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From Data Confusion to AI Confidence

Data Is the Foundation of Trustworthy AI
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Global Insights on Data Confidence

AI PULSE SURVEY | VOL 2

9 min read

Data isn’t just fuel—it can also be friction. The latest AI Pulse Survey reveals that the biggest barrier to AI success isn’t technology—it’s trust. Organisations that outperform in AI are those that trust their data, govern it well, and empower their teams to use it confidently. This report highlights how data confidence drives ROI, where gaps persist, and what leading organisations are doing to turn complexity into clarity.

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What your peers are saying about AI Adoption

Key Findings

AI Maturity Stages Defined

These 5 stages of maturity were leveraged by respondents to benchmark their status.

Scaling AI? Scale your data confidence too

Data Confidence by AI Maturity

Confidence in data: a maturity marker

Key takeaways

  • Progress with AI closely correlates with the quality and management of data.
  • Early-stage AI adoption often begins with not-fit-for-purpose, incomplete or imperfect datasets, which are refined over time through iterative processes.
  • As organisations mature, their data practices become more structured and intentional.

Data Confidence by Industry

Technology sector leads the way in data confidence

Key takeaways

  • A strong culture of innovation and early AI adoption – along with easy access to digital infrastructure and fewer regulatory barriers – gives the technology sector a distinct competitive edge over other sectors.
  • Financial services data trust variability is significant in a sector where minor data issues can lead to serious consequences.
  • Retail and consumer packaged goods confidence levels are mixed which may reflect the challenges of managing large volumes of customer and supply chain data across multiple channels.

Data Confidence Pays Off

Organisations confident in their data are 3x more likely to exceed AI ROI expectations

Key takeaways

  • Data confidence grows with maturity.
  • This confidence is built through a combination of governance, training, and transparency—not just technical infrastructure.
  • As organisations mature, they become better equipped to manage and trust their data, which directly fuels AI success.

Bias Awareness Evolves with Maturity

Stage 1 and Stage 5 both report low bias—but Stage 1 likely isn’t seeing it, while Stage 5 is actively reducing it.

Key takeaways

  • Bias is always present; but recognition, detection and mitigation depends on data literacy and maturity. As organisations progress, they develop the literacy and frameworks needed to identify and reduce bias—transforming it from a hidden risk into a managed variable.
  • Stage 5 organisations mitigate bias proactively through governance and transparency.
  • Early-stage organisations may need better tools and frameworks to identify bias. Their relatively simple use cases may also factor into making bias less likely.

Data dragging you down? Biggest hurdles to AI optimisation

Data Challenges by AI Maturity

From start to scale: security and tech gaps hold AI back

Overcoming Data Challenges

Train hard, audit often, scale smarter 

Meet the minds behind the report and insights

Peter Mottram

Peter is a Managing Director in Protiviti’s Technology Consulting Practice and leads the Enterprise Data and Analytics (ED&A) solution globally. He has more than 20 years of experience in data and analytics including IT, information management strategy, data management, data security & privacy, regulatory compliance, master data management, business intelligence and advanced analytics solutions. Most of his career has been focused on Financial Services including clients such as MUFG, SunTrust (now Truist), JPMC, Wells Fargo, Morgan Stanley, Comerica and many others.

"When it comes to launching AI initiatives, waiting for perfect data can be a major roadblock. The truth? Progress starts with imperfection. Here are five reminders to help your team move forward confidently - even when the data isn't flawless."
Read "Avoiding the perfect data trap: five key reminders for AI success" in the full report

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Matt McGivern

Matt is a Managing Director in Protiviti's Information Technology Consulting group where he leads Protiviti's Global BI and Data Governance solution area. He has more than 18 years of experience in information technology, financial services and project management. He has worked in professional services for the last 15 years, focusing on data warehousing, financial and management reporting, project management and full lifecycle software development. He has also completed major projects focused on financial and management reporting, business intelligence and general management consulting.

At Protiviti, he is focused primarily on business intelligence, strategy and technology projects. He is the Global Lead for Protiviti's Information practice, covering BI, Data Governance, and Data Warehousing.

“Investments in data quality must reach front-end systems — where trust is often weakest. Strengthening these entry points may be the best opportunity to build lasting confidence from the ground up.”

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Coming September 30 - Pulse #3 Results

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Findings from AI Pulse Survey Vol. 1

 

Key Findings

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Stages of AI Adoption and Maturity

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The payoff is in the progression of AI maturity.

Key takeaways

  • There’s a strong case for continued AI investment and scaling, especially if early results are promising.
  • High initial investments in AI can delay returns in early stages of adoption, making it vital to set realistic ROI expectations in the early stages and demonstrate the potential to scale over time.
  • Enhancing AI capabilities systematically can improve ROI. Organisations should develop a roadmap to progress through AI maturity stages, focusing on scaling AI applications, improving data infrastructure, and investing in AI talent.
  • While the transformation stage is rare across all sectors, there’s a significant opportunity for first movers to gain competitive advantage by using AI not just for efficiency, but for innovation and market leadership.

AI Maturity drives varied challenges from use case confusion to data hurdles.

Key takeaways

  • Integrating AI with legacy systems is challenging due to incompatible data formats, outdated architecture, and limited API capabilities. These challenges peak in the middle stages of AI adoption, suggesting that developing a robust integration strategy early can alleviate issues as AI deployment scales. Fixing the issue requires a holistic approach, considering data compatibility, system architecture, change management, and more.
  • Quality data is crucial for AI success, yet often overlooked early on. Without proper data, infrastructures fall short. As projects mature, data gaps and challenges become evident. Organisations should prioritise data availability and quality from the outset by assessing data needs during the governance and approval process and ensuring that data is resilient and secure. Continuous monitoring and updating of data strategies will enhance project success. Additionally, infrastructure must support the seamless integration and management of data.
  • Ongoing learning and adaptation are essential for understanding AI use cases. Organisations should remain agile and refine their AI strategies as they progress through AI maturity stages.

AI maturity, industry and role drive AI success metrics.

Key takeaways

  • Cost savings and employee productivity are the most frequently cited key indicators of AI success, underscoring the universal importance of reducing costs and improving workforce efficiency.
  • An organisation's maturity stage influences which indicators are perceived to be the most important for AI success. As organisations progress, the focus shifts from cost savings and process efficiency to productivity, efficiency, and growth.
  • Establish a continuous feedback loop to monitor AI performance and make necessary adjustments. Consider how AI projects will contribute to innovation and competitive advantage over time. Involve all relevant stakeholders early to ensure buy-in and set realistic expectations.

Build base capabilities to climb the maturity curve.

Key takeaways

  • People: Train and upskill the workforce to address AI literacy and technical skills gaps. Business leaders should continuously learn and adapt.
  • Process: Set clear use cases, measurable goals, and transparent performance measures. Involve key stakeholders early and develop robust data governance practices.
  • Technology: Ensure tools integrate with existing systems, are scalable, and secure. Design adaptable infrastructure and use monitoring tools for iterative improvements.
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