Artificial Intelligence (AI) is no longer just a buzzword or the preserve of large enterprises. Small and medium-sized businesses (SMBs) are increasingly recognising its transformative potential, from streamlining operations to delivering smarter customer experiences. However, implementing AI can feel daunting, especially when there’s a risk of disrupting daily business functions. Fortunately, there’s a safe, low-risk way to begin: AI pilot projects.
In this guide, we walk you through how to pilot AI solutions in your SMB without disrupting operations, ensuring you gain early value while minimising risk.
Why AI Pilot Solutions Are Important:
AI pilot projects are essential for SMBs looking to explore artificial intelligence solutions without jeopardising their operations. These small-scale trials allow businesses to test AI in a controlled setting, applying tools like chatbots or predictive analytics to specific tasks, such as improving customer service or streamlining inventory.
By starting with a pilot, SMBs can evaluate the technology’s impact—whether it saves time, reduces costs, or enhances customer experiences—while minimising risks to daily workflows. This approach builds confidence among stakeholders, ensures AI implementation for SMBs aligns with business goals, and provides a clear path to scale successful solutions across the organisation.
Beyond testing functionality, AI pilot projects help SMBs refine their readiness for AI adoption for SMBs. They reveal gaps in data quality, integration, or team skills, allowing businesses to address these issues before committing to larger investments. Pilots also foster a culture of innovation by involving staff early, reducing resistance to change, and demonstrating AI’s value in practical terms.
For SMBs, where resources are often limited, AI pilot projects offer a strategic, low-stakes entry into the AI landscape, enabling them to harness AI solutions for small businesses to drive business growth and stay competitive in an evolving market.
Step-by-Step Guide to Piloting AI Solutions
Step 1: Identify Low-Risk, High-Impact AI Use Cases
The foundation of successful AI pilot projects is choosing use cases that deliver quick wins without disrupting operations. Focus on areas with clear pain points and measurable outcomes.
- Audit Operational Challenges: Identify inefficiencies, like slow customer responses or inventory errors. For example, 56% of SMBs cite operational bottlenecks as a growth barrier
- Select Non-Critical Processes: Target secondary functions, like customer query handling, to avoid core disruptions.
- Prioritise Quick Wins: AI chatbots can reduce query times by 40% within weeks
Action: Conduct a data audit, listing all sources and quality issues. Partner with NCS London to streamline this process with expert data management tools.
Step 2. Engage the Right Stakeholders
Resistance to change, reported by 28% of SMBs (Deloitte, 2024), can disrupt AI pilot projects. Engaging stakeholders and training staff fosters alignment and confidence.
- Communicate Benefits: Highlight how AI frees time for creative tasks, not replaces jobs.
- Involve Teams: Include staff in pilot planning to address concerns early.
- Provide Training: Offer AI training for SMBs, with online courses starting at £200.
- Set Expectations: Clarify the pilot’s limited scope to reduce anxiety.
Action: Host a workshop to align stakeholders, scheduling AI training for key staff. Engage NCS London for change management for AI support.
Step 3: Choose Scalable, SMB-Friendly AI Solutions
Selecting the right AI solutions for your pilot is critical to avoid complexity. SMBs need affordable, user-friendly tools that integrate seamlessly with existing systems.
- Match Tools to Use Cases: For customer service, choose NLP-based chatbots costing £300-£500/month. For analytics, opt for predictive models with simple dashboards.
- Evaluate Vendors: Prioritise providers with SMB-focused solutions and UK support. Platforms like Capterra highlight AI tools with 4.5/5 usability ratings.
- Ensure Scalability: Select cloud-based AI solutions, like Microsoft Azure AI, to support future growth.
- Test Compatibility: Confirm tools integrate with your CRM or ERP, addressing the 29% integration concerns.
Action: Shortlist three AI solutions for your use case, requesting 30-day trials. Partner with NCS London for AI integration for SMBs to ensure compatibility.
Step 4: Prepare Your Data for the Pilot
Data is the fuel for AI pilot projects, but poor quality derails 30% of AI initiatives (Gartner, 2023). Preparing data ensures your pilot runs smoothly without operational hiccups.
- Cleanse Priority Data: Remove duplicates and errors from relevant datasets, like customer or inventory records.
- Ensure Accessibility: Centralise data using APIs to avoid silos, a challenge for 39% of firms.
- Validate Quality: Test data accuracy, aiming for 90% reliability.
- Limit Scope: Use a subset of data to minimise risk, like one month’s customer queries.
Action: Spend one week cleansing data for your pilot. Consult NCS London for data management for AI to ensure readiness.
Step 5: Launch the Pilot with Minimal Disruption
Data is the fuel for AI pilot projects, but poor quality derails 30% of AI initiatives (Gartner, 2023). Preparing data ensures your pilot runs smoothly without operational hiccups.
- Start Small: Deploy the AI solution in one department, like customer service, for 30-60 days.
- Use Parallel Systems: Run the AI tool alongside existing processes to avoid reliance.
- Monitor Closely: Assign an IT manager to oversee performance and address issues.
- Limit Users: Restrict access to a small team to contain risks.
Action: Launch a 30-day pilot, running the AI tool in parallel with current systems. Use NCS London’s AI implementation support to monitor progress.
Step 6: Measure and Evaluate Pilot Outcomes
Data is the fuel for AI pilot projects, but poor quality derails 30% of AI initiatives (Gartner, 2023). Preparing data ensures your pilot runs smoothly without operational hiccups.
- Track KPIs: Measure outcomes, like 20-30% efficiency gains or customer satisfaction improvements.
- Gather Feedback: Survey staff on usability and operational impact.
- Assess Disruptions: Confirm no significant workflow interruptions occurred.
- Document Learnings: Note challenges, like integration hiccups, for future deployments.
Action: Launch a 30-day pilot, running the AI tool in parallel with current systems. Use NCS London’s AI implementation support to monitor progress.
Step 7: Plan for Scaling or Iteration
Post-pilot, decide whether to scale, refine, or pivot your AI pilot projects based on outcomes, ensuring long-term success.
- Analyse Results: If KPIs are met, plan enterprise-wide deployment.
- Address Gaps: Fix issues, like data quality, before scaling.
- Secure Funding: Use pilot success to justify further AI investment.
- Iterate if Needed: Test a new use case if the pilot underperforms.
Action: Draft a scaling plan or iterate the pilot within 90 days. Collaborate with NCS London for AI scaling strategies to ensure seamless expansion.
Conclusion: Launch Your AI Pilot Journey with Confidence
The UK’s AI landscape, set to add £38 billion to the economy by 2030 (World Economic Forum, 2024), offers SMBs a chance to lead. AI pilot projects provide a low-risk entry, but success requires careful planning to avoid disruptions. By selecting high-impact use cases, preparing data, and measuring outcomes, you ensure AI implementation for SMBs delivers business growth.
At NCS London, we specialise in guiding SMBs through AI pilot testing for SMBs with minimal risk. Our expertise in data management for AI, AI integration, and AI performance analytics ensures your pilot succeeds. Don’t let fear of disruption hold you back—contact NCS London today to launch AI solutions for small businesses that transform your SMB into a 2025 powerhouse.
