Turn AI from experiments into measurable outcomes — faster, safer, and with less risk — by partnering with a Microsoft consultancy that knows what works for organizations.
Despite the hype, many AI initiatives never get beyond pilots or don’t deliver ROI. The reasons are rarely technical. They’re about clarity of business outcomes, data readiness, governance, and change management. Here’s how to avoid the common traps and get value in weeks, not years.
Why organizations should use a consultancy-led approach
- Speed to value: Proven delivery patterns and templates get you from idea to pilot quickly, without reinventing the wheel.
- Right-sized scope: A consultancy helps define practical, high-impact use cases (e.g., customer service, finance, sales enablement) instead of chasing shiny demos.
- Built-in governance: Security, compliance, and data protection are designed in from day one, reducing risk and rework.
- Change management: Adoption plans, training, and champions ensure people actually use what you deploy.
- Microsoft stack alignment: Make the most of your existing Microsoft 365, Teams, SharePoint, and Azure investments.
Why AI initiatives stall
- Vague problem statements: No clear success metrics or owner. “Try AI” isn’t a strategy.
- Poor data readiness: Content scattered across drives and sites, no data classification, and weak access controls.
- Shadow AI risks: Unapproved tools create security and compliance exposure.
- No adoption plan: Pilots end, users go back to old habits, benefits fade.
- Underestimating ongoing effort: AI needs monitoring, iteration, prompts and workflows maintained, and outcomes measured.
- Unclear licensing and platform choices: Confusion around Microsoft 365 Copilot, Copilot Studio, Azure OpenAI Service, and Power Platform.
A practical, low-risk rollout plan
1) Start with a sharp business case
Pick one or two high-value, low-risk use cases and define success upfront. Examples: reduce average handling time by 20%, accelerate proposal creation by 30%, or increase first-contact resolution by 10%. Agree on data sources, owners, and a 6–8 week pilot timeline.
2) Get data and permissions ready
Map where information lives (SharePoint, Teams, file shares, line-of-business systems) and fix access controls. Apply classification and data loss prevention so AI only sees what users are allowed to see. Microsoft Purview helps discover, label, and protect sensitive data across Microsoft 365 and connected repositories.
Need help establishing governance? See our Microsoft Purview services.
3) Pilot where people already work
Start in familiar tools to minimise friction. Microsoft 365 Copilot brings natural language assistance into Word, Excel, PowerPoint, Outlook, and Teams. For team knowledge and workflows, combine Copilot with Microsoft Teams and SharePoint so content is secure, searchable, and governed.
Explore Copilot options with our Copilot enablement services. Where custom experiences are needed, we can integrate Azure OpenAI through Power Platform or build with Copilot Studio.
4) Close the loop on adoption and value
Train users, set up champions, and capture feedback. Instrument your pilot: track usage, satisfaction, time saved, and quality improvements. Visualise results with Power BI dashboards so leaders see progress weekly.
5) Operationalize and scale
Harden security, expand data coverage, and move to supported operations. Establish simple AI operations: prompt/version control, release cadence, and performance monitoring. Consider Managed Services to keep platforms current and governed while your team focuses on innovation.
Governance and security across the Microsoft ecosystem
- Identity and access: Ensure role-based access and conditional access policies in Microsoft 365 so AI honours existing permissions.
- Data protection: Use sensitivity labels and DLP in Microsoft 365 to prevent oversharing. Purview provides auditability and lifecycle controls.
- Safe connectors: Prefer first-party Microsoft connectors and approved APIs; document data flows for audit and privacy.
- Responsible AI: Establish clear review processes, human-in-the-loop for sensitive decisions, and visible user guidance.
Measuring value and controlling cost
- Define baselines: Measure current effort, cycle time, and quality before you start.
- Track adoption: Monitor Copilot usage in priority teams and correlate with outcomes.
- Iterate ruthlessly: Retire low-value prompts/solutions, double down on winners.
- Right-size licensing: Align Copilot and platform licensing to active users and proven use cases.
How Evocate can help
- AI Readiness and Governance: Rapid assessments, data mapping, and Purview-led controls to make your information AI-ready. Explore Microsoft Purview.
- Copilot Enablement: Use-case discovery, pilot design, adoption programs, and safe rollout of Microsoft 365 Copilot.
- Modern Work Foundations: Secure collaboration with Teams and SharePoint, aligned to Microsoft 365 best practice.
- Insights and Automation: Outcomes dashboards in Power BI, and workflow integration with Power Platform.
- Run and Evolve: Ongoing Managed Services to keep your AI solutions secure, governed, and continuously improving.
Ready to de-risk your AI rollout and deliver measurable value? Contact us or email sales@evocate.com.au.
FAQ
Isn’t Microsoft 365 Copilot enough on its own?
Copilot is powerful, but results depend on data quality, permissions, and clear use cases. Consultancy support ensures the right foundations, adoption, and measurement so value shows up quickly.
How do we protect sensitive data when using AI?
Use existing Microsoft 365 permissions, sensitivity labels, and DLP so AI respects access boundaries. Purview provides discovery, classification, and audit. Start with a scoped pilot and expand once controls are proven.
What’s a realistic timeline to value?
Most organizations can scope, prepare data, and run a meaningful pilot in 6–8 weeks. Measurable improvements (time saved, quality uplift) typically appear by week four if adoption is prioritized.
Do we need data scientists or a new data platform?
Not to start. Many use cases leverage Microsoft 365, Teams, SharePoint, and Power Platform. As you scale, you may add data engineering or analytics capacity to deepen insights.
How do we control cost?
Start small, prove value, and align licensing to active users. Track adoption and retire low-value solutions. Managed Services can reduce operational overhead and avoid costly misconfigurations.
Next steps
If you want AI that delivers outcomes — not shelfware — start with a targeted, governed pilot and scale what works. Evocate can help you get there. Get in touch.
