Every organizations needs to balance embracing new innovation and not getting distracted by “trends” that come and go. Since 2022, artificial intelligence (AI) has been the focus on many industries. Is it a true innovation to work? Or a trend that will soon pass? In this article, we’ll explore the key findings of the 2023 Global Trends in AI Report. The findings paint a clear picture that regardless of how AI future trends turn out in the long run, the majority of organizations are pursuing AI integration as a way to innovate their work. If you’ve been skeptical of AI’s importance, now is the time to learn more about the technology and what it can do.
[/et_pb_text][et_pb_text admin_label="AI Adoption at Scale" _builder_version="4.16" _module_preset="default" global_colors_info="{}"]AI Adoption at Enterprise Scale:
If you’re on the sidelines for conversations about AI, the most alarming statistic is 69 percent of survey organizations already have at least one AI project in production. If you haven’t already begun your journey with AI, then as of this writing you’re already behind.
This statistic indicates the interest in AI has progressed past the experimental stage. Artificial intelligence isn’t a trend like “big data” or “crypto” where the evangelists speak in vague terms and the conversation ultimately leads to a promise someone will figure it out for enterprise. Organizations are already implementing AI into their workflow.
At Shelf, we’ve seen the fastest integration has been with support centers for large international companies. We’ve written about how the support center is the perfect department to measure the productivity gains of more accessible knowledge, but AI technology (and AI-powered knowledge management) has many use cases.
That same survey found 28 percent of respondents have reached enterprise scale with their AI projects. This means more than a quarter of surveyed organizations have widely implemented AI into their work and it’s already driving increased value to their company.
[/et_pb_text][et_pb_text admin_label="Revenue-Driven AI Strategies" _builder_version="4.16" _module_preset="default" global_colors_info="{}"]AI Future Trends: Revenue-Driven AI Strategies:
You might be thinking: other organizations have been able to adopt AI, but I don’t think my internal stakeholders have been convinced. The sentiment toward AI has shifted since the market has taken its initial interest. Implementing AI isn’t a cost-savings initiative through higher productivity, it’s a revenue driver to solidify your place in the market.
One study of more than 5,400 AI adopters found revenue-focused drivers motivated AI and machine learning (ML) projects for 69 percent of respondents. The remaining 31 percent were driven by cost-savings. Artificial intelligence isn’t a way to do “more with less,” but integral to your business growth strategy. If you’re not already utilizing AI to grow your business, then by definition your organization’s market share will shrink in favor of competitors who use AI.
This trend is further reinforced by the fact that 70% of responses from AI pioneers (organizations with AI or machine learning projects in production) reported they rely on AI as a revenue driver. The nature of this additional revenue can be diverse. Augmenting your workforce with AI can lead to greater productivity — potentially enabling a department like sales to pursue more growth opportunities. Artificial intelligence can also augment products or services to make already established products more compelling to the market. We’ve seen this most viscerally with Adobe’s adoption of AI tools at the same time many other image generators sought to compete with traditional design services.
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AI Future Trends: Data Management Challenges:
The proliferation of data – both customer data and internal documents – has long been an opportunity that technology hasn’t been able to take full advantage of until now. Organizations are facing a significant challenge managing the increasing volume of data and that will only continue with data needed to train AI models.
Data management has been reported by as a top technological inhibitor for AI/ML deployments for 32 percent of companies responding to the the State of AI survey. This puts data management concerns ahead of security concerns (26 percent) and compute performance issues (20 percent). The data architecture for many organizations is ill-equipped to handle the data-intensive nature of AI. The need to manage data more efficiently has increased interest in knowledge management solutions to make an organization’s stored data more accessible and efficiently accessed. Knowledge management solutions can be incredibly valuable for their ability to keep information up-to-date and accurate while providing feedback on your content’s maintenance. The rising importance of knowledge management is just one of AI's future trends.
[/et_pb_text][et_pb_text admin_label="Sustainability and AI" _builder_version="4.23.1" _module_preset="default" global_colors_info="{}"]AI Future Trends: Sustainability and AI:
Sustainability has been a priority for many organizations and AI can accelerate operations shifting to more sustainable practices. More than 68 percent of respondents cited concerns about the impact of AI/ML on energy use and their organization’s carbon footprint. While it is true greater computational power would result in more energy — thus a bigger draw on the public grid and therefore less sustainable — this computation necessarily requires migrating data to cloud servers. The computing done by Chat-GPT is not performed locally on your computer. The computing for other AI tools won’t be done locally either. To make your own data accessible to AI, organizations will have to migrate data to the cloud as well. All of this means computation and digital storage will be centralized to facilities that can optimize energy usage far better than your office’s back closet (or wherever you host your servers). Roughly 74 percent of respondents stated sustainability was a critical motivator for moving workloads to the cloud. By leveraging AI to optimize processes, monitor energy consumption, and build eco-efficient products, organizations can align their AI strategies with corporate sustainability goals.[/et_pb_text][et_pb_text admin_label="The Value of AI Varies" _builder_version="4.16" _module_preset="default" global_colors_info="{}"]
The Value of AI Varies:
Larger entities are adopting AI at a higher rate than others. Among enterprises with revenues ranging from $500 million to $5 billion, 82 percent have adopted AI into their business. By comparison, 52 percent of enterprises with revenues between $100 million and $499 million have adopted AI into their business. The distinction may be the difference between available resources for new initiatives. This difference between large and medium sized AI adoption suggests the value of AI — new revenue streams, cost savings, and overall productivity gains — will first benefit organizations that are already market leaders. Alternatively, this present an opportunity for medium-sized entities to breakout from their position and gain more of their respective market.
As previously referenced, there are different use cases for AI adoption and industries have focused on different functionality. For example, the automotive industry has focused on increasing product volume and improving service quality whereas the healthcare industry has prioritized patient experiences and healthcare practitioner productivity. This suggests that while “82 percent of large enterprises have adopted AI,” it’s not necessarily the case each of those enterprises has adopted every use case of AI for their business. The degree to which each enterprise sees success may vary on their AI strategy and priorities. The value of specific strategies and implementations will depend on AI's future trends and how the market responds to this innovation.
[/et_pb_text][et_pb_text admin_label="Conclusion" _builder_version="4.16" _module_preset="default" global_colors_info="{}"]Conclusion:
The 2023 Global Trends in AI Report emphasizes the widespread acceptance of artificial intelligence as a true innovator in the market place. With the majority of organizations already implementing AI projects — and AI becoming a revenue driver — it is evident AI strategies are essential for future success. Committing to an AI strategy will require resolving some known challenges. Overcoming data management hurdles, establishing processes to meet sustainability initiatives, and understanding industry-specific uses of AI to drive value are key to harnessing the potential of AI. If you have been waiting for strong evidence before investing your organization’s time and resources into AI, the knowledge most organizations have already taken that step should be convincing. Now is the time to begin your journey into AI for your enterprise. That journey could begin with understanding your own organization’s knowledge.
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