Generative AI: Transforming Business Landscapes and Overcoming Implementation Challenges

Council Post: The Executive Playbook For Generative AI: Turning Potential Into Business Value

2024 is the year generative AI moves beyond stargazing. Heady rhetoric -- like how AI is an invention "almost like" the printing press -- obscures what's actually important for business leaders today. The focus should instead be on how businesses can implement AI in ways that automate workflows end-to-end and drive immediate value, all while mitigating risks and ensuring compliance. Unlike the mixed messages in the market today, the reality is that this is achievable today. Choose a business output or workflow you want to transform first. This depends on your priorities but can be anything from fulfilling IT requests and resolving tickets more efficiently to managing employee car service reservations and filing corresponding expense reports. VC and industry vet Chamath Palihapitiya frequently makes the astute comparison that data is the "new oil" and AI tools and software are the refineries. They're all approaching the opportunity in different ways, refining data into an infinitely valuable resource. The applications are the output. Just like we use oil for everything from powering jets to producing plastics, those end-user "outputs" and the tangible workflows you transform via automation are what ultimately matter most for a business, not the nitty-gritty specifications of an underlying LLM. Companies looking to score early wins with GenAI should move quickly; however, not at the expense of being methodical. Those hoping that GenAI offers a shortcut past the tough -- and necessary -- detailed analysis of business outputs will likely yield disappointing results. Launching a pilot AI workflow is (relatively) easy, but getting those pilots to scale and create meaningful value is hard. This is because they require a broad set of changes in the way work actually gets done. The broad excitement around generative AI and its relative ease of use has led to a burst of experimentation across organizations. Most of these initiatives, however, won't generate a true competitive advantage. For example, a customer informed me they previously bought multiple licenses of a major developer platform's copilot tool, but since they didn't have a clear sense of how to work with the technology, progress was slow. Other unfocused efforts typically revolve around experimenting with multiple vendor tools at the same time or using multiple LLM interfaces without a cohesive goal in place. Avoid these pitfalls by building your approach parameters, objectives and processes in advance. For enterprise leaders under pressure to understand, monitor and adopt AI for their organization, focus on driving near-term value by aligning GenAI efforts to your internal end users' current roles. Focus on improving and automating workflows, such as your procurement manager sourcing multiple products across departments, the salesperson in Chicago who needs enablement for selling a brand-new product or a customer service manager resolving dozens of queries before lunch. It's important to ground all rhetoric about AI in reality. Here's an example of a real GenAI implementation that illustrates the three steps outlined above. A global systems integrator adopted generative AI with a razor-sharp focus on business outputs. They first chose to implement it in their human resources department. The reason was that it had an internal domain, a secure authentication infrastructure in place and a reduced transaction risk. They set about engaging their business users along with a GenAI software partner to analyze traditional workflows and business outputs deeply like automating time sheets, travel policies and benefits management. All these seemingly "mundane" business outputs were analyzed for efficiency and then plotted on how generative AI could improve and transform the experience for employees. The results? Just 15 months later, the company had 40-plus use cases automated for daily tasks and auto-resolved 6.5 million queries in a safe, compliant manner. This translates to hundreds of thousands of dollars in workforce labor savings and counting. Generative AI has the opportunity to radically transform the enterprise. By the end of this year, over 77% of companies will either be using or exploring AI in their organization. But at the end of the day, it matters how their implementation ultimately impacts business output. Business leaders care that it makes their lives easier and drives results -- and, more likely than not, they couldn't care less about which LLM is behind it. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?

