Buying AI productivity software now feels very different from buying a standard collaboration tool. In the past, unified communications procurement centred on meetings, messaging, user experience, and total cost of ownership. Buyers now also need to evaluate copilots, AI agents, governance boundaries, data access, integration depth, and whether any of it will create measurable value for teams and the business. Enterprise buyers therefore need a clearer readiness process, stronger commercial questions, and a more disciplined way to assess vendor claims. Otherwise, it becomes very easy to overspend on licences and underuse the platform. That leads to AI that looks impressive in a demo but changes very little in practice.This matters especially for UC Today’s audience. In unified communications, AI is increasingly embedded inside the tools employees use every day. Buyers evaluating copilots and workplace assistants are not only buying features. They are buying a potential operating model change.
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The platform may influence how meetings are run, how decisions are captured, how follow-up work is routed, how data is exposed, and how much control IT retains over all of it. According to McKinsey:
“Agentic AI is changing what the procurement function can achieve—shifting procurement’s focus from transaction tasks to a strategic driver of growth, sustainability, and resilience.”
Buying workplace AI is no longer just a sourcing exercise. It is part of how the enterprise decides to shape work, risk, and value creation in the years ahead.
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What Should Be Included in an AI Productivity RFP?
An AI productivity RFP should define the business problem, workflow goals, governance requirements, integration expectations, adoption plan, the commercial model, and the evidence the vendor must provide to support ROI claims.
Many organisations make the same first mistake. They write an RFP around product categories instead of operating problems. If the document simply asks vendors to describe their AI assistant, workflow features, or agent capabilities, buyers end up comparing marketing language rather than practical fit. A stronger brief starts with the friction the organisation is trying to remove.
That may mean reducing meeting overload, improving post-meeting follow-up, accelerating approvals, cutting admin work in Teams or Zoom, linking calls to CRM updates, or supporting IT and service workflows through embedded AI. The core requirement is to describe the work that needs to improve, not just the technology you hope will improve it.
What Vendors Should Be Forced to Answer
From there, the RFP should require vendors to address a more rigorous set of criteria. This includes clearly distinguishing which workflows are fully automated and which remain assistive. It should also define the boundary between copilots and autonomous agents. Vendors should outline native system integrations, detail how permissions are managed and enforced, and specify the level of control retained by IT. They must also explain how success will be measured and what reporting capabilities are in place to demonstrate value post-deployment.
Procurement should also insist on specificity. Vendors should not just say their tool improves productivity. They should show how it improves productivity in a defined environment, for a defined role, and with defined usage assumptions. That is the difference between an interesting AI demo and a credible buying guide process.
Why Readiness Matters Before Vendor Shortlisting
One reason so many AI buying processes drift is that organisations jump into automation platform evaluation before they understand their own readiness. They shortlist suppliers first and only later realise they have not aligned stakeholders, defined workflows, checked governance constraints, or decided how they will measure success. By then, the conversation is already distorted by the vendor narrative.
Microsoft’s current Copilot onboarding guidance offers a useful example of what good readiness can look like. The company explicitly recommends that enterprises use its Microsoft 365 Copilot Optimization Assessment before deployment to evaluate data governance maturity and data security controls. This is not just a technical pre-check. It shows that organisations should shape adoption, licensing, and governance decisions through readiness, not leave them until after the deal is signed.
Microsoft’s guidance also separates readiness into specific stages: get the organisation ready, choose the right licence, prepare the apps and network, assign licences, and then drive adoption. Even if a buyer is not selecting Microsoft, that sequencing is valuable. It shows how AI workplace tools need more structured preparation than a standard SaaS purchase.
What Readiness Really Means
In practice, readiness usually means three things. First, the organisation needs clarity on which workflows matter most. Second, it needs alignment on the guardrails, especially around data, oversight, and admin control. Third, it needs a realistic understanding of who will use the tool, how often, and under what licence model. Without that, even the best procurement process can still lock in waste.
How Can Buyers Evaluate Automation ROI Claims?
Buyers should evaluate automation ROI claims by testing the logic behind them, asking for role-based evidence, and separating assistive gains from orchestration gains.
This is where many enterprise buying processes get fuzzy. AI vendors often talk about hours saved, faster output, or improved productivity, but those claims are not always based on the same assumptions. One supplier may count time saved drafting a recap. Another may talk about workflow orchestration that reduces handoff delays. Another may include avoided spend from licence consolidation or fewer manual steps in service operations. Those are not equivalent gains, and procurement should not treat them as if they are.
A more credible AI ROI assessment starts by asking what type of value is actually being promised. Common value points include time savings for the user, improved throughput for a team, better collaboration quality, or reduced cost per workflow.
Microsoft’s own ecosystem is quietly acknowledging the need for more structured modelling here. Its Microsoft 365 Copilot and Chat Value Envisioning Tool is designed to help organisations evaluate licensing requirements, usage costs, and expected business impact before they scale deployment. That is a useful signal for buyers more broadly. Even the largest vendors know that AI procurement now needs a value case, not just a product pitch.
“This powerful tool enables businesses to seamlessly evaluate, strategize, and optimize their Copilot deployment by providing comprehensive insights into licensing requirements, usage costs, and expected business impact.”
How to Challenge the Maths
Procurement teams can use that logic in any RFP. Ask vendors to state exactly how they model business impact, which roles they benchmarked, what level of adoption they assume, and what counterfactual they are comparing against.
Most importantly, ask them to distinguish between value from simple assistance and value from deeper workplace automation. The former may be easier to deploy. The latter may create more significant gains, but only if the architecture and governance are mature enough.
Who Should Be Involved in Buying AI Workplace Tools?
Buying AI workplace tools should involve procurement, IT, security, business owners, employee experience or HR stakeholders, and the teams responsible for adoption and change management.
Too many enterprise AI buying processes still begin and end with a small technical team or a single business sponsor. That rarely works well. Productivity tools sit too close to the daily work of employees, too close to business systems, and too close to sensitive data for a narrow buying group to make a sound decision alone.
Procurement should shape the commercial model and challenge vendor claims. IT should assess architecture, integration depth, and admin controls. Security and governance teams should examine permissions, oversight, logging, and data boundaries.
Business leaders should define where the tool needs to create value. HR or employee experience stakeholders should stress-test the adoption and trust implications. Finally, whoever owns rollout and enablement needs to be involved early, not after the contract is done.
This cross-functional approach matters because AI tools can succeed technically and still fail operationally. A platform may integrate perfectly, yet underperform because employees do not trust it, managers do not know how to measure success, or licensing decisions were made without understanding actual user demand. In other words, procurement can reduce deployment risk, but only when it links to readiness, governance, and adoption from the start.
What Governance Controls Should Be Assessed?
Enterprise buyers should assess governance controls around data access, identity, permissions, auditability, model boundaries, admin policy controls, and human oversight.
Governance is now one of the biggest differentiators in Unified communications AI procurement. It is no longer enough for a vendor to say the system is secure. Buyers need to understand how the AI behaves inside real workflows, what data it can touch, and what controls administrators have once it is live.
