Generative AI vs Predictive AI: Comprehensive Guide to Understanding Their Impact

The Generative AI Advantage: Product Strategies to Differentiate by Sarthak Handa

The global generative AI market size was valued at $10.5 billion in 2022, and is projected to reach $191.8 billion by 2032, growing at a CAGR of 34.1% from 2023 to 2032. The growth in demand for AI-generated content has been a significant driver for the generative AI market growth. With AI-generated content, companies and individuals can create massive amounts of content quickly and at scale. This efficiency is especially valuable in industries such as marketing and advertising, where personalization and variety of content is critical to effectively engaging audiences.

The second half of 2024 has seen growing interest in agentic AI models capable of independent action. Tools like Salesforce’s Agentforce are designed to autonomously handle tasks for business users, managing workflows and taking care of routine actions, like scheduling and data analysis. Successful technological shifts often hinge on the convergence of compute power, data infrastructure, and user interfaces. So, in this piece, we highlight four companies held within the Global X Artificial Intelligence & Technology ETF (AIQ) that excel in these and other areas critical to the successful proliferation of AI.

What is enterprise AI? A complete guide for businesses

It is both appealing and economically rational for them to stop at the API, and let the developer universe worry about the messiness of the real world. Whether a model is pre-trained on millions of moves in Go (AlphaGo) or petabytes of internet-scale text (LLMs), its job is to mimic patterns—whether that’s human gameplay or language. It can’t properly think its way through complex novel situations, especially those out of sample. While the actual implementation of Strawberry is a closely guarded secret, the key ideas involve reinforcement learning around the chains of thought generated by the model.

Due to the way generative AI models are trained, there is also an inherent risk of bias. While silos and prompt engineering can overcome some of these limitations, generative AI isn’t ready for applications that may involve sensitive customer interactions where small mistakes can create large issues. In architecture and design, generative AI can aid in creating optimized building designs based on specific criteria such as environmental factors and space utilization. The automotive industry can benefit from generative AI by generating designs for car components, enhancing aerodynamics, and improving overall vehicle performance. In digital marketing, AI-generated personalized advertisements can cater to specific customer preferences, leading to higher conversion rates. As companies seek ways to optimize content creation and cater to individual preferences, the adoption of generative AI is expected to witness substantial growth.

Major Players and Startups Shaping the Generative AI Landscape

OpenAI’s DALL-E text-to-image technology is aimed at restaurants that need to quickly create images of food for their social media pages, websites, and menus. Microsoft announced a major upgrade to its existing search and browser capabilities with the launch of the generative AI-augmented Bing search engine and Edge browser in February 2023. These tools are based on OpenAI’s generative pre-trained transformer (GPT)-3.5 and can create content and engage in conversational search and browsing experiences.

• A 2024 study found that three-quarters of product features are rarely used, underscoring the need for precision.
• This article, brings out some of the key strategies that product leaders are leveraging to use Generative AI for delivering a differentiated offerings to their customers.
• The data mesh is a distributed, decentralized (not in the crypto sense) approach to managing data tools and teams.
• In Generative AI, “one size fits all” approach doesn’t make the cut for specialized use cases.

The dawn of software application delivery started with large backend programs running on specialized servers, and the advent of PCs introduced the desktop into the application development lifecycle mix. Later, the rise of the web and social media paved the way for increased speed and scale in the number of applications and their interconnections. The appearance of mobile apps and the cloud incorporated new levels of automation and agility into the development lifecycle. As a result, the SDLC for application delivery became radically different from what it used to be.

Generating data to train models

For IT decision-makers, the emphasis is moving from exploring the cool, new technology to identifying good data for training customers on LLMs for their apps without introducing operational or reputational risks to processes. “This may well be the catalyst that IT leaders needed to change the paradigm on data quality, making the business case for investing in building high-quality data assets,” Carroll said. This technology finds applications in personalized content generation, recommendation systems, and interactive AI-driven interfaces. As businesses and users seek more interactive and customizable AI-generated content, retrieval augmented generation offers a compelling solution, driving its rapid growth in the generative artificial intelligencemarket. Generative AI refers to the branch of artificial intelligence focused on creating or generating new content such as original and realistic images, text, music, and videos. This involves training machine learning models to understand and learn patterns in existing data to generate new and unique content.

Aqua Security Listed in OWASP’s LLM and Generative AI Security Solutions Landscape Guide for 2025 – GlobeNewswire

Aqua Security Listed in OWASP’s LLM and Generative AI Security Solutions Landscape Guide for 2025.