Moving beyond AI paralysis

The global AI market has exploded, estimated at around $4 billion in 2014, to a staggering $200 billion today (July 2024). The number of AI startups continues to surge, up roughly 14X since 2000. People are leveraging AI in their everyday lives, as 77% of devices are anticipated to use some form of AI, even in washing machines. As capitalizing on this market growth is critical for people and businesses alike, the Saudi Arabian government just made a huge splash by starting a $40 billion dollar venture into the space and is currently looking for someone to lead the fund. The AI revolution is just beginning. "Most organizations are gearing up for a headlong rush toward the adoption of generative AI to stay competitive, even as they're feeling overwhelmed by the number of tools, paralyzed by choice between a myriad of potential use cases, and under pressure to deliver dramatic results," says Ryan Barker, Field CTO at AHEAD. The question remains: where to start? The slightly boring but very critical answer will always be with the data. Having the data conversation "Every AI conversation leads directly to a data conversation," Barker says. "You need to align the right data to your AI use cases before you can get real value for your enterprise from AI." AI, of course, relies on data, and given that data volume is expanding at a wild speed, questions around quality, lineage and long-term storage are more critical than ever. Complicating the issue is the number of disparate tools and technologies used to access and manage that data, causing bottlenecks that hamper AI tools. Assessing data readiness is one of the first areas AHEAD tackles for its clients," Barker says. "And it kicks off with finding where the required data lives -- there's always a lot of it, and it's frequently siloed and hiding in a huge number of places, whether on-prem, in the cloud, at the edge or even sitting on someone's device. It could be forgotten in a legacy application and require unique methods of extraction, or safe but useless behind a variety of access controls." Even before tackling the data discovery process, it's critical to ensure that a strong governance and data quality practice is in place as a foundation for best practices when cleaning the data. Luckily, there have been major advances in the tools and technologies that handle data governance and quality control. These tools can automate quality checks as the data is found and investigated since they're able to understand the content and structure of information as it comes in. "The data piece can end up being a pretty huge issue to tackle," Barker adds. "Typically, we navigate clients through figuring out a data set to start with so it's not overwhelming and you're making concrete progress while you're in the beginning learning stages. But it's all about making sure we get the most value out of AI once we get to that stage." Defining technology investments As one of Barker's customers lamented, AI is the solution in search of a problem. Choosing a direction and the right tools to achieve success can be paralyzing. "Some companies are leaning into already-built tools like Microsoft Copilot to enhance their business processes right out of the gate, and that's a good first step, but it doesn't preclude other, larger investments," Barker explains. Some of the advanced use cases will require enterprises to adopt more vertically aligned solutions and models to maximize the value proposition of those investments and get the most out of process change. "When you make an investment with a platform integration, it's typically specific for just that platform and not really shared across all applications and all data within an organization," Barker says. "You're limited. You're locked in." Every organization struggles with knowledge management and deriving insights from disparate sources. For many, the first step is to build a scalable RAG framework that incorporates multiple data repositories to leverage across the organization. Retrieval augmented generation (RAG) as a first step "We're seeing a lot of companies get early value by leaning into building RAG architecture," Barker says. "It's probably the best place to make an investment, because it scaffolds all your AI initiatives by letting you extract accurate information, insights and value from your data -- something that's been notoriously difficult to do for decades." RAG architecture tackles the limited nature of LLMs, which are generally hemmed in by their training dataset. It can retrieve information from external sources, whether that's publications outside the organization or proprietary company data within, to create a generative AI system that's dynamic and relevant. Instead of needing to fine-tune and re-train an LLM every time there's new information, RAG architecture adds necessary context to a user's prompt, making it a far more cost-effective way to add specialized data to your LLM. RAG adds a great deal of transparency, and the number of unchallenged hallucinations is dramatically reduced, because the LLM can point to the sources it used for its responses enabling users to verify the system's claims. Answers are also improved by information that's more up to date than the training dataset. LLM-powered chatbots can leverage RAG to deliver more useful answers based on company knowledge bases, which improves the customer experience by making chatbot responses less generic and more relevant. Internally, generative AI-enabled search engines and knowledge bases are vastly improved with the addition of company data across an array of roles -- for example, accounting can access financial databases, sales teams can query their CRMs and sales statistics and more, improving operations from the start, all done in natural language. "I call it hybrid AI because you're leveraging different pieces of technology, whether it's in the cloud, on-prem, vector databases on top of your data or elsewhere, to build a platform that scales easily while you tackle more use cases over time," Barker says. "It's a great place for companies to start, and there's so much value in creating an environment where users at every level of the organization can just ask questions about their own data and get quick answers."

