Today’s Enterprise AI Investments Can Lead to Tomorrow’s Success Through Emerging Technologies

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Data Society           
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December 2022     
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business leaders agree that AI is critical to success over the coming years

Forward-thinking enterprises that embraced AI technologies long ago might be tempted to rest on their technologically progressive laurels. However, with the latest crop of technological capabilities ripening for use across industries, organizations that once took the lead in AI adoption risk falling behind if their commitment to AI initiatives wanes as challenges arise. 

The Gap Between AI Inspiration and Realization

many organizations struggle to bridge the divide between adopting AI and realizing the technology’s business value

Despite AI’s decades of recognition as a mighty force for enterprise success, many organizations struggle to bridge the divide between adopting AI and realizing the technology’s business value. According to Deloitte’s “State of AI in the Enterprise” Fifth Edition, 94 percent of business leaders surveyed agreed that AI is critical to success over the next five years. However, the portion of respondents who self-identified as “underachievers” and are not seeing the value they expected from their AI initiatives increased to 29 percent over last year. Yet, current trends suggest that AI’s evolution rapidly enables more accessible, relevant applications with the potential for significant impact across industries. 

AI Drives the Waves of the Future

Access to emerging AI technologies is more readily available than ever. For example, McKinsey reported a 94.4-percent increase in training speed for AI models only since 2018. This broader access has been ushered in by new technology developments such as: 

  • GPUs that increase computing power and speeds. 
  • Burgeoning big data sets.  
  • Access to low code/no code platforms.
  • IoT adoption. 

IT leaders are finding tremendous opportunities to expand their tech stacks with powerful new technologies. And the express lane to this innovation-driven progress is built upon data analytics and AI-enabled innovations. Therefore forward-thinking organizations across industries and sectors are positioned to take advantage of this new wave of applied AI.

The Advent of Applied AI

AI is essential to leading-edge capabilities that are gaining momentum to transform industries. The promise these capabilities offer has garnered sufficient confidence that McKinsey Technology Trends Outlook 2022 found that the relative number of patents filed in 2021 was 30 times greater than in 2015. 

AI will drive—and benefit from—trending innovations in such areas as:

  • User Experience - AI is projected to augment more than 50% of user touches by 2024 through speech, written word, and computer-vision algorithms. AI can also potentially enhance customer experience, powering interactive innovations such as the Metaverse, which offers promising targeted marketing and advertising possibilities.
IT business innovation
  • Generative AI - The evolution of AI-driven capabilities, including Natural Language Processing and Natural Language Generation, has given rise to improved generative AI capabilities that can deliver output ranging from visual art to poetry and music. Generative AI can also be applied to risk assessment and fraud detection, making this technology relevant to the banking industry. In addition, Gartner expects generative AI to become increasingly instrumental in product development, estimating that by 2025, over 30% of new drugs and materials will be discovered using generative AI. Gartner further projects that generative AI will account for 10% of all data produced by 2025. 
AI is transforming industries
  • Synthetic Data - Generative AI can also create synthetic data, an emerging resource Gartner predicts will represent 60 percent of the data used in AI development and analytics projects by 2024. Synthetic data can also prove helpful in facilitating safe data sharing by reducing the use of personally identifying information from real-world data. 
  • Digital Twins - Representing real-life objects, systems, or processes, digital twins produce data that informs critical decisions in healthcare, life sciences, city governments, transportation, and manufacturing, to name just a few industries. This emerging technology has the potential to support critical functions ranging from anticipating equipment maintenance needs, managing supply chains, and helping hospitals optimize their operations flows to increasing employee safety and productivity through cognitive ergonomics.
  • Knowledge Graphs - Enabling machines to connect related data points from diverse sources, knowledge graphs produce more precise responses to queries and help analysts uncover links between data sets. These tools are helpful in such applications as online recommenders, drug discovery, customer support chatbots, and financial risk assessment. Knowledge graphs also provide meaningful context for their results, creating transparency and supporting AI explainability.    

Moving Forward With AI

Given the abundance of emerging technologies AI offers, organizations have good reason to pursue AI initiatives with renewed vigor. However, to use these advanced AI technologies effectively and responsibly, organizations must also address AI explainability and provide continuous learning across workforces. Large enterprises that have embraced such programs for years, and midsize companies that are just beginning their AI journeys, must continue to invest in the knowledge and resources that enable them to use these technologies responsibly and effectively. 


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