Multi-Sector Innovation Sandbox

Key finding: By explicitly involving private-sector industries in defence innovation sandboxes, MND becomes both a beneficiary and a driver of enterprise innovation in Canada — de-risking new technology on a small scale before major investment, and opening pathways to commercialize solutions it helped develop.

Innovation often flourishes in sandbox environments — controlled settings where new ideas can be tested safely and iteratively. The Minister of National Defence (MND) has recognized this through its IDEaS (Innovation for Defence Excellence and Security) program, which already hosts sandboxes and contests for specific challenges (e.g. counter-drone systems). This strategic plan proposes expanding the sandbox concept to broader operational and process innovations, and explicitly designing some sandbox projects for dual-use applicability in civilian sectors such as finance, insurance, manufacturing, healthcare, data management, and beyond. By doing so, MND can accelerate its own digital transformation while also spurring innovation that benefits Canada's economy and other public agencies.

Establish an AI & Automation Proving Ground

Mirroring a recommendation from the U.S. defence AI strategy, MND will set up a virtual "AI and Autonomous Systems Proving Ground". This would be a sandbox environment (both a technical platform and a governance framework) where developers, MND units, and industry partners can experiment with AI-driven solutions on real MND data and workflows, without impacting live operations. For example, MND could provide a sanitized dataset of maintenance records or supply transactions to startups and let them deploy AI models in the sandbox to optimize those processes. Use cases might include an AI agent that schedules vehicle repairs across a fleet, or a machine learning model that predicts supply chain bottlenecks. The sandbox would have cloud computing resources, simulated MND systems, and test cases defined by MND experts.

Importantly, private sector companies (from a bank wanting to test an AI for fraud detection to a manufacturer tuning a production AI) could be invited to utilize the sandbox's infrastructure and even the lessons learned. By cross-pollinating use cases (e.g. an AI that works for MND logistics might translate to commercial logistics), the sandbox drives efficiency both for MND and industry. It also allows MND to fail fast and cheap — trying bold ideas in isolation first. Prizes or funding (via IDEaS or similar) can incentivize participation, with MND acting as both beneficiary and incubator of new tech. This environment will be crafted with appropriate security (separating classified info) and ethical oversight for AI.

Cross-Sector Pilot Projects in Key Domains

We recommend launching a series of themed innovation sandboxes/pilots over the next three years, each focusing on a domain that is a priority for MND and has high relevance to other sectors. Possible examples include:

• Predictive Maintenance Sandbox. Bring together MND maintenance units, AI firms, and, say, a railway or airline company to jointly pilot predictive maintenance tools. Each participant can apply the tool to their equipment (e.g., CF-18 engines, Air Canada jet engines, VIA Rail locomotives) in a sandbox setting, share data (as feasible), and discuss best practices. All parties benefit from improved algorithms and techniques.

• Operational Efficiency (Workflow) Sandbox. Collaborate with a major bank or insurance company on automating large-volume document processes. For instance, MND's payroll or claims process might be analogous to an insurance claims process; a sandbox project could develop an AI document processor that handles forms for both, learning from both contexts. This breaks down silos between public and private sector problem-solving.

• Smart Infrastructure Sandbox. Partner with a city or tech firm to pilot smart base technologies (IoT for energy, security) that also apply to smart buildings in cities. A base could serve as a mini smart city testbed, evaluating sensors and analytics that a municipality or hospital network might also use to reduce costs and improve resiliency.

• Digital Training and Education Sandbox. Involve academia and EdTech companies to test AI-based training for both military and civilian skills (e.g. a simulation platform for disaster response that could train both soldiers and first responders).

By explicitly involving private sector industries in these sandboxes, MND ensures solutions are interoperable with and informed by civilian best practices, and it opens pathways for commercializing solutions that MND helped develop (potentially lowering future costs for MND as solutions mature and become off-the-shelf). This also aligns with the Government of Canada's push for innovation ecosystems — MND can be a hub that brings together companies, universities, and other agencies to tackle complex problems in a low-risk environment.

Policy and Culture Support for Innovation

For sandboxes to thrive, MND must foster a culture that encourages experimentation and tolerates the possibility of failure in controlled settings. Leadership should formally endorse these initiatives, and policies will be adjusted as needed — for example, simplifying procurement of experimental technology (perhaps using flexible contracting or other transaction authorities so sandbox projects aren't bogged down) and providing legal frameworks for data sharing with private partners under appropriate agreements. We will also institutionalize the practice of taking sandbox results and scaling them: if a pilot demonstrates clear benefits in the sandbox, fast-track it to real-world deployment (with necessary evaluations). The aim is that by Year 3, MND has a repeatable pipeline from "innovative idea tested in sandbox" to "production implementation". This pipeline de-risks innovation: new technologies can be vetted on a small scale, proving their value and ironing out issues, before major investments are made.

Expected Outcomes

The innovation sandboxes are an enabling strategy, so their outcomes are measured in successful innovations adopted and the ripple effects of those innovations. In the short term (3 years), we expect at least 5 major pilot projects to run through the sandboxes, of which a majority yield deployable solutions that MND integrates. These might include, for example, a new AI scheduling system, a cross-domain data analytics tool, or a novel cybersecurity solution (since cyber resilience is also a form of operational resilience). Each successful innovation could lead to ongoing cost savings or performance gains; cumulatively, they contribute to the plan's overarching goals of efficiency.