Council Post: How Generative AI Can Improve Employee Productivity And Happiness

Andrey Kalyuzhnyy, CEO of 8allocate. As a technology leader, Andrey helps businesses overcome challenges with tailored software solutions. Since ChatGPT's public launch, opinions on generative AI have split. On one side, there are fears of job displacement, redundancies and layoffs. On the other side, there is an almost futuristic belief in human-machine synergy, ushering in a new era of economic prosperity. Generative AI (GenAI) won't replace humans or enable fully autonomous operations in the foreseeable future. What these technologies can offer now and in the future is immersive skills augmentation, making the workforce smarter, more effective and more satisfied. Generative AI models produce text, visual and audio inputs based on the training data. Because they're trained on a large corpus of public knowledge, their outputs reflect common human behaviors, practices and ways of thinking. This makes Generative AI a helpful assistant for a variety of tasks -- programming, data modeling, text summarization and media materials creation -- but not an independent executioner. GenAI tools are great at "co-piloting" -- assisting the workforce with routine, repetitive and time-consuming tasks. As a technology leader and AI enthusiast, I've been involved with custom AI solutions development and the fine-tuning of open-source LLMs. My conclusion is that generative AI will bring the strongest impact on workforce productivity and creativity. Here are a few examples: The flip side of digital transformation is growing data silos. An average enterprise now uses 1,061 different applications, with only 29% being integrated. Lack of integration forces workers to switch screens, do manual data entry and spend time hunting down relevant information. This prolongs onboarding for new hires, increases information asymmetry and breeds operational inefficiencies. GenAI can improve knowledge management. Thanks to retrieval augmented generation (RAG), open-source LLMs can be fine-tuned to search connected databases, identify relevant information and produce a contextually relevant response. Thomson Reuters, for example, created an AI assistant that produces relevant answers based on expertise contributed by legal subject matter experts and attorney editors. Generative AI tools bring in a conversational interface for data analysis. Instead of complex technical queries, users can use text commands to access and analyze data across multiple dimensions. Colgate-Palmolive created a GenAI tool to synthesize consumer and shopper insights, understand consumer sentiment and inform product development. Enterra Solutions, likewise, built a System of Intelligence platform that supplies advanced consumer intelligence, growth insights, demand and supply analytics and competitor analysis to enterprises. (Disclosure: Enterra is one of 8allocate's partners.) GenAI models also bring a new degree of efficiency to the creation processes. An Adobe survey found that 66% of creative professionals use AI to improve content and 58% use it to increase content production volumes. In EdTech, generative AI tools are proving useful in curriculum development and content authoring. Fine-tuned LLMs can generate instructional materials from corporate documents and policies. Likewise, they can help trainers produce personalized materials for different learner cohorts. Amira Learning, for instance, built an adaptive tutoring system for improving reading skills. Similar solutions are also coming to the corporate training market. GenAI also reduces the cost and complexities of creating immersive simulation training in AR or VR formats. With GenAI tools, companies can create realistic training scenarios for soft and hard skills. AR and VR also improve product development processes, allowing teams to virtually iterate on complex concepts before creating physical prototypes. Lastly, data and creativity are mutually reinforcing -- and GenAI tools commoditized access to advanced forecasting. Some of the best ideas are born at the intersection of seemingly unrelated industries or creative takes on existing solutions. GenAI adoption requires organizational readiness in terms of people, processes and technology. Organizations risk facing spiraling costs, compliance issues and low ROI without proper preparations. To unlock the full potential of GenAI, evaluate your maturity in these four areas: * Strategy And Governance: Can you identify and objectively evaluate promising GenAI use cases in your business? Work backward from your goals (e.g., cost optimization or productivity gains) to narrow down your focus. Determine which KPIs can be a good proxy for measuring the impact. Model expected usage patterns and solution cost to better estimate the ROI for selected use cases and secure executive-level sponsorship. * Data Organization: Even off-the-shelf GenAI tools require a strong technical baseline of cloud computing and mature data management practices. Data silos, system interoperability issues and a lack of unified data governance strategies undermine your ability to quickly iterate on different GenAI use cases. You may need to implement changes to your data pipelines to improve data integration across systems and implement proper controls for ensuring data privacy, security and ethical usage. * Technology Competence: GenAI model fine-tuning and implementation requires a somewhat different skill set than traditional software engineering and data science. Do you have the qualified talent to advise you on the technical feasibility of selected use cases and set up an effective model development process? Perhaps you may need a new role(s) to lead and execute GenAI-related initiatives. * Skills And Culture: GenAI adoption may be met with skepticism by employees who are unfamiliar with it. Over 67% of senior managers don't feel comfortable using advanced AI and data analytics tools. How are you planning to educate your people on the benefits, risks and limitations of new tools? Your workforce must understand the appropriate usage scenarios and governance steps, as well as feel empowered to use the technology available to them. Mature organizations develop internal AI governance frameworks to ensure safe technology adoption and effective change management. A typical framework covers privacy, ethics, compliance, performance, operations and security controls in place for the adopted systems. In conclusion, generative AI technologies can improve operating efficiencies, workforce productivity and employee satisfaction from the work they get to do. However, GenAI adoption also requires prudence from the executive teams, especially amid emerging AI regulations like the National Privacy Bill and the EU AI Act. Start having an open dialog at every level about the benefits and risks GenAI can bring into your organization to identify the optimal use cases, address skepticism and create excitement about the technology's vast potential. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?