Posted: Mon, 18 Nov 2024 08:00:00 GMT [source]

We are likely to witness a significant shift from the conventional app store pricing model to more dynamic, consumption based billing systems. These models, reminiscent of utility billing like phone lines, are poised to become more prevalent, aligning with the concept of renting AI agents. In this setup, customers would pay based on the extent and nature of their AI usage, offering a flexible and potentially more equitable pricing structure. The winners in this evolving landscape will be those who invest in developing their own models generalised or small foundational models to plug gaps in the generalised space.

ChatGPT, Bing and Bard are general purpose chatbots, but crafting bespoke chatbots is a nascent creative space powered by Character.AI, founded by one of the authors of the original Transformer paper, Noam Shazeer. TXI’s Chekal sees the potential for generative AI to improve patient outcomes and make life easier for healthcare professionals. Generative AI can extract and digitize medical documents to help healthcare providers access patient data more efficiently. It will also improve personalized medicine and therapeutics by organizing more medical, lifestyle and genetic information for the appropriate algorithms. Intelligent transcription will save time and help summarize complex information as part of doctor-patient conversations rather than as a separate process. It will also improve patient engagement through personalized recommendations, medication reminders and better symptom tracking.

It also obliges providers of AI services to suspend their services and take immediate action to bring them in line with the law if they are found to be generating illegal content. Another application, Wenxin Baizhong, is a platform aimed at simplifying the task of creating industry-specific generative AI-powered search engines. Firstly, as well as being trained on unstructured text, it also has access to a knowledge graph. This is a structured database of basic information points and, crucially, the semantic links between them. It includes scientific, demographic, geographic and economic data and is similar to the one used by Google to present facts alongside its search results.

Machines can analyze a set of data and find patterns in it for a multitude of use cases, whether it’s fraud or spam detection, forecasting the ETA of your delivery or predicting which TikTok video to show you next. These strong growth figures are further supported by McKinsey’s estimates on the broader economic impact of generative AI. According to their analysis, generative AI could contribute between $2.6 trillion to $4.4 trillion to the GDP (Gross Domestic Product) in advanced economies, amounting to 4% to 7% of the overall GDP. The list has applications that are more general than ever, and we expect it to expand in both depth and breadth in the coming years. Replicating existing functions better and cheaper, will be followed by evolving entirely new user interfaces to deliver valuable new experiences.

These partners include Accenture, Cisco, Dell Technologies, Deloitte, Hewlett Packard Enterprise, Lenovo, SoftServe, and World Wide Technology (WWT). The catalog is built on NVIDIA NIM, a slate of microservices composed of downloadable software containers for speeding the deployment of enterprise gen AI applications. “NVIDIA NIM Agent Blueprints are runnable AI workflows pretrained for specific use cases that can be modified by any developer,” said Justin Boitano, vice president of enterprise AI software products at NVIDIA.

Connection between core models and applications

With strategic efforts to tackle existing challenges, the Indian GenAI ecosystem is poised for sustained growth, contributing to the country’s position as a key player in the global innovation landscape. The largest of these infrastructure companies host the massive amounts of data needed for enterprise AI applications in a format that facilitates all sorts of data pipelines. Databricks has distinguished itself from Snowflake, a notable incumbent in the space, by being specifically designed for the needs of AI/ML data teams.

NTT DATA’s innovative approach to application modernization utilizes Azure OpenAI, enhancing efficiency and transforming legacy systems with AI-driven solutions. Read how companies in various industries solved their challenges by partnering with NTT DATA. Organizations must help employees adopt and make the most of new Generative AI tools by using them responsibly and safely and must also design a future-proof organization that responds to the talent challenges of Generative AI.

Meanwhile, the very core of the blockchain proposition is to enable the creation of decentralized networks that allow participants to share resources and assets. There is fertile ground for exploration there, a topic we started exploring years ago (presentation). And ultimately, all those partnerships seem to only create more need for Microsoft’s cloud compute – Azure revenue grew 24% year-over-year to reach $33 billion in Q2 2024, with 6 points of Azure cloud growth attributed to AI services. The data part is complex – there’s a more tactical question around running out of legally licensed data (see all the OpenAI licensing deals), and a broader question around running out of textual data, in general. Yann LeCun discussed how taking models to the next level would probably require them to be able to ingest much richer video input, which is not yet possible.