Key AI Constraints to Manage When Deploying AI-Driven Technologies

AI expanded exponentially in 2024, and its rapid growth is expected to continue through 2030. According to Statista, last year, the global market for AI grew beyond $184 billion — a giant leap from $50 billion in 2023. Predictions point to the market value racing past $826 billion by 2030.

Yet, a study by Boston Consulting Group (BCG) found that 74% of companies struggle to achieve and scale value from AI technologies. Among the likely reasons are the constraints that can be introduced when enterprises operationalize AI workloads.

Constraints introduced by any new technology can negatively impact an enterprise in multiple ways. Examples include innovation roadblocks, quality compromises, flexibility and adaption limitations, project delays, development complexity, workflow bottlenecks and team burnout.

AI introduces several potential constraints into an enterprise, including four leading risks — data governance, security, privacy and compliance. These issues are not new, but the introduction of AI adds another layer of complexity on top of existing challenges.

Overcoming these constraints can help AI-driven projects succeed. According to BCG, the leaders who have been successful with AI in the enterprise have achieved 1.5 times higher revenue growth and 1.4 times higher returns on invested capital. 

Four main AI constraints and their solutions

Knowing the AI constraints that can negatively impact an IT enterprise and properly managing them can help overcome the restrictions. This helps organizations become better positioned to achieve success in their AI-driven initiatives and goals. Here’s how.

1. Governance constraints:

Establishing effective governance controls as guardrails for AI involves setting clear accountability, transparency and oversight mechanisms. However, organizations often struggle with the absence of standardized frameworks, inconsistent definitions of roles, and challenges in adapting controls to the rapidly evolving ethical, legal, and operational landscape.

Solution: Effective AI governance requires guardrails. Deploying an air-gapped environment can help ensure every data point used in AI solutions is governed and accessed within the enterprise’s environment. This creates a barrier that helps prevent unauthorized access or leaks of critical information because it isolates the data from cyberthreats. Incorporating governance processes to ensure data quality throughout the intake process is critical for ensuring accurate representations of an organization’s information.

2. Security constraints: 

Cyber attackers are employing AI technologies in their attacks. This can lead to unauthorized access, misuse, and manipulation of data and AI systems, as well as attacks on other internal systems.

Solution: Overcome AI security constraints by investing in AI-powered cybersecurity solutions. The right solutions can help strengthen your security posture. Enhancing threat detection by leveraging AI will help unburden your security team, so team members can focus on launching strategic offense tactics.

3. Privacy constraints: 

AI systems often handle sensitive data for an organization and its workers, customers and partners. This raises privacy regulators’ concerns about data breaches, including challenges in ensuring compliance and data protection. Additionally, managing user consent and handling cross-border data transfers can complicate adherence to strict privacy standards.

Solution: Implement a comprehensive privacy framework with robust encryption, fine-grained access controls and data anonymization techniques. Regular privacy audits, continuous monitoring, and adherence to frameworks, such as GDPR, CCPA, EU AI Act, and other AI and privacy legislation can further ensure that AI systems protect personal data, while maintaining regulatory compliance.

4. Compliance constraints: 

Scaling AI introduces challenges for meeting diverse and ever-changing regulations across different regions and industries. Proprietary and even some opensource models can make it difficult to ensure transparency and traceability for audits.

Solution: Adopt a dynamic compliance framework that integrates continuous monitoring, regular audits, and collaboration with legal and regulatory experts. Adopting models that provide transparency into their use for training data and traceability can generate outputs that go a long way toward ensuring systems remain auditable and aligned with both current and emerging regulatory requirements.

Innovative ways to overcome AI constraints

When accounting for these four common AI constraints and implement appropriate mitigation strategies, your organization can increase the likelihood of achieving successful and responsible AI adoption across your enterprise.

Foundry for AI by Rackspace (FAIR™) takes overcoming AI constraints seriously by helping organizations ensure that their AI technologies are:

• Symbiotic: Enhances human capabilities safely. This means developing clear ethical principles to guide AI development and deployment for fairness, transparency and accountability.
• Secure: Prioritizes data protection and regulatory compliance.
• Sustainable: Encourages transparent and unbiased systems for a more equitable future.

Watch the video that answers the question: What are the constraints I should look for when implementing AI systems in the enterprise?