Council Post: Interactive AI Systems: 20 Build Challenges (And How To Solve Them)

Seeking to tap into the power of artificial intelligence, many companies are working on interactive AI systems. Such systems can provide a variety of valuable benefits, from enabling always-on customer service to enhancing existing products to helping employees more quickly, easily and efficiently perform tasks. There are many cost, competitive and productivity advantages that a well-designed interactive AI system can bring -- but developing one comes with complications companies must be prepared for. Below, 20 members of Forbes Technology Council detail some of the challenges that come with building a robust, safe and well-functioning interactive AI system and how they can be solved. In my experience, the biggest issue organizations face when developing an AI system is framing the problem to be solved. Too often, AI is implemented on top of old, bloated processes with the aim of making a business function more efficiently. To deliver real impact, a more effective mindset is, "How do we use this technology to solve an issue that has been impossible to address before?" - Mark Cameron, Alyve Consulting A key challenge in developing an interactive AI system is ensuring you have the internal capabilities needed to build and maintain it, including data, infrastructure and expertise. Additionally, aligning stakeholders' and users' expectations is crucial. Misalignment can lead to dissatisfaction and underutilization of the AI system. - Fabiana Clemente, YData Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify? One challenge in developing an interactive AI system is minimizing hallucinations, where the AI generates inaccurate or nonsensical outputs. You can minimize hallucinations and ensure more accurate, relevant responses by leveraging proprietary knowledge and building robust workflows with continuous feedback from end users. - Rick Zhuang, Firework A key challenge in building an interactive AI system is ensuring relevant responses. With users making wide-ranging requests, there must be intelligent orchestration to grasp true intent and optimize inputs and outputs. Without such orchestration, interactions quickly become hit-or-miss. Orchestration will help the system develop trustworthy, contextualized responses and create seamless user experiences. - Alex Saric, Ivalua Creating natural, coherent, conversational AI requires advanced language models trained on massive knowledge bases and interactive environments. Curated training data and infrastructure enable rapid feedback and the safe exploration of cutting-edge techniques such as commonsense reasoning and open-domain Q&A. This iterative process unlocks the next era of interactive AI experiences. - Karan Jain, NayaOne Trust is a major challenge. The best way to build trust is to ensure that humans are in the loop. In machine learning systems, human-in-the-loop approaches are crucial for establishing trust in advance of full automation. Humans must treat GenAI outputs as if they were created by a junior employee, reviewing, validating and correcting these outputs as necessary. - Lalitha Rajagopalan, ORO Labs, Inc. Accuracy isn't just an internal development challenge; it's a hot topic for anyone discussing AI. Hallucinations are always a concern with interactive AI, but with the right guardrails in place, those hallucinations can be negated. Multiple layers of guardrails can ensure heightened accuracy, and implementing those guardrails should be just as big of a priority as AI development itself. - Frank Fawzi, IntelePeer Algorithmic discrimination is one of the main challenges when developing an interactive AI system. To minimize bias, companies need to explore creating privacy-focused AI models that leverage high-performance computing power and enable real-time, precise decision-making without necessarily compromising user privacy. - Justin Lie, SHIELD AI Technologies It will be difficult to manage the access rights for AI accounts. Moreover, if AI relies on faulty data sources or training, it could lead to serious problems. Another challenge is that large and sensitive data models may require human verification, which could take a lot of time. If hackers break into the communication between users and AI systems, they could perform insider attacks easily. - Thangaraj Petchiappan, iLink Digital The biggest challenge is earning and keeping users' trust. Ensure your system's information adheres to legal restrictions