governance with the new compute commitments so teams can run compliant, cost‑effective models closer to home - consultations and Budget 2024 point to a multi‑billion dollar push (including the AI Compute Access Fund) to expand Canadian sovereign compute and lower GPU costs (ISED consultations on AI compute and access funding); and move fast on people‑first upskilling so employees can safely turn minutes of AI review into sustained productivity - practical, workplace‑focused programs like the Nucamp AI Essentials for Work bootcamp teach prompt writing and everyday AI application in 15 weeks for teams that need action‑able skills now (Nucamp AI Essentials for Work bootcamp (15 weeks)).

Put simply: govern well, pay for the right compute, and train the workforce - do that and backlog that once took weeks can become minutes of human review, unlocking real cost containment and better service for Canadians.