Discover 10 best practices for AI deployment in mid-market companies — coming from the Google Cloud team, Google Partners and Google Workspace users. Learn how to scale AI adoption, avoid pitfalls, and drive real business impact.
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Why AI Deployment Matters Now
AI is no longer a future ambition—it’s shaping the way organizations operate today. But while adoption is widespread, most leaders admit they aren’t seeing the full payoff. According to McKinsey, “The State of AI,” March 2025, 72% of organizations report using AI in at least one business function. Also, 92% plan to increase AI investments over the next three years. However, another report (“Generative AI adoption in the enterprise,” 2025), 71% of companies face significant adoption challenges. And only only 12% say they are truly reaping the rewards of their AI investments. Therefore, if you’re not sure where to begin with embedding AI in your operations, or not seeing immediate ROI, you’re not alone. This blog post is aimed at helping you navigate the challenge and find a shorter, more successful path to AI deployment.
Where These Best Practices Come From
But before we start, a couple of words on where the best practices we cover come from. The framework advocated in this article, is based on insights from different authroritative sources:
- Google Cloud teams working with enterprises across industries. Some of the best practices featured in this post, are highlighted in the Google's ebook Best Practices For AI Deployment in 2025, shared with the Google Partners.
- Google Cloud partners helping organizations operationalize AI at scale. Zenphi works with several dozens of Google partners, and constantly collects their insights during one-on-one calls and round tables
- And the last but not the least — Zenphi customers who successfully embedded AI in Google Workspace to streamline operations. For us, these companies represent the mosr reliable source of information, as they have faced the challenges you're facing now, and found their ways around them.
Together, these sources offer a practical roadmap for scaling AI with measurable ROI.
The #1 AI-Driven Automation Platform For Google Workspace Users
Zenphi is purpose-built for Google Workspace automation, making it the go-to platform for businesses that want both IT and business process automation in one place. It is voted as the best by hundreds of mid-sized companies and SMEs that want end-to-end automation inside Google Workspace without juggling multiple platforms.
10 Best Practices for Successful AI Deployment
1. Anchor in Culture
Embed AI into everyday workflows so it feels natural, not forced.
Result: AI becomes part of team identity, not an external tool.
Hear from Ryan Duguid—product leader, tech investor, and startup advisor—on the best ways to embed AI into day-to-day operations (2 min video).
2. Assign a Clear Owner or Champion
Every rollout needs both executive sponsorship and team-level champions.
A healthcare customer of Zenphi created a cross-functional AI committee spanning IT, HR, and compliance.
Result: Silos broke down and adoption accelerated across functions.
3. Choose an Integration Partner You Can Trust
AI projects rarely succeed in isolation. Work with a partner who understands both your business operations and your tech environment and is capable of going far and beyond to help you accomplish your goals.
Many Zenphi customers rely on our team to guide them through the process of deploying AI and building AI workflows. Hear directly from Paul Kerton, Google Workspace manager at Lift Schools of London (30,000 students) on why did they choose Zenphi as an automation partner — 1 min video.
4. Start Small, Gain Wins, and Scale From There
Begin with a simple, high-value use case—like automating approvals or inbox triage. Once you see results, expand.
For example, if you’re looking for an AI for IT Ops automation, there are some most likely candidates to begin your journey with. Listen to the former Google employee, now Zenphi’s Partner and AI automation expert — Mike Klambro — suggesting the most obvious workflows for the quick wins (2 min video).
5. Define Proof Points and Metrics
Track your before-and-after baselines (time saved, error reduction, throughput). Hard numbers make it easier to secure budget for future AI investments.
For example, one of Zenphi customers from healthcare, CIT Clinics, has saved 20 minutes per patient during the onboarding process due to the AI-powered workflow automation
Hear directly from their Head of Operations on the results they achieved quickly (2 min video).
6. Choose a Platform That Works With Your Existing Tech Stack
AI should complement, not replace, your current systems. Zenphi is built for Google Workspace but also offers pre-built integrations with 150+ other solutions—including Microsoft Outlook, Teams, Excel Online, and SharePoint.
7. Make It Painless for the Wider Team
Not everyone should have to learn a new platform. The people benefiting from AI automation don’t need to manage it—they just need to keep focusing on their best work.
With Zenphi, automation happens in the background. For example, one of our customers in education explains — team members who don’t build AI-powered automations, are not even aware that there’s a smart Autonomous AI Agents for Google running in the background. They keep using the tools they are familiar with (Gmail, Forms, Sheets) — just incredibly happy with the improved outcome. Hear from the Camp Ramaquois director on how they rolled out AI to the wider team while avoiding a steep learning curve.
8. Align With Team and User Needs
Tailor your AI rollouts to different departments. Finance might want precision, while Sales might want speed. The best adoption happens when solutions are personalized.
Result: Employees see AI solving their problems, not abstract ones.
9. Refine Frameworks Continuously
AI is evolving too fast for static playbooks.
Zenphi customers run quarterly reviews to refine workflows and sunset outdated automations.
Result: Continuous improvement keeps adoption fresh.
10. Define a Transformation Roadmap
Think beyond pilots.
One Zenphi manufacturing client mapped an 18-month plan: document processing → supplier workflows → predictive analytics.
Result: Clear expectations and trust in long-term AI adoption.
Pitfalls to Avoid in AI Deployment
Even with best practices, many organizations stumble. Watch for these:
- Overhyping results and setting unrealistic expectations.
- Skipping baselines, making ROI impossible to prove.
- Failing to adapt frameworks as AI evolves.
- Poor communication, leading to siloed adoption.
- Blind trust in AI outputs without human validation.
- Not tracking usage, leaving leaders blind to adoption patterns.
- Overcommitting to first tools without considering alternatives.
Depending on the size of your business, these pitfalls may have different levels of impact. As shown in this helpful chart from the Google Cloud team, smaller businesses are most affected by the lack of clear baselines, while larger companies tend to suffer more from unrealistic expectations and rigid existing operations.
Final Takeaway
AI is moving from hype to necessity. For SME, successful deployment depends on embedding AI into culture, governance, and operations—while avoiding the most common pitfalls.
If your team is utilizingGoogle Workspace environment, with Zenphi you can go beyond experiments and deploy AI-powered workflows that deliver measurable results across your business.
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