and moral guidelines, providing accurate data. Train AI models on diverse, legally acquired datasets. Handle user data with care, avoiding its use for model training, and ensure users retain ownership of their data. This approach helps maintain trust and complies with legal and ethical standards. - Slava Podmurnyi, Visartech Inc. Ensure the AI model can understand and respond appropriately to a wide range of user inputs, including ambiguous or poorly phrased queries. This challenge, in the domain of natural language understanding (NLU), can be addressed by training the AI model with large and diverse datasets. This broad coverage ensures the model can handle the various ways users might phrase their queries. - Andy Boyd, Appfire The big challenge is cultural sensitivity. Different cultures have unique norms, making it difficult for an AI system, often trained on broad or universal values, to cater to all appropriately. This can be overcome by creating sovereign solutions tailored to specific regions and using local datasets to train the AI. This approach ensures that the system is aligned with the cultural context. - Andre Reitenbach, Gcore One emerging issue in developing an interactive AI system is ensuring data privacy and security. This challenge can be addressed through robust encryption protocols, stringent access controls and continuous vulnerability monitoring. Additionally, integrating privacy-by-design principles during development ensures proactive data protection, preempting potential issues before they manifest. - Christopher Rogers, Carenet Health A company must sanitize the inputs to its AI system or risk the exposure of sensitive data or the system behaving in unpredictable ways. Using techniques such as paraphrasing user inputs ensures that users aren't hijacking your model through the inclusion of malicious prompts. You should also be checking and validating outputs to ensure they match expected targets and behaviors. - Matt Dickson, Eclipse Telecom People tend to assume AI systems are correct, and therefore, they don't edit or override the results. This could be because they're busy, lazy or don't feel confident questioning the output. But AI is imperfect and still requires human verification for many tasks. Companies need to prepare for this and be explicit about the level of intervention needed to get accurate results. - David Talby, John Snow Labs A challenge in developing interactive AI is ensuring it's well-timed. Consider chatbots: They often pop up at random, offering help or suggestions that may be wholly irrelevant to you. Particularly in industries such as commerce, you want to engineer your AI around key moments in the buying journey. It should interact with consumers when they're actually open to receiving support or guidance. - Raj De Datta, Bloomreach The best types of interactive AI systems are ones that foster personalization. Accomplishing this starts with feeding your algorithms the right data and, even more importantly, the right context. Training systems on the wrong data or too much data inherently creates bottlenecks. Tapping into the right datasets directly leads to customized and intuitive interactive AI systems. - Gleb Polyakov, Nylas A major challenge in developing an interactive AI system is that large language models output natural language, while most systems use formats such as JSON or XML. Overcoming this requires a conversion layer to translate LLM outputs into structured formats. Utilizing robust prompt engineering techniques in this layer can accurately map and transform responses, ensuring seamless integration with existing systems. - Hashim Hayat, Walturn Most AI models today operate as "black boxes," offering very little visibility into how decisions are made. This can lead to biased or unsafe decisions. As far as possible, companies leveraging AI in their products should prioritize transparency, including providing information about how the AI functions and how they manage user data. - Mike Britton, Abnormal Security An interactive AI system needs to be customized for the user and purpose, and this requires contextual data. When developing custom AI models using retrieval-augmented generation, properly managing user access and enforcing data permissions is crucial for security. This starts with classifying data and carefully identifying and enforcing permissions to access data that are in line with the business purpose of the system and the user. - Claude Mandy, Symmetry Systems Inc.

