Despite the rapid advancements in artificial intelligence (AI), large and complex organisations in both the public and private sectors are struggling to adopt the technology at the same pace as smaller firms. A new report by Codiance has revealed that key barriers, including security concerns, integration challenges, and regulatory compliance, are slowing AI adoption for these entities. While smaller, more nimble organisations are seizing the benefits of AI, their larger counterparts face significant obstacles that are difficult to overcome.
According to Codiance, experts in system development for large organisations, this growing gap in AI adoption could leave larger organisations at a distinct disadvantage. With AI being hailed as one of the most transformative technologies of the current era, the inability of complex organisations to adopt it efficiently could result in missed opportunities for growth and innovation.
The report outlines several reasons why larger organisations are facing difficulties in implementing AI. Chief among them are the security protocols that need to be in place, integration with existing legacy systems, and the necessity to comply with stringent regulations. These factors, while critical for maintaining operational stability and trust, also make it much harder for larger organisations to move quickly on AI projects. As a result, many are lagging behind in what is rapidly becoming a highly competitive field.
Security and compliance concerns slow adoption
The complexity of integrating AI into large organisations stems from their need to handle sensitive data and comply with various industry regulations. Security is paramount for these entities, particularly in industries such as healthcare, finance, and government, where data privacy and protection are non-negotiable. The fear of breaches and mishandling of data makes many organisations hesitant to adopt AI solutions that might compromise their existing security frameworks.
Compliance with local and international regulations presents another significant hurdle. Organisations must ensure that AI technologies comply with laws governing data protection, such as the General Data Protection Regulation (GDPR) in Europe. Any misstep in this area could result in hefty fines and damage to an organisation’s reputation. This need for rigorous compliance checks further delays AI integration, making it a slow and sometimes frustrating process for large organisations.
Smaller organisations, on the other hand, often have the advantage of being more agile. With fewer regulations to navigate and a more streamlined approach to integrating new technologies, they can adopt AI much faster. This has allowed them to leverage AI for customer service, marketing automation, and data analysis at a pace that leaves many larger organisations behind.
Codiance outlines two AI strategies
To address these challenges, Codiance suggests two primary approaches for AI integration in large organisations: the “power station” model and the “self-power” model. Each offers a different path to adopting AI, depending on the specific needs and resources of the organisation.
The power station model involves leveraging AI services from third-party providers, such as OpenAI, Meta, or Alphabet. This approach allows large organisations to tap into the AI capabilities developed by industry leaders without having to invest in building their own systems from scratch. It offers significant advantages in terms of cost and scalability, as third-party AI platforms continuously evolve and improve. For many organisations, this model provides a faster and more efficient way to incorporate AI into their operations.
But the power station model is not without its challenges. Relying on external providers means sharing sensitive data with third parties, raising concerns about data privacy and security. Organisations must also ensure that the AI services they use comply with all relevant regulations, which can add another layer of complexity. Service reliability is another consideration; just as organisations depend on electricity from power grids, they would rely on AI providers to ensure continuous and reliable service.
The alternative is the self-power model, which involves developing AI capabilities internally. This approach offers greater control over the AI systems being used and allows organisations to tailor the technology to their specific needs. For industries with highly specialised requirements, such as medical research or financial services, this level of customisation can be invaluable. Additionally, by keeping AI development in-house, organisations can maintain stricter security protocols and avoid the risks associated with sharing data with external providers.
But the self-power model requires significant upfront investment in talent, technology, and infrastructure. Building proprietary AI systems from the ground up is resource-intensive, and maintaining these systems requires continuous updates and improvements to stay current. Scaling such systems to meet growing demand can be challenging, particularly for organisations that may not have the resources of AI giants like OpenAI or Meta.
Emerging capabilities and future potential
One intriguing aspect of AI adoption, regardless of the model chosen, is the emergence of capabilities that developers did not explicitly train the AI systems for. This phenomenon, known as emergent capabilities, has been observed in systems like OpenAI’s ChatGPT, which developed the ability to perform arithmetic reasoning without being specifically trained to do so. These capabilities can offer unexpected benefits, but they also introduce unknowns that large organisations must consider carefully.
To mitigate the risks associated with emergent AI capabilities, Codiance recommends that organisations adopt a retrieval-augmented generation (RAG) approach. This ensures that AI-generated content and outputs are subject to human review, helping to maintain control and prevent unintended consequences.