Moreover, by involving outside industries, MND helps propagate best practices between defence and civilian sectors. Private companies might adopt a solution first tested with MND, improving their productivity, while MND might learn from commercial sector approaches to problems (e.g. finance industry's advanced risk analytics could inform military logistics). In essence, MND becomes both a beneficiary and a driver of enterprise innovation in Canada. This elevates MND's profile as a forward-thinking organization and can attract talent interested in cutting-edge projects. Finally, these efforts position MND to better respond to future challenges — having sandboxed emerging tech (like new AI models or quantum computing applications) means MND can operationalize them faster than if it stuck only to traditional acquisition.

Initiative Assessment: Impact, Scalability, and Feasibility

The table below summarizes how each major initiative in this plan rates in terms of impact, scalability, and feasibility, reaffirming that our recommended actions are both ambitious and achievable within a Canadian public sector setting:

Initiative	Impact on Cost & Efficiency (What)	Scalability (Where/How Broad)	Feasibility (3-Year Horizon)
Predictive Maintenance (AI-driven equipment upkeep)	High: Lowers maintenance costs by up to ~25%; prevents up to 70% of breakdowns, improving readiness. Extends asset lifespans, reducing capital replacement needs.	Enterprise-wide across vehicle fleets, aircraft, ships, base utilities. Scalable as more assets get sensor-equipped; begin with pilots (e.g. Navy) and expand to all services.	Feasible: Proven in MND pilot and industry; requires upfront investment in sensors and analytics team. Can be phased (start with critical systems). ROI positive within 1–2 years of deployment.
Digital Procurement (e-procurement, AI analytics)	High: Streamlines purchasing — up to 30% cost reduction and 40% efficiency boost reported. Reduces admin overhead and unit prices through smarter buying.	Applicable to all procurement categories (except major weapon systems). Can be scaled across MND and even to wider government procurement if successful.	Feasible: Commercial e-procurement solutions exist; moderate integration effort. Aligns with Treasury Board digital standards. Training and change management required, but similar projects done in other departments.
Real Estate Optimization (consolidation, energy retrofits)	Medium-High: Directly cuts facility O&M costs (utilities, maintenance). A 20% reduction in office space can yield substantial rent and energy savings. Greener infrastructure lowers long-term costs.	Nationwide across MND's property portfolio. Scale by focusing on largest cost-driver sites first (e.g. big bases, HQ offices). Consolidation impacts multiple regions.	Feasible: MND has a roadmap (DRPPS); requires inter-group coordination and some upfront capital for retrofits. Disposal of assets can be slow (approvals needed), so must start early. High-level buy-in (Deputy Minister, CDS) already in place.
Workflow Automation & AI Agents (RPA, AI for admin)	High: Can eliminate a large portion of manual workload. Many processes >50% faster, with fewer errors. Frees personnel for higher-value tasks, effectively reducing labor costs.	Scalable to numerous functions: finance, HR, logistics, customer service. Once platform in place, adding new bots/agents is relatively fast. Whole-of-department impact.	Feasible: Technology is mature (RPA) or maturing rapidly (AI agents). Start with simple RPA in Year 1, incorporate advanced AI by Year 2–3 as models improve. Needs IT governance and cyber controls, but manageable.
Asset Tracking (RFID/IoT) (inventory management)	High: Greatly improves asset visibility and utilization. Prevents loss/theft and avoids redundant purchases — translating to cost savings. Up to 90% time savings in inventory tasks observed.	Can be rolled out across all supply depots, equipment warehouses, and even at unit level for critical gear. Leverages standard tech (RFID) that is easily replicable at scale.	Feasible: RFID tech is off-the-shelf. Initial tagging effort needed, but can be done in stages. Several militaries and industries have done this, de-risking the approach. Integration with existing inventory systems required but technically straightforward.
Innovation Sandboxes (cross-sector tech pilots)	Indirect/Long-term: Provides pipeline for future high-impact solutions (e.g. new AI tools could yield major efficiency gains). In short term, modest costs for potentially huge long-term payoffs.	Scalable in scope (multiple sandbox themes) and participants (involving more industries, academia, international partners over time). Could become a permanent innovation hub for MND.	Feasible: MND already runs IDEaS with sandbox elements; this expands scope. Needs dedicated team and modest funding. Benefits from Canada's strong tech sector engagement. Manageable under existing R&D budgets.

Implementation Roadmap (2025–2028)

Delivering this strategic plan will require a phased approach over three years, with clear milestones to ensure accountability and momentum.

Focus Area	Year 1 (2025–26): Initiation & Quick Wins	Year 2 (2026–27): Scaling Up	Year 3 (2027–28): Full Integration
Innovation Sandboxes	Design the Sandbox Framework: identify governance, funding sources, and initial themes. Launch Sandbox #1 (for example, an "AI for Maintenance" sandbox challenge) and Sandbox #2 ("Digital Admin Bot" challenge) with calls to industry and academia.	Multiple sandbox projects running: e.g. Maintenance AI sandbox yields a prototype predictive tool in use by MND maintainers on a trial basis; a procurement sandbox project delivers a beta AI contract analysis tool. Evaluate and transition successful pilots to live environments (with expedited procurement if needed). At least 3 new solutions from sandboxes fully adopted within MND. Host at least one sandbox event where innovators test tech at an MND site (could be tied to IDEaS). Increase cross-sector collaboration — perhaps co-host a sandbox with another department (e.g. jointly with Transport Canada on a logistics resilience scenario).	Institutionalize innovation: MND makes the sandbox program a continuous, rolling effort with yearly challenge cycles. Document and share outcomes: publish case studies of sandbox successes that other industries or departments have also adopted, solidifying MND's role as an innovation leader. Establish partnerships/MOUs with a few private companies to join sandbox efforts (perhaps a bank for AI process automation, a tech firm for logistics AI). Secure ongoing funding for the innovation program beyond Year 3, showing its value in dollars saved or capability gained.