Council Post: Is Generative AI The New Hero Of Your Consumer Products And Retail Business?

These days, it's impossible to avoid the buzz about generative AI (GenAI) in the consumer products and retail space, and most of it relates to general consumer attitudes and consumer adoption -- in other words, what people think of it and if they're using it. Those are interesting questions, but it's not just whether consumers are using GenAI; it's about how -- because it's the "how" that will shape the future of the industry. But the picture is broader than this. GenAI is being used in various ways in the consumer packaged goods (CPG) and retail industries, and not all of them are directly customer-facing. For example, we're seeing the use of GenAI in supply chain networks. This is one area in which efficiency gains can make the biggest difference to a CPG business. GenAI is being used in conjunction with predictive AI in areas such as supply and demand planning. The insights it provides have enabled some organizations to increase levels of automation. It means that businesses need to manually handle exceptions in a few cases only, which allows them to focus on more strategic aspects. The same combination of predictive AI and GenAI is being used to make forecasting more granular. Instead of traditional A/B testing -- trying these two approaches in parallel and gauging the results -- sales representatives can use artificial intelligence to gauge the likely flows of inventory more easily and can assess brand and product impact in individual stores. This will help CPG organizations and retailers work together to adjust their offer -- what they sell and where -- and enhance their formulations for maximum advantage. GenAI is also being used to address issues of sustainability. Organizations can use the technology to make their product/offer more sustainable, and at the same time make the production process more cost-efficient. In doing so, they can enhance the value of products or services for key demographics for whom these factors are especially important. It's early days for developments like these, but they're happening. Time and again, we see how important the relationship between CPG businesses and retailers is becoming and how great the potential benefits of their cooperation are. In AI, this means the aggregation and collation of data sets can enable both parties to explore not just what people buy but also why people buy. The larger the body of data, the more robust it will be, and it can help retailers and CPG organizations alike to identify factors that lead to purchases. They can develop processes that accurately target product development and fulfillment specific to geographies, with less R&D and a faster time to market. I already mentioned the consumer buzz around GenAI. CPG companies and retailers can use GenAI to further personalize reach with varying demographic behaviors. For instance, online influencers are trusted by some demographics, Gen-Z in particular, and so we're seeing smaller brands using AI to develop avatars or virtual influencers to extend reach across new social media channels. Similarly, the use of GenAI in marketing materials such as copywriting and photo generation is going to be joined by moving images. Text-to-video conversion platforms, under test at the moment, could change the way commercials are made and accelerate their use. However, concerns around the use of GenAI remain. People aren't naïve. According to the Capgemini Research Institute (CRI), almost two-thirds of consumers (62%) harbor concerns about GenAI producing false or misleading testimonials or reviews (pg. 20). A similar proportion (61%) of AI-aware consumers were concerned about the possibility of bias in GenAI models leading to unrepresentative results. Organizations using or planning to use GenAI in CPG and retail environments need not merely bear these concerns in mind but need to address them directly. Confidence and trust in a solution depends on putting effort into making it work reliably, in people's best interests and in line with their expectations. For example, this means that GenAI models may hallucinate as a result of inherent bias, limitations in the training data and lack of real-world understanding. In general, though, the stats are upbeat. According to Capgemini's research report, over half (52%) of users have replaced traditional search engines with GenAI tools for product recommendations (pg. 14), and two-thirds of them (66%) welcome such recommendations (pg. 16). Stand back and take stock, and you'll find a common theme here in all of this: GenAI, while exciting and transformative, is a technology that serves rather than drives business. The focus, as ever with new tech, should be not on its dazzling potential but on what organizations want to practically achieve with it. Improved efficiency, greater sustainability, higher customer satisfaction? That's a pretty good start. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?

Council Post: The Generative Physical AI Revolution: Transforming Industries