Every major enterprise software vendor is selling AI in 2025. The pitch is compelling, the demos are impressive, and the price tags are significant. Microsoft Copilot at $30/user/month. Salesforce Einstein at $75/user/month for the full Einstein 1 tier. ServiceNow Now Assist at $15–$25/user/month on top of your existing ITSM license. Workday AI capabilities bundled into premium tiers.
The question every CFO and IT leader should be asking is not “is AI powerful?” — it clearly is. The question is: “Does this specific AI product, at this specific price, generate measurable value in my organization’s specific context?” That is a harder question, and vendors are not incentivized to help you answer it honestly.
This guide is an attempt to provide that honest answer, drawing on available ROI research, public customer case studies, and a clear-eyed view of what these products do and don’t do in real enterprise environments.
Microsoft 365 Copilot: $30/User/Month — What You Get and What It Takes to Get Value
Microsoft 365 Copilot integrates generative AI into Word, Excel, PowerPoint, Outlook, Teams, and Loop. It can draft documents, summarize meeting recordings, generate presentations from prompts, analyze spreadsheet data using natural language, and draft email replies.
The price: $30/user/month, which requires a qualifying Microsoft 365 license (E3, E5, Business Standard, or Business Premium). This is $360/user/year — for a 500-user deployment, $180,000 annually.
Where Copilot demonstrably works: Microsoft’s own internal research (published with appropriate caveats about self-reported productivity) suggests Copilot users save 1.2 hours per week on average. If you value that time at even $50/hour, the annual value per user is $3,120 — more than 8× the $360/year license cost. Microsoft has published customer case studies showing measurable reductions in meeting preparation time, email processing time, and document drafting time.
Where Copilot struggles to deliver measurable ROI: The 1.2 hours/week figure is an average across all user types. For users who don’t spend significant time in Word, Excel, and Outlook, the realized productivity gain is much lower — sometimes approaching zero. A manufacturing plant operations manager who spends most of their day on the floor, in SAP, or in Zoom has fewer Copilot touchpoints than a management consultant drafting documents all day.
The practical implication: Copilot ROI is highly usage-profile-dependent. Organizations that deploy Copilot to their highest-consumption Microsoft 365 users — strategy teams, finance analysts, HR business partners, marketing teams — will see meaningfully different returns than organizations that deploy it uniformly across all roles.
What Copilot requires to function: The AI is only as useful as your Microsoft Graph data — which means your SharePoint content needs to be organized, your Teams meetings need to be recorded and transcribed, and your email needs to be in Exchange Online. Organizations with messy SharePoint architectures, extensive on-premises file shares not synced to SharePoint, or Teams adoption below 70% get meaningfully less from Copilot than the Microsoft demos suggest.
The right procurement approach: Pilot Copilot with 50–100 of your highest-intensity Microsoft 365 users for 90 days. Measure actual time saved on specific, pre-identified task types. Calculate the productivity value. Then decide whether the economics support broader rollout. This discipline separates organizations that capture Copilot value from those that spend $180,000/year on an underutilized line item.
Salesforce Einstein AI: What $75/User/Month Buys in Einstein 1 Sales
Salesforce’s AI story has become Einstein Copilot, bundled into the Einstein 1 Sales tier at $500/user/month — which includes Salesforce Enterprise capabilities, Data Cloud, Revenue Intelligence, and the full Einstein Copilot generative AI assistant.
If your organization is on Salesforce Enterprise ($165/user/month), the upgrade to Einstein 1 Sales represents a 203% per-user price increase. At 100 sales reps, that’s moving from $198,000/year to $600,000/year — a $402,000 annual increase to unlock AI capabilities.
What Einstein Copilot actually does: Within Salesforce, Einstein Copilot can summarize account history, draft follow-up emails from CRM data, generate call summaries from integrated conversation intelligence, surface deal risks based on engagement patterns, and answer natural language questions about pipeline data. The underlying models are a combination of Salesforce’s own AI models and third-party LLMs processing customer data through Salesforce’s zero-data-retention (ZDR) architecture.
The ROI case for Einstein 1: The strongest ROI case is for organizations where: (a) sales reps spend significant time on CRM data entry and post-call documentation, (b) deal cycles are long and account history is complex enough that AI-assisted research saves meaningful time, and (c) the Einstein 1 Data Cloud capabilities unify customer data across marketing, service, and sales in ways that create revenue-generating insights. For large enterprise sales organizations with 200+ reps running complex deals, the pitch is coherent.
Where Einstein 1 is probably overpriced: For inside sales teams running high-velocity, transactional deals where call cycles are short and CRM records are simple, the Einstein 1 premium is difficult to justify. $500/user/month for a 20-person inside sales team is $120,000/year — at that scale, the Einstein AI features are likely to be underutilized relative to the investment.
Salesforce also offers Einstein features in lower tiers — Einstein Activity Capture (automatic email and calendar logging), Lead Scoring, and Opportunity Insights are available in Enterprise at no additional cost. The incremental Einstein value at the $500/user Copilot tier is specifically the generative AI conversational interface and Data Cloud integration. Evaluate those specific capabilities against your sales workflow before deciding the upgrade is warranted.
ServiceNow Now Assist: $15–$25/User/Month — ITSM AI That’s Actually Useful
ServiceNow’s generative AI layer, Now Assist, is arguably the most mature and immediately practical of the enterprise AI add-ons discussed here, because its use cases are narrow, specific, and clearly valuable in high-volume service environments.
Now Assist for ITSM enables: AI-generated case summaries (automatically summarizing incident history for agents picking up tickets), intelligent search (natural language queries against the knowledge base), predictive routing (AI assignment of incidents to the right team), and Virtual Agent AI conversations (deflecting common requests through conversational AI before they reach a human agent).
At $15–$25/agent/month on top of existing ITSM licensing, Now Assist’s economics are straightforward to model for organizations with measurable ticket volumes:
A 200-agent contact center processing 50,000 tickets/month. If AI-powered Virtual Agent deflects 15% of tickets (7,500 per month), and each deflected ticket would have cost 12 minutes of agent time at a $30/hour all-in agent cost, the monthly savings are: 7,500 tickets × 0.2 hours × $30 = $45,000/month. Now Assist license cost for 200 agents at $20/agent: $4,000/month. ROI in this scenario: more than 10× the license cost.
The deflection rate assumption matters enormously in this model. ServiceNow quotes 15–30% deflection rates in case studies. Real-world deflection rates for organizations at the beginning of their AI journey typically run 5–15% until the knowledge base is sufficiently populated and the Virtual Agent flows are well-configured. Plan conservatively and build to the higher rates over 12–18 months.
The Honest Framework for Evaluating Enterprise AI Add-Ons
Three questions to answer before signing any enterprise AI add-on contract:
1. Can I measure the specific outcome this AI is supposed to drive? If the value proposition is “saves time,” you need to know whose time, on which specific tasks, and how many hours per week. If you can’t answer those questions before the pilot, you can’t verify ROI after it.
2. What adoption rate is the ROI model built on? Vendor ROI calculators assume 80–90% feature adoption. Real enterprise software adoption rates for new AI features typically start at 30–50% and grow over 12–18 months of enablement and change management investment. Build your ROI model on the adoption rate you can realistically achieve in year one, not the theoretical maximum.
3. What happens to the ROI if the adoption assumption is halved? Stress-test the model. A $30/user/month AI product that generates $100/user/month in value at 80% adoption generates $62.50/user/month in value at 50% adoption — still positive. A product that barely passes ROI at 80% adoption fails at 50%. Know which scenario you’re in before you commit to organization-wide rollout.