AI Consulting for Large Organizations and Enterprise Teams
Large organizations rarely lack AI tools. What they lack is agreement on which uses are allowed, which are worth scaling, and who owns the result. We help large organizations, and single business units inside them, start in a way procurement, IT, and legal can support: a scoped assessment, clear usage guidelines, one pilot measured against a baseline, and a rollout plan only when the pilot earns it. One senior consultant leads and does the work.
The Challenges
Pain points we solve for enterprise ai solutions
AI pilots that stall in one department because nobody planned how to scale, fund, or support them
Business units buying overlapping AI tools with no shared inventory, approved-tools list, or budget view
Executives asked to approve AI spend without an agreed way to rate risk or measure return
Staff using public AI tools informally because the approved path is unclear or slow
AI assistants describing your organization from outdated or third-party pages instead of your own
The Canadian Market
How large Canadian organizations are approaching AI
Large employers are a small share of Canadian businesses but a large share of the jobs. Innovation, Science and Economic Development Canada counted 3,380 businesses with 500 or more employees in December 2024, about 0.3 percent of employer businesses, yet large businesses employed about 4.5 million people, or 36.4 percent of the private sector workforce. They concentrate where head offices do: 1,528 in Ontario, 666 in Quebec, 423 in British Columbia, and 391 in Alberta.
They are further along with AI than smaller firms, and they lean on outside help more. In Statistics Canada's second-quarter 2026 survey, 27.8 percent of businesses with 100 or more employees reported using AI in the previous 12 months, against 19.2 percent of all businesses. Among those larger AI users, 68.1 percent trained existing employees, 51.7 percent trained executives, and 30.2 percent used external consultants or vendors, nearly three times the 10.7 percent rate among businesses with one to four employees.
Caution rises with size too. In the same survey, 30.0 percent of businesses with 100 or more employees cited cybersecurity or privacy concerns as a barrier to AI, compared with 11.6 percent of the smallest firms. That is why large buyers usually ask first for an approved-tools list, a usage guideline, and a clear view of where data goes.
Governance expectations are becoming concrete. OSFI's Guideline E-23 on model risk management, published September 11, 2025 and effective May 1, 2027, expects federally regulated banks, insurers, and trust and loan companies to keep an inventory of their models, including AI and machine learning models, rate their risk, and manage them from design through decommissioning. Outside regulated sectors there is no general federal AI statute: the proposed Artificial Intelligence and Data Act was part of Bill C-27, which did not pass before the parliamentary session ended in January 2025. Privacy law is moving, though: on June 15, 2026 the government introduced Bill C-36, which would enact a new private-sector privacy law, the Protecting Privacy and Consumer Data Act, and amend PIPEDA. It was still before Parliament in October 2026. The federal Voluntary Code of Conduct on advanced generative AI systems (September 2023) sets out principles such as accountability, transparency, and human oversight that many organizations use as a reference. We help build practical guidelines and tool inventories in that spirit; we do not give legal or regulatory advice, and your counsel has the final word.
Sources
- Statistics Canada, Analysis on artificial intelligence use by businesses in Canada, Q2 2026
- Innovation, Science and Economic Development Canada, Key Small Business Statistics 2025
- OSFI, Guideline E-23: Model Risk Management (2027)
- ISED, Voluntary Code of Conduct on the Responsible Development and Management of Advanced Generative AI Systems
- Parliament of Canada, LEGISinfo: Bill C-27 (44th Parliament, 1st session)
- Parliament of Canada, LEGISinfo: Bill C-36 (45th Parliament, 1st session)
AI Opportunities
How AI transforms enterprise ai solutions
An AI opportunity assessment for one business unit, ranked by value, effort, and risk
Written AI usage guidelines and an approved-tools list, drafted with your IT, privacy, and legal leads
Department-by-department workshops built on your own documents and tools
Staged pilot-to-rollout support, with a baseline measured before anything is built
An AI search visibility review of how ChatGPT, Perplexity, and Google AI Overviews describe you today
Example Scenarios
What this looks like in practice.
