AI is already embedded inside most organisations. It spreads the way spreadsheets once did, often ahead of formal policy. Employees summarise contracts, analyse reports, generate code, draft communications, and interpret dashboards using AI tools embedded in browsers, SaaS platforms, and productivity suites. The strategic question is no longer whether AI is being used. It is whether that use is visible, governed, and scalable, or fragmented, unmeasured, and risky.
For early adopters, the initial challenge was building trust and encouraging employees to use AI tools. Today, the challenge, both for organisations that actively deploy AI and for those that hesitate, is governance and coordination. AI is already part of everyday business operations, whether companies formally acknowledge it or not. What remains undecided is whether its use stays fragmented or evolves into an enterprise capability defined by structure, visibility, and accountability.
Shadow AI is initiative moving faster than governance, and that is predictable.
AI usage now extends across marketing, operations, engineering, finance, and customer support. Teams use AI to compress cycle times, increase throughput, and improve output quality. Early results are often positive. Reports are drafted faster. Code is generated more efficiently. Customer communications become more personalised.
Yet as usage expands, leadership visibility declines. Tools are acquired at departmental level. APIs are connected independently. Assistants and agents are configured locally. Budget impact disperses across cost centres. What emerges is not chaos, but decentralisation, without an operating model.
McKinsey’s research consistently finds that organisations capturing durable value from AI build structural capabilities around integration, governance, and measurement. AI leaders are distinguished less by experimentation and more by operating discipline.
The gap, therefore, is coordination. AI adoption creates value at team level. Scaling that value requires alignment at organisational level.
Shadow AI presents itself as productivity. An analyst copies data into an external tool to accelerate a report. A developer integrates an LLM API to streamline documentation. A marketing team uses generative AI to improve campaign performance. Each action is rational within its context.
Zylo’s coverage of Shadow AI illustrates how decentralised SaaS adoption, combined with embedded AI capabilities, reduces visibility across enterprises. Tools proliferate quickly because they deliver immediate utility.
The benefits are visible immediately. Output increases. Delivery speeds up. Innovation gains momentum. Costs diffuse across departments. Risk compounds gradually. Nothing appears broken. This is precisely why Shadow AI expands.
Fragmentation manifests first in security and compliance. Data flows into external tools without consistent access controls. Model interactions lack central traceability. Auditability weakens in regulated environments. The threat does not arise from malice, but from helpful acceleration.
Second, fragmentation obscures cost. AI workloads run outside shared monitoring frameworks. Inference expenses accumulate across teams. Overlapping tools solve similar problems without consolidation. Financial visibility becomes partial rather than comprehensive, and optimisation becomes guesswork
Third, fragmentation constrains scalability. Solutions that perform well in local contexts resist industrialisation. Standards for prompts, models, and evaluation diverge. Data platforms feed inconsistent pipelines. What works for one team remains difficult to reuse across others.
Shadow AI appears most prominently in organisations that are already digitally advanced. Cloud-native environments, modern data platforms, and agile delivery cultures create fertile ground for AI experimentation. Teams possess both the tools and the incentive to move quickly.
Governance structures, however, evolve more slowly than operational capacity. Ownership between IT, security, data, and business leadership requires clarification. AI capabilities embed into everyday systems faster than operating models adapt. The result is predictable: capability grows faster than control.
Governance functions as infrastructure. It provides the structure within which AI can scale safely and predictably.
Effective AI governance provides visibility into where AI operates, who uses it, what data it accesses, and how performance and cost evolve. It establishes shared standards for integration, access, and evaluation. It converts decentralised experimentation into structured capability. At minimum, this means being able to:
McKinsey’s work underscores that scaling AI depends on embedding it into processes, data platforms, and organisational structures. Governance is what converts isolated gains into enterprise impact.
In practical terms, governance requires an operating layer where assistants, agents, and models run within managed environments. Data access follows defined permissions. Usage is observable. Cost is measurable. Performance links to business outcomes.
Transitioning from Shadow AI to strategic AI typically requires an enabling layer that brings visibility, governance, and orchestration across distributed AI usage. Platforms such as Alteus act as this resolution layer, providing a unified AI operating environment where teams deploy assistants and agents, connect them securely to enterprise data sources, control access, and monitor usage, costs, and performance across departments.
When organisations address Shadow AI coherently, the operating model changes in practical, business-facing ways: fewer tools and vendors to manage, more leverage from what remains; standardised data foundations that support multiple use cases; reusable AI components rather than isolated initiatives; and clear ownership and accountability for risk, cost, and outcomes.
In organisations where AI becomes strategic, this operating layer sits alongside mature data engineering and cloud-native foundations and integration practices. This way, AI becomes an enterprise capability that can be scaled, measured, and improved over time. The goal is not to suppress local initiative, but to channel Shadow AI into a coherent operating model. From tool to system, from experimentation to structure.
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Interested in how we can help? Explore Alteus at alteus.ai
Emil Calofir is the Head of AI at eSolutions, where he leads the integration of AI solutions across a range of projects, with a strong focus on Generative AI and building practical, scalable capabilities that support business needs. With a software architecture background and broad industry exposure, he helps teams translate complex requirements into robust, production-ready systems.
Earlier in his career, Emil worked in mobile game development before moving into software engineering roles, contributing to projects at Deutsche Bank and Orange Romania, particularly in API development, microservices, and data warehousing.
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