In the era of digital transformation, industries are undergoing a profound evolution driven by artificial intelligence (AI) and data analytics. Innovations and advancements in generative AI (GenAI), virtual assistants, machine learning (ML), hybrid clouds, predictive analytics, and natural language processing (NLP) are set to revolutionise industries by transforming manufacturing, automating workflows, personalising services, enhancing customer experience (CX), and driving business growth.

Further, as AI implementation in organisations shifts from the proof-of-concept stage to full-scale deployments, the failure to implement robust, enterprise-wide AI strategies can impede large scale implementations that unlock enterprise-wide value from the technology. This is intensifying the pressure on enterprises to tackle barriers like data readiness, legacy infrastructure limitations, complex technology integrations, data disparities, and regulatory compliance to reap the rewards of democratised AI.

AI demystified: Key AI maturity indicators

• Strategy and Roadmap Articulation: Indicates the preparedness of enterprises to articulate business goals, expected AI outcomes, and governance structures, along with leadership commitment and involvement of technology stakeholders to drive new initiatives.

• Data Readiness: Refers to the integration, standardization, and readiness of enterprise data to be leveraged for AI and ML deployments, while building data architectures that ensure data streamlining.

• Regulatory Compliance and Policy Alignment: Indicates an organisation’s preparedness with respect to the policies/norms that need to be adhered to, thereby keeping pace with government regulations or other industry compliance standards.

• Technology Implementation: Considers the stage and the extent of AI deployment and infrastructure within an organisation, with the end goal of ubiquitous AI implementation across multiple functions and applications.

Organisation's need to assess where they stand in terms of AI maturity, and which strategies will help their teams scale AI implementation across different business functions.  In addition the teams in question need to be equipped to tackle the complexities of AI deployment.

Frost & Sullivan finds that though 97% of enterprises view AI and ML as important tools in helping them achieve their business goals, just 1% have achieved a ubiquitous level of AI maturity. As a result, providers are being pushed to capitalise on the opportunity to provide advisory services, support enterprise-wide AI adoption, and develop compelling value propositions that focus on data security and industry knowledge. Today, Frost & Sullivan research shows the following emerge as key customer/IT decision maker (ITDM) concerns about adopting AI across business processes:

• Data privacy and security

• Quantifying return on investment (ROI)

• Defining ethical AI practices

This, according to the company, implies that as providers democratise AI, they face the imperative of looking beyond their core offerings to strengthen integration and data migration support services, develop comprehensive advisory capabilities/IT services, and align growth strategies with rapidly transforming technology landscapes.

Capitalising on emerging growth opportunities

Today, AI and advanced analytics are bringing about a new era of data-driven decision making and automation. Chatbots, for instance, are transforming customer service by providing instant responses and freeing up human agents for more complex tasks. AI algorithms are seamlessly analysing massive datasets with unprecedented accuracy, uncovering hidden patterns and generating valuable insights that were previously impossible. Further, AI is also revolutionising network management, optimising resource allocation, preventing network failures, and enhancing security through advanced threat detection. This is unleashing the various growth opportunities for ICT providers to boost operational efficiencies, catalyse creativity and innovation, and create new revenue streams.  For example, GenAI Deployment Services; AI Implementation Platforms; System Integration Services; CT Advisory Services; Data Readiness and Management; Applied AI Applications; Embracing Responsible AI; and, Multimodal Foundational Models, amongst others.