Opening Remarks
AI has two very different jobs right now.
One makes it easier to talk to information.
The other changes what happens next.
The first group – let’s call it chatty AI – is everywhere: email drafts, meeting summaries, code suggestions, “ask your data” tools, marketing copy, customer-service bots… even blog posts 😁
All useful. But if you run time-critical operations, the AI that matters is Operational AI – systems that translate demand and constraints into executable actions: who, what, where, when, and for how long.
If your goals are SLA, throughput, and cost-to-serve, you need AI that reallocates hours – not words.
Chatty AI has its place – and it’s nailed the job it was built for
Recent wins speak for themselves:
- Docs & Email: Instant drafts, rewrites, and tone shifts.
- Code assistants: Autocomplete, refactor suggestions, unit test scaffolds.
- Meetings: Summaries, actions, owners, transcript search.
- Support: Conversational answers, intent detection, macros.
- BI tools: “Explain yesterday’s dip,” “show top drivers,” natural language to SQL.
These tools remove friction and make understanding faster. But they rarely own the decision.
After the summary, a human still decides what to change, when, and within which constraints.
What Operational AI really is
Operational AI ingests live signals, applies objectives and constraints, and outputs executable decisions that slot straight into frontline systems – with transparency and auditability built in.
In the warehouse, that means pulling in:
- Signals: Orders, arrivals, returns, exceptions.
- Constraints: Budgets, capacity, skill mix, compliance, automation limits, transport cut-offs.
- Output: A concrete, executable plan – staffing, tasking, equipment use, space allocation, wave releases, dock schedules – with a clear view of SLA, cost, and utilisation impacts.
Key traits:
- Outputs an action, not a paragraph.
- Optimises under real constraints – spend, storage, skills, and time windows.
- Explains the why – deltas, trade-offs, and decisions.
- Integrates with execution systems (WMS, WES, WFM, TMS, OMS – or spreadsheets if that’s day one).
In logistics, Operational AI spans people, inventory, automation, and transport.
Here, we’re focusing on labour – the fastest lever on SLA and cost-to-serve – showing how signals and constraints turn into an approve-ready labour plan with visibility, insight, and control.
Operational AI in action
Here’s how it works in practice.
A flash promotion lands, driving a sharp spike in inbound orders. Within minutes, the Team Lead spots it and updates demand in the operational AI. That triggers an automatic re-plan.
The platform evaluates the new order volume, current staffing, and all live constraints – budget, skills, automation capacity, and SLA – and produces a new set of recommendations:
- Move people between departments to balance flow.
- Offer targeted overtime where it matters most.
- Adjust task timings to protect throughput.
The Team Lead sees the comparison – before vs after, impact vs cost – and approves with one click.
The approved plan syncs straight into scheduling and WFM systems so everyone stays aligned.
At shift end, the actual performance data flows back into Predyktable, refining tomorrow’s baseline automatically.
What once was reactive guesswork, becomes controlled, data-driven action.
Why it matters
Today, many warehouse decisions still rely on gut feel, fragmented data, and static spreadsheets.
Operational AI changes that; adding speed, accuracy, and shared clarity to every decision, while keeping humans firmly in control.
- Removes gut feel
Decisions are based on live signals, not instinct. Each reallocation, overtime approval, or re-plan is measurable and defensible. - Does the heavy lifting on data
It ingests and connects data across orders, labour, and automation. So teams act on insight, not spend hours building it. - Brings speed, accuracy, and transparency
Re-plans in minutes, not hours. Each decision comes with the “why” – the trade-offs and impact on SLA and cost. - Keeps everyone aligned
Once approved, the live plan syncs across all systems – one source of truth, zero version drift. - Keeps humans in the loop
The machine optimises. The human decides. Approvals, overrides, and scenarios remain human-owned – keeping accountability and trust intact.
Operational AI brings the discipline of data science to day-to-day operations; removing noise, speeding up reactions, and helping people make better decisions faster.
My take as Predyktable’s Chief Product Officer
Conversational AI has transformed how we access and share information. That’s valuable – and it’s here to stay. But the next wave of impact won’t come from better summaries. It’ll come from systems that act – tools that don’t just describe operations, but actively optimise them.
That’s Operational AI. The AI that makes real decisions under real constraints, balancing cost, capacity, and service in seconds, not shifts.
Over the next few years, you’ll see it embedded across warehouses, transport networks, and store operations – anywhere people are deciding who does what, when, and with what resources.
At Predyktable, that’s exactly where we’re focused: putting AI where every minute and every decision matters.
Not replacing people but empowering them with speed, visibility, and confidence.
The future of AI isn’t about talking smarter.
It’s about operating smarter.
That’s Operational AI.
That’s Predyktable.