Illustrative scenarios, not client results. They are based on common workflows in this industry, and any figures are examples rather than outcomes from specific clients.
Pilot-to-Rollout Across Departments
Before
A customer service AI pilot showed promise but never expanded. Other departments bought their own tools. Nobody owned the learnings, the spend, or the risk review.
After
A staged plan takes the proven workflow to one department at a time, on tools IT has approved, with a baseline, adoption measures, and a named owner in each unit.
Executive Alignment on AI Investment
Before
Each VP has a different vendor pitch. There is no shared way to decide what to build, what to buy, and what to ignore.
After
A cross-functional assessment and executive workshop produce a short, ranked list of initiatives with estimated effort, risk, and success measures, so leadership can fund one or two with clear owners instead of ten without.
Typical Engagements
How a enterprise ai solutions engagement usually runs.
These are composites of how this work typically unfolds, not reports on specific clients.
AI usage guidelines and department workshops for a 300-person organization
Staff are already using public AI tools, and leadership wants rules and skills in place before buying more. We survey current use, then draft AI usage guidelines and an approved-tools list with your IT, privacy, and legal leads (your counsel signs off). Half-day workshops follow, department by department, built on each team's real documents. Typical shape: guideline drafting scoped as an assessment (from $1,500) plus a workshop series (from $1,500), over six to ten weeks.
Assessment, pilot, and rollout for one business unit
For a finance, operations, or customer service unit inside a larger organization. Stage one is an assessment that maps the unit's workflows and ranks opportunities (from $1,500). Stage two is one pilot, built inside your own tenant on approved tools and measured against a baseline taken beforehand (from $1,500). Stage three, only if the pilot earns it, is a rollout plan covering training, support, and ownership, usually scoped as a strategy engagement (from $5,000). Each stage is a separate decision, typically over three to six months.
Search Visibility
SEO & GEO for Enterprise AI Solutions
For large organizations, SEO and GEO are less about ranking one page and more about making sure search engines and AI assistants describe you accurately. We audit how those tools describe your organization today, trace the sources they draw on, and fix what you control: capability pages, location information, structured data, and consistency across business units and regional sites. No one can guarantee what an AI system will say, so we report what changes and what does not.
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FAQ
Common questions about AI for enterprise ai solutions.
Scale and focus. A large firm brings many people and the capacity to run an organization-wide transformation at once. We bring one senior consultant who does the work, with no hand-off to a junior team. That suits a first program in one business unit, a governance and training push, or a carefully run pilot.
We start with one business unit and a written assessment, then move in stages. Each department gets workflows and training fitted to its own work, but on one approved-tools list and one set of usage guidelines, so the second department does not rebuild what the first already learned.
Most organizations start with an assessment (from $1,500) or a workshop series (from $1,500). Automation builds start at $1,500 each, and a multi-stage strategy and rollout program for a business unit starts at $5,000. Every stage is scoped and priced separately, so you can stop after any one of them.
We agree on success measures before building anything: hours spent on a task, turnaround time, error or rework rates, and how many people actually use the tool. We measure a baseline first and report against it after each stage, including when a pilot does not earn a rollout.
By keeping the scope honest. One senior consultant leads every engagement and does the core work, with a small bench of trusted specialists for volume under the same oversight. Work is staged (assessment, then pilot, then rollout) so each stage fits the capacity to do it well. If you need dozens of people deployed across the whole organization at once, a large firm is the better fit, and we will say so in the first conversation.
Yes. We sign NDAs before discovery, complete vendor onboarding forms and security questionnaires, and work inside your own tenant, accounts, and approved tools rather than moving your data into ours. Designs default to data minimization: the least data needed, kept in systems you already control. We do not hold certifications such as SOC 2 and do not claim to. We work within the security requirements your IT and privacy teams set, and your counsel or privacy officer reviews anything with legal implications, including how PIPEDA, provincial privacy law, and Bill C-36 (a proposed new federal private-sector privacy law still before Parliament) would apply.
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