Shane Cooper | Head | Digital Advisory | Forvis Mazars South Africa | mail me |
According to our latest C-suite barometer, business leaders entered 2026 with both confidence and caution. Notably, 92% of executives reported a positive growth outlook for their organisations.
At the same time, Artificial Intelligence (AI) emerged as the most influential external trend. Specifically, 40% of respondents identified it as the key factor shaping business over the next 12 months. In parallel, 39% ranked IT and technology transformation as the top strategic priority for the next three to five years.
AI readiness lags behind ambition
Taken together, these findings tell a clear story. Organisations are moving from AI ambition to AI readiness as a central strategic concern.
AI has shifted from experimentation to a core boardroom issue. It now sits alongside growth, competitiveness and transformation. However, the broader environment remains complex. Leaders must navigate economic pressure, rising competition, regulatory demands and persistent uncertainty. Despite this, they continue to invest in growth.
This combination of optimism and instability creates a governance challenge. While enthusiasm for AI is widespread, readiness to use it effectively remains uneven. In other words, many organisations are still transitioning from AI ambition to AI readiness.
The barometer highlights this tension. For the first time, it measures confidence in AI readiness separately. It reports that 94% of executives believe their organisations are prepared for AI. However, the same report signals weaker confidence in talent-related areas. It also emphasises the need for strategic intervention to ensure responsible AI use. In addition, it identifies data security as a critical investment area within the broader technology shift.
Delivering measurable business value
Given these dynamics, boards must ask a more rigorous question. They should move beyond whether the organisation uses AI. Instead, they must assess whether it can deploy AI safely, responsibly and with measurable value.
A credible answer requires more than executive confidence. It demands a structured view of readiness across key operating conditions. These conditions determine whether AI initiatives scale or fail.
Typically, these factors receive little attention. They include strategic clarity, data quality, infrastructure, governance, skills, adoption, security and value measurement.
If organisations ignore these foundations, even well-funded programmes can fail. They often fragment into disconnected pilots with unclear accountability and weak returns. Notably, this is where the gap between AI ambition and AI readiness becomes most visible.
Assessing AI readiness
Reaching a reliable conclusion requires a robust AI readiness assessment. Importantly, this assessment must go beyond technology alone. A business may access advanced tools and still lack readiness.
For example, it may not have a board-approved roadmap. It may lack executive sponsorship or reliable and accessible data. In addition, it may not have controls for explainability and bias. It may also lack the right talent mix or the willingness to adopt new ways of working. Finally, it may not track business value effectively.
A strong assessment framework can expose these gaps. It uses weighted scoring, multi-dimensional evaluation, and benchmarking design. As a result, it delivers tailored insights for different stakeholders.
For executives and non-technical leaders, it provides clear strategic insights. For CIOs and technical leaders, it offers advanced metrics. For AI Centres of Excellence, it delivers frontier-level capability assessments.
These differentiated insights matter. AI readiness varies depending on the audience. Boards require visibility into strategy, governance, risk and value. Technology leaders need evidence on architecture, MLOps, data lineage, scalability and deployment discipline. Innovation teams must assess whether advanced capabilities can scale without weakening controls.
A credible readiness tool must address all these dimensions. At the same time, it must avoid becoming superficial or overly complex. This balance is essential when moving from AI ambition to AI readiness.
Unlocking enterprise-wide capabilities
Boards must demand this level of discipline. AI has become too critical to manage as an informal innovation stream. Instead, organisations must treat it as an enterprise capability. It affects growth, risk, operating models and capital allocation.
The barometer reinforces both timing and urgency. The overall investment index has reached 69%, its highest level since 2022. Leaders are increasing investment across core business areas. In addition, the report identifies AI implementation as the greatest opportunity within ongoing technology transformation. This creates both opportunity and risk.
Organisations that assess readiness early gain a strategic advantage. They prioritise the right use cases and align governance sooner. They also allocate capital more effectively and avoid fragmented initiatives.
An internal diagnostics tool
In contrast, organisations that skip this discipline face two risks. They may move too slowly and lose competitive ground. Alternatively, they may move too quickly and create uncontrolled activity. Neither outcome is desirable. One leads to stagnation, while the other creates costly inefficiency.
Boards can address this challenge by asking practical questions:
- Do we have a board-backed AI roadmap linked to clear business outcomes?
- Are our priority use cases tied to measurable value?
- Is our data reliable enough to support AI? Can infrastructure scale safely and cost-effectively?
- Are governance, compliance and security controls aligned with risk?
- Do we have the talent, training and culture to embed AI into operations?
- Can we demonstrate value creation, not just technical activity?
In conclusion
AI readiness assessments provide a structured internal diagnostic. They help leadership teams test whether confidence aligns with operational reality.
This alignment delivers strategic value. It shapes board discussions, informs transformation planning, and supports vendor evaluation. It also establishes benchmarks for progress and identifies where advisory support can add value. Ultimately, many organisations operate in a landscape where ambition exceeds capability. However, those who successfully move from AI ambition to AI readiness will govern more effectively. They will invest more intelligently and scale with greater control.
By contrast, organisations that confuse confidence with preparedness risk failure. Over time, they may discover that optimism alone cannot sustain an operating model.
