David Menninger's Analyst Perspectives

Ventana Research recently announced its 2024 Market Agenda for Artificial Intelligence, continuing the guidance we have offered for two decades to help enterprises derive optimal value from technology and improve business outcomes. 

The application of artificial intelligence and machine learning within enterprises is transforming analytics. Opportunities to take advantage of these technologies will increase over the coming year as software providers incorporate generative AI in products and automate more AI processes. Our upcoming research will explore the extent to which these advances reduce the skills needed to reap the full benefit of AI, but we assert that through 2026, more than one-half of enterprises will realize their AI competencies and skills are insufficient and will require new investments to avoid being at a competitive disadvantage. Only one-quarter of enterprises (23%) report they have the skills they need, while two-thirds (65%) report they need more skills. Unfortunately, enterprises are having the most difficulty finding and retaining those with AI skills. Finding and retaining those with cloud platform skills and those with data and database skills are also among the top five most challenging. 

AI involves the development of systems and software capable of automating tasks that have previously required human intelligence. It encompasses machine learning (ML), deep learning and GenAI to deliver capabilities including predictions, recommendations, personalization, speech and visual recognition as well as translation and summarization. To get the most out of AI systems, enterprises need to involve people in business and executive roles outside of the IT department in determining use cases and success metrics. As revealed by ISG’s recently completed AI Buyer Behavior Survey, the second most common challenge enterprises face is a lack of holistic, company-wide vision.  

Ventana Research offers research-based guidance on analytics and data to help enterprises enhance the value of information through the smarter use of data. Going beyond earlier methods of business intelligence, dashboards and reports is essential to ensure that everyone is able to not only access data but is also empowered to act on it to optimize the business. Our Artificial Intelligence expertise provides a holistic perspective that addresses both the use cases and requirements to successfully apply AI technologies. It includes six focus areas: Computer Vision, Deep Learning, Generative AI, Machine Learning Operations, Model Building including Large Language Models and Natural Language Processing. ISG’s AI Buyer Behavior Survey explores these topics, providing insights to guide decision-making, while we help enterprises assess, evaluate and select software providers through our Ventana Research Buyers Guides. 

Computer Vision 

Computer Vision refers to a type of AI that interprets image and video data to identify and classify objects and actions. Once interpreted, this data can provide an additional source of input to a variety of decision-making processes. Only 1 in 7 enterprises consider image or video data an important source of information for their analytics activities today, but ISG research shows that among those that have adopted AI, nearly three-quarters of enterprises (71%) report positive outcomes interpreting images. We assert that through 2026, 1 in 5 enterprises will adopt computer vision applications to monitor and improve their operations. In 2024, our Artificial Intelligence Buyers Guide research will include an assessment of a software providers’ computer vision capabilities. 

Deep Learning 

Deep Learning refers to a type of ML that utilizes neural networks with more than three layers to simulate the learning behavior of the human brain. Deep learning algorithms generally require large amounts of data and perform tasks such as image and speech recognition. Increases in processing power, including advances in GPUs, have made deep learning much more accessible. Without these advances, the cost and time required to execute deep learning algorithms were prohibitive for many use cases. As deep learning has become more affordable, in part due to cloud-based computing resources, we assert that through 2026, deep learning algorithms will continue to drive advances in natural language processing (NLP) and image recognition, enabling new use cases. In 2024, our Artificial Intelligence Buyers Guide research will include an assessment of a software providers’ deep learning capabilities. 

Generative AI 

Interest in and application of GenAI is exploding. The ISG study 2023 Future Workplace shows that 85% of enterprises believe that investment in GenAI technology in the next 24 months is important or critical. GenAI is being used successfully for NLP such as chatbots/assistants, extracting information from and summarizing documents, and assisting with software development tasks such as code generation and application migration. Nearly every software provider is incorporating GenAI into their products to make them easier to use and more efficient. Further insights into the provider landscape will be included in our 2024 Buyers Guides focused on Artificial Intelligence which contain a related Generative AI Buyers Guide. 

Machine Learning Operations 

Machine learning Operations (MLOps) focuses on the application of agile development, DevOps and lean manufacturing in support of AI. It encompasses model training, testing, deployment and orchestration of related data processing pipelines. While MLOps emerged prior to the explosion of interest in large language models (LLMs), the same techniques and concepts are being extended to encompass LLMs. I assert that by 2026, 4 in 5 enterprises will use MLOps and LLMOps to improve the quality and governance of their AI efforts. Further insights into the provider landscape will be included in our 2024 Buyers Guides focused on Artificial Intelligence which contain a related MLOps Buyers Guide. 

Model Building and Large Language Models 

The process of training, testing and tuning AI models, including LLMs, is a complicated process that requires highly skilled individuals and disciplined processes. As noted above, two-thirds of enterprises report they do not have the skills they need. Due in part to a lack of process, we assert that through 2026, model governance will remain a significant concern for more than one-half of enterprises, limiting the deployment and therefore the realized value of AI models. AI platforms have improved dramatically with capabilities such as hyperparameter optimization, but these platforms still require specialized knowledge. We expect to see the application of GenAI to further improve the ease of use. We will explore this and other elements of the model building process in our 2024 Buyers Guides focused on Artificial Intelligence.  

Natural Language Processing  

Conversational computing is becoming an increasingly common part of user interfaces. Smart speakers and mobile assistants propelled consumer interest in natural language capabilities and now GenAI powered chatbots are creating an explosion of interest across both consumers and the workforce. Future enhancement with domain-specific and custom LLMs will make NLP more accurate and more broadly applicable over time. We also expect to see enterprises demanding more multilingual capabilities if NLP is to live up to its promise of reaching more of the workforce. We expect that by 2026, one-half of conversational analytics will incorporate multilingual capabilities to support a larger portion of enterprises’ employees.  

Subscribe to our Ventana Research community to stay up to date on our 2024 research efforts. Check out the Artificial Intelligence expertise and focus pages for our detailed research agenda and continuously updated 90-day calendar as well as more research facts and best practices. 

Regards,

David Menninger