We recently surveyed nearly 700 AI practitioners and leaders around the world to uncover the biggest obstacles AI teams face today. What emerged was a troubling pattern. Almost half (45%) of respondents lack confidence in AI models.
Despite significant investments in infrastructure, many teams are forced to rely on tools that fail to provide the observation and monitoring needed to ensure reliable and accurate results.
This gap has left too many organizations unable to safely scale AI or realize its full value.
This isn’t just a technical obstacle, it’s also a business obstacle. Increasing risks, tightening regulations, and halting AI efforts have real consequences.
The mission of AI leaders is clear. Bridge this gap with smarter tools and frameworks so you can scale AI with confidence and stay ahead of the competition.
Why confidence is the biggest problem for AI practitioners
The challenge of building trust in AI systems affects organizations of all sizes and experience levels, from those just starting their AI journey to those with established expertise.
As one ML engineer explained in the Unmet AI Needs survey, many practitioners feel stuck.
“We don’t hold ourselves to the same standards that other large companies do. As a result, the reliability of our system is poor. “I wish they were more rigorous about testing and security.”
This sentiment reflects the broader reality facing AI teams today. Gaps in reliability, observability and monitoring represent ongoing challenges that impede progress, including:
- Lack of confidence in the quality of generative AI output. Teams struggle with tools that fail to capture hallucinations, inaccuracies, or irrelevant responses, resulting in unreliable results.
- Limited ability to intervene in real time. When models exhibit unexpected behavior during production, practitioners often lack effective tools to quickly intervene or adjust.
- Ineffective warning system. Current notification solutions are noisy, inflexible, and fail to address the most important issues, resulting in delayed resolution.
- Lack of visibility across the environment. Lack of observability makes it difficult to track security vulnerabilities, discover accuracy gaps, and trace the source of problems throughout the AI workflow.
- Model performance deteriorates over time. Without a proper monitoring and retraining strategy, predictive models in production become increasingly unreliable and pose operational risks.
Even seasoned teams with strong resources struggle to address these challenges, highlighting significant gaps in existing AI infrastructure. To overcome these barriers, organizations and AI leaders must focus on adopting powerful tools and processes that empower practitioners, instill confidence, and support scalable growth of AI initiatives.
Why effective AI governance is important for enterprise AI adoption
Confidence is the foundation for successful AI adoption and directly impacts ROI and scalability. However, governance gaps such as lack of information security, model documentation, and seamless observability can hinder progress and create a series of problems.
Weak governance makes it difficult for AI practitioners to build and maintain accurate and trustworthy models. This erodes end-user trust, delays adoption, and prevents AI from reaching critical mass.
Poorly managed AI models can easily fall victim to instantaneous injection attacks, which leak sensitive information and allow malicious input to manipulate the model’s behavior. These vulnerabilities can result in fines and lasting reputational damage. For consumer-facing models, solutions can quickly erode customer trust due to inaccurate or unreliable responses.
Ultimately, these outcomes could turn AI from a growth-driving asset to a liability that undermines business goals.
Reliability issues are very difficult to overcome because they can only be solved through highly customized and integrated solutions rather than a single tool. Hyperscalers and open source tools typically offer piecemeal solutions that address reliability, observability, and monitoring aspects, but these approaches place the burden on AI practitioners who are already overwhelmed and frustrated.
Closing the trust gap requires committed investment in holistic solutions. A tool that helps organizations scale AI responsibly while easing the burden on practitioners.
Increasing confidence starts with reducing the burden on AI practitioners with effective tools. Auditing your AI infrastructure often uncovers gaps and inefficiencies that negatively impact reliability and budget drain.
Here are some things AI leaders and teams should pay special attention to:
- duplicate tool. Redundant tools waste resources and complicate learning.
- Disconnected tools. Complex setups require time-consuming integrations without addressing governance gaps.
- Shadow AI infrastructure. Haphazard technology stacks result in inconsistent processes and security gaps.
- Tools in a closed ecosystem: A tool that puts you in a walled garden or requires your team to change their workflow. Observability and governance must integrate seamlessly with existing tools and workflows to avoid friction and enable adoption.
Understanding your current infrastructure will help you identify gaps and inform your investment plans. An effective AI platform should focus on:
- Observability. Real-time monitoring and analytics and full traceability help you quickly identify and remediate vulnerabilities.
- security. Enforce centralized control and ensure AI systems consistently meet security standards.
- Compliance. Protect, test, and document AI systems to ensure they comply with regulations, policies, and industry standards.
By focusing on governance capabilities, organizations can make smarter AI investments, increasing their focus on improving model performance and reliability, thereby increasing trust and adoption.
Global Credits: AI Governance in Action
Global Credit needed quick and accurate risk assessment of loan applications as it wanted to reach a wider range of potential customers. Led by Tamara Harutyunyan, Chief Risk Officer and Chief Data Officer, they have turned to AI.
In just eight weeks, they developed and delivered a model that helps lenders increase loan acceptance rates and profits without increasing business risk.
While this speed was a key competitive advantage, Harutyunyan also valued comprehensive AI governance, which provides real-time data drift insights so the team can update models in a timely manner to maintain stability and revenue goals.
Governance was important to deliver a model that would expand Global Credit’s customer base without exposing the business to unnecessary risk. The AI team can quickly monitor and explain model behavior and is ready to intervene if necessary.
The AI platform also provided essential visibility and explainability behind the models, ensuring compliance with regulatory standards. This gave Harutyunyan’s team confidence in their model and allowed them to explore new use cases while staying compliant amid regulatory changes.
Increased AI maturity and trust
AI maturity reflects an organization’s ability to consistently develop, deliver, and manage predictive and generative AI models. Trust issues affect all maturity levels, but increasing AI maturity requires investing in platforms that close the trust gap.
Important features include:
- Centralized model management for predictive and generative AI in any environment.
- Real-time intervention and adjustments to protect against vulnerabilities such as PII leaks, instantaneous injection attacks, and incorrect responses.
- Customizable protection models and technologies to build safeguards for specific business needs, regulations, and risks.
- Security shield for external models to protect and manage all models, including LLM.
- Integrate into your CI/CD pipeline or MLFlow registry to streamline and standardize testing and validation.
- Real-time monitoring with automated governance policies and custom metrics to ensure robust protection.
- Pre-deployment AI red teaming for jailbreaks, bias, inaccuracies, toxicity, and compliance issues to prevent issues before models are deployed to production.
- To prevent project failure, we solve the 90% failure rate due to poor commercialization through performance management of AI in production.
These capabilities help enable scalable AI that users trust by standardizing observability, monitoring, and real-time performance management.
The path to AI governance starts with a smarter AI infrastructure
The trust gap plagues 45% of teams, but that doesn’t mean it’s impossible to overcome.
Understanding a broad range of capabilities, including observability, monitoring, and real-time performance management, can help AI leaders assess whether there are critical gaps in their current infrastructure and invest smarter in new tools.
When AI infrastructure actually solves practitioner pain points, companies can confidently deliver predictive and generative AI solutions that help them achieve their goals.
For a holistic look at the most common AI practitioner pain points, download our Unmet AI Needs Survey and start building a smarter AI investment strategy.
About the author
Lisa Aguilar is Vice President of Product Marketing and Field CTO at DataRobot, where she is responsible for building and executing the go-to-market strategy for its AI-based predictive product line. As part of her role, she works closely with product management and development teams to identify key solutions that can address the needs of retailers, manufacturers and financial services providers through AI. Prior to DataRobot, Lisa worked at ThoughtSpot, a leader in search and AI-based analytics.
Meet Lisa Aguilar
