Artificial Intelligence (AI) has become a foundational technology for enterprise digital transformation. In the digital construction sector, where data is abundant and project margins are tight, AI is redefining how organisations plan, design, build and operate.
Yet, despite soaring adoption, companies are struggling to make AI cost-effective. A recent Mavvrik survey of 372 enterprise organisations found that 85% cannot forecast their AI costs within a 10% margin, with nearly a quarter exceeding budgets by more than 50%. This financial unpredictability is fuelling a critical debate: should businesses rely on accessible public AI models, invest in secure private AI alternatives, or combine both through hybrid AI strategies?
AI adoption is accelerating—but budgets are falling behind
According to Stanford University’s 2024 AI Index Report, business use of AI has surged to 78% of organisations, up from 55% the previous year. Corporate AI investment reached $252.3 billion worldwide, reflecting how essential the technology has become.
However, the financial risks of AI adoption are intensifying. In digital construction, where Building Information Modelling (BIM), generative design, and predictive maintenance rely heavily on data, mismanaging AI costs can erode project profitability. Public AI may seem attractive for speed and scalability, but uncontrolled usage-based fees are leaving companies exposed.
Public vs private AI: understanding the divide
The central challenge is recognising the differences between public AI models and private AI platforms, and how they affect security, costs, and control:
- Public AI models are accessible, cost-efficient entry points for experimentation and scale. They excel in general tasks but carry risks around data privacy, compliance, and unpredictable costs.
- Private AI platforms are secure, custom-built environments designed to safeguard sensitive information and deliver precision. They require upfront infrastructure investment but provide predictable cost structures and full control over data and outputs.
- Increasingly, enterprises are adopting hybrid AI strategies, combining public and private models to balance accessibility, scalability, and security. In fact, Mavvrik reports that 61% of companies now run AI workloads across both environments.
- Security, cost and control: The three critical differentiators
Data security
Security remains the most pressing concern for enterprises. Cloudera research shows that 53% of organisations cite data privacy concerns as their top barrier to AI adoption. Public AI models operate on broad, external infrastructures with varying levels of data governance. In contrast, private AI platforms offer closed, restricted environments where data never leaves the enterprise perimeter.
Cost predictability
Public AI is typically billed on usage, creating a “metered” cost structure that is difficult to forecast. Construction firms running thousands of generative design queries or scheduling optimisations may find themselves overspending rapidly. Private AI, while requiring higher upfront investment, mirrors traditional IT cost structures and enables accurate long-term budget planning.
Control and competitive advantage
Public AI delivers generic outputs optimised for wide audiences. Private AI allows organisations to embed proprietary datasets, unlocking competitive differentiation in construction workflows such as predictive safety monitoring, energy modelling, or asset lifecycle optimisation. This level of customisation can transform AI from a cost centre into a strategic asset.
Žilvinas Girėnas, Head of Product at nexos.ai, emphasises that successful enterprises are moving beyond the binary choice: “The debate over public versus private AI comes down to a simple question: Is your data an asset or a liability? When you feed proprietary information into a public model, you risk turning your most valuable asset into a liability. A private AI strategy flips that script—treating your data like the crown jewels and enabling high-precision, secure solutions. But the most advanced enterprises combine both approaches, guided by a governance framework that aligns technology with strategy.”
Practical steps for enterprises adopting AI
For organisations in digital construction and beyond, balancing AI cost control, data governance, and scalability requires a structured approach. Girėnas highlights three non-negotiables:
- Classify your data before choosing your AI approach
Identify whether your AI use cases involve sensitive data, customer information, or regulated project content. Public AI may suit general tasks, but private AI is essential for compliance-driven environments.
- Implement unified cost visibility
Deploy real-time monitoring tools that track AI spend across public APIs, private infrastructure, and hybrid deployments. This prevents runaway costs and allows executives to forecast AI budgets with confidence.
- Build audit trails from day one
Introduce logging systems that capture every AI query, output, and decision, linking interactions to specific users and timestamps. This provides accountability for compliance while enabling continuous improvement in AI performance.
Looking ahead: AI in construction strategy
As AI becomes deeply embedded in digital construction, organisations must view the public vs private AI debate not as an either/or decision but as part of a broader digital strategy. Firms that treat data as a strategic asset, prioritise security, and establish governance frameworks will achieve cost predictability while unlocking competitive advantage.
The construction sector, already grappling with productivity, sustainability, and cost pressures, cannot afford uncontrolled AI spending or data risks. By adopting a hybrid AI strategy, companies can harness the scalability of public AI while protecting their most valuable information through private platforms.
The lesson is clear: in the era of digital construction, success with AI depends not just on technology adoption but on strategic cost management, robust governance, and intelligent deployment across both public and private environments.
Read next: UK construction performance plummets – Build in Digital
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