intrinsic trustworthy AI capabilities, where AI is a natural part of the functionality, in terms of design, deployment, operation and maintenance,” explained Ericsson in its Defining AI Native white paper. “An AI native implementation leverages a data-driven and knowledge-based ecosystem, where data/knowledge is consumed and produced to realize new AI-based functionality or augment and replace static, rulebased mechanisms with learning and adaptive AI when needed.” Definitions of AI-native also can vary from company to company. Here is how Juniper Networks defines AI-native in a post, through a networking lens: “For us, AI-Native Networking means harnessing AI to improve NetOps, and developing networks specifically optimized for AI workloads. In other words, AI-Native is purpose-built for AI rather than adding AI as an afterthought.” With embedded AI, the AI is integrated into a product or service. The resulting AI-enabled – or AI-driven – solution typically offers superior performance. By embedding AI into existing products, companies can add value to their products and go to market much faster. Again, it’s critical to investigate how companies actually use AI in their products and services. As Atleson explained, using an AI tool during development is different from the product containing AI. ‘Evolution Not Revolution’ According to CompTIA, 22 percent of firms are aggressively pursuing AI across a range of technology products and business workflows, while 33 percent are engaging in limited implementation of AI. Meanwhile, 45 percent of firms are still in the exploration phase. But while adoption is accelerating, many organizations are running into unexpected obstacles, such as a lack of skilled individuals, unclear ROI metrics and the complexity of AI systems. Organizations also tend to struggle with poor data quality due to insufficient data pipelines. Moreover, businesses overwhelmingly lack the change leadership to succeed with AI transformation. In a recent Accenture study, two-thirds of executives said they lack the technology and change leadership expertise to drive the necessary reinvention for leveraging the transformative power of gen AI. While these issues can be resolved, they require time, resources and careful planning. Oftentimes, businesses try and move too quickly with AI and attempt to force results. But AI is rarely a quick fix, and to be successful companies must move methodically and responsibly. In fact, Accenture found that companies can unlock an additional $10.3 trillion in economic value by simply adopting responsible, people-centric approaches to gen AI. At the end of the day, AI is presently an emerging field and there are more questions than answers. But one thing is certain: As AI progresses and more new technologies come to market, the need for reliable partners will become even greater. By being selective with vendors, asking the right discovery questions and deploying technologies with care and outcomes in mind, partners can help customers maximize AI investments, avoid costly blunders and build trust. o Source: Gartner, January 2024 Current AI Efforts and Future Investment Source: CompTIA, 2024 IT outlook Aggressively pursuing integration 22% Limited implementation 33% Exploration 45% Significant decrease Moderate decrease No change Moderate increase Significant increase Future Investments Current Adoption 25% 37% 25% 7% 7% How SAT is Tailored to Organizations Source: Egress cybersecurity leader survey Training is tailored to each individual employee Training is tailored to each department or team Default training modules offered by the provider Training is tailored to the organization as a whole 28% 46% 19% 7% 30 THE CHANNEL MANAGER’S PLAYBOOK
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