Thanks to OpenAI’s early consumer push, the generative AI era that reshaped work life began in plain sight — and business users have kept voting with their keyboards. What started as a viral consumer tool has become a persistent presence inside enterprises, while legacy software vendors and cloud giants scramble to embed enterprise-grade copilots into core workflows. The result is a three-way contest: consumer-first models (ChatGPT, Claude, Perplexity), platform incumbents (Microsoft, Salesforce, SAP) and security-first enterprise vendors (Cohere, specialist regional players). Each camp claims victory on one metric or another — ease of use, ecosystem reach, or data governance — but the real winners so far are the users who now have more AI choices than ever. The central question for IT leaders and CIOs is no longer “if” but “which AI fits which use case, under what controls, and at what cost to privacy, compliance and long-term vendor lock-in.”
When OpenAI opened ChatGPT to the public at no charge, adoption exploded: within approximately two months the model was estimated to have crossed the 100‑million monthly‑active‑user threshold — a record pace compared with major consumer apps. This rapid adoption was documented by industry research and coverage at the time and used as proof of the pent-up demand for conversational AI. (arstechnica.com)
The immediate aftermath created two parallel forces:
Background / Overview
When OpenAI opened ChatGPT to the public at no charge, adoption exploded: within approximately two months the model was estimated to have crossed the 100‑million monthly‑active‑user threshold — a record pace compared with major consumer apps. This rapid adoption was documented by industry research and coverage at the time and used as proof of the pent-up demand for conversational AI. (arstechnica.com)The immediate aftermath created two parallel forces:
- A wave of employee experimentation — often unsanctioned — that security teams call shadow AI (an extension of the older “shadow IT” problem).
- A fast enterprise reaction: major software vendors introduced embedded AI copilots designed to sit inside business apps and operate within corporate governance controls. Notable launches: SAP’s Joule (announced September 27, 2023), Microsoft’s Copilot family (made broadly available to enterprise customers starting November 1, 2023), and Salesforce’s Einstein Copilot (general availability announced April 25, 2024). (techcommunity.microsoft.com, openai.com, techcommunity.microsoft.com, salesforce.com)
Partnership plays, sovereign data and the rise of “security‑first” providers
SAP + NVIDIA: bringing an enterprise AI stack together
SAP’s strategy includes using specialized partners to deliver enterprise AI capabilities. In March 2024 SAP publicly expanded its partnership with NVIDIA to combine SAP Business AI and NVIDIA’s AI Foundry and NIM microservices to accelerate fine‑tuning and on‑prem/cloud deployment models for enterprise customers — a clear bet on giving customers choices around where data and models reside. That arrangement reinforces SAP’s pitch: customers can run domain‑tuned models while keeping data under their control.Cohere and the “security‑first” pitch
Cohere has positioned itself as a vendor focused on enterprise requirements — data residency, private deployments, and model choices tailored to corporate needs. Cohere attracted strategic investments and partnerships (including major AI ecosystem players) and explicitly markets itself as an alternative to repurposed consumer models for regulated businesses. Those claims are supported by press coverage of funding rounds and vendor partnerships. (ft.com)
Local sovereign offerings: a market wedge
Regional players — for example, UK‑based OneAdvanced — are carving systems that promise to keep customer data in national jurisdiction and to comply with local law. For public sector organisations and healthcare customers, data sovereignty is becoming a procurement checklist item; suppliers who guarantee in‑country hosting and local controls can win contracts where global cloud options prompt regulatory concerns. OneAdvanced explicitly markets private, UK‑hosted AI services aimed at NHS and university customers. (myworkplace.helpdocs.io)
Product comparisons: experience, compliance, cost and extensibility
Ease of use vs governance — a four‑axis tradeoff
When selecting an AI assistant for knowledge workers, decision-makers should weigh at least four dimensions:- User experience and speed (how fast and easy is it for a knowledge worker to get results?
- Data protection and auditability (are prompts and context logged, where is the data stored, and can it be excluded from training?
- Integration depth (can the assistant read and act on ERP/CRM data and metadata?
- Total cost of ownership and vendor lock‑in (licensing, compute costs, and dependency on a single vendor).
Developer and AI‑ops concerns
Beyond individual use cases, enterprises evaluate copilots by how they accelerate software delivery (code suggestions, debugging, auto‑generation) and how well they translate natural language into structured queries (e.g., SQL generation for analytics). Vendors emphasize that embedded copilots reduce friction by exposing LLMs inside development and reporting workflows — but CIOs worry about reproducibility, model drift, and ongoing oversight requirements.
Enterprise strategy: pragmatic recommendations for decision‑makers
- Start with classification: triage tasks by data sensitivity. Reserve external consumer models for non‑sensitive creative or exploratory work; mandate enterprise copilots for regulated documents and client data.
- Pilot with real metrics: measure time saved, error rates, and policy violations. Track adoption and the sources of “shadow AI” to decide whether to expand or lock down.
- Insist on audit trails and exportability: ensure AI outputs and prompts can be logged, exported, and, if necessary, removed in compliance with subject access or litigation holds.
- Negotiate IP and training clauses: confirm whether vendor contracts permit model training on customer data and insist on clear rights to derivative outputs.
- Invest in AI literacy and a governance playbook: shadow AI is as much a people problem as a technology one. Training, incentives and clear policy will reduce risky behaviour.
Market signals and what the recent deals reveal
OpenAI’s enterprise push and government deals
OpenAI has aggressively moved into enterprise territory: ChatGPT Enterprise offered controls and guarantees that customer data wouldn’t be used to train models. Beyond commercial contracts, OpenAI’s 2025 memorandum of understanding with the UK government — signed July 21, 2025 — shows how strategic the competition has become. That MoU opens exploratory paths for using advanced models in public services and signals governments’ appetite to partner with AI labs — but it also raises questions about oversight, procurement transparency and the conditions under which public data might be used. The MoU is explicitly non‑binding, but it underlines the size of the prize for vendors that secure public sector trust. (gov.uk, microsoft.com, openai.com, gov.uk, ft.com)
Source: Business Reporter https://www.business-reporter.co.uk/digital-transformation/which-ai-is-winning-the-race-for-business-users/
