Webinar - Agriware AI & Innovations

Co-Creation with the Industry

Watch the warm-up from our AI roundtable: inspiration drawn from standard Microsoft Business Central features, translated into opportunities for horticultural businesses. Together with around 50 Agriware customers from all over the world, we collected more than 15 ideas — from smart agents providing concise alerts to autonomous AI workflows.

Proof of concept

At Agriware, we’re developing these innovations together with our customers, with one clear goal: creating tangible value in the emerging AI domain for horticulture. Get in touch to improve your idea or business process. The best idea will be developed into a working prototype during next month’s annual Hackathon.

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Hosted by Arno Hogervorst - Innovations Manager at Mprise Agriware

Want to know more on our vision on AI & Innovation, watch here

Short version: AI is no longer a “someday” add-on for horticulture. With the current versions of Mprise Agriware Business Central, you can use chat, Copilot features, and task-focused AI agents to cut admin, speed up planning, and keep customers informed—without months of custom dev.

What changed (and why it matters)

The breakthrough isn’t just large language models (LLMs) talking like humans. It’s the combination of:

• Natural-language interfaces (chat/voice) that let users ask for data and get answers fast.
• Agents: small, purpose-built virtual assistants that watch for triggers (an email, an inspection value crossing a threshold) and then act—following rules you control in plain language.

Bottom line: you tell an agent what to do in your words; it handles the repeatable steps and asks you to confirm when it should.

What you can use today in Business Central

These are shipping features users can turn on and benefit from now:

• Chat with Copilot (in-product): Ask “Show me invoice data for customer X” or “What’s their total balance?” Get an answer in the right-hand pane. Works across Business Central and Agriware data.
• Autofill suggestions: Those two-star icons propose values or calculations in context. You can accept or override.
• Data analysis with Copilot: Think pivot tables you can steer by asking “Group by year, add due date.” Faster analysis, fewer clicks.
• Bank reconciliation assist: Drag-and-drop bank statements, and Copilot pre-matches ~75% of lines. You stay in the loop to approve the rest.
• Predict Late Payments: Machine learning flags which invoices are likely to slip, with a confidence score—so account managers can act before cash gets stuck.

Design principle: user-in-the-loop. AI proposes; people approve.

‍Vision without the buzzwords

Expect more natural interaction (wearables, voice, on-device AI) and more adaptable UX. But the core—a clean, shared data model in Agriware—remains. That’s where robots, scanners, and third-party tools need to land structured data so everything else can work.

How to get involved (and influence what ships next)

• Agriware Community Portal: Post ideas, vote, and comment. We use your input to prioritize.
• Internal Hackathon (November): We’ll prototype the top-voted AI agent idea in one day to prove value and de-risk scope.
• Version notes: Some features are preview vs. production and can vary by region/version. We’ll keep shipping Agriware-side capabilities to reduce dependency on regional rollouts.

FAQs

Is this five years out?
No. Most capabilities above exist in current Business Central/Agriware builds. Some agent features are in preview and improving with upcoming releases.

Do I need custom development?
Less than you think. Many “we need AI” requests are already standard in Business Central/Agriware. Agents help where rules + repetitive steps bottleneck your team.

Who’s responsible—the AI or me?
You are. Agents prepare, propose, and execute with your approval. That keeps quality high and audit trails clean.

Takeaways you can act on this month

• Turn on Copilot in Business Central and use chat for day-to-day questions.
• Pick one high-friction workflow (e.g., availability shifts → update delivery dates) and pilot a focused agent.
• Tighten master data (variants, pot sizes, color mapping). AI can’t fix vague or conflicting codes.
• Set alerts, not meetings. Let agents watch expiry windows, demand changes, and inspection thresholds—and ping the right role at the right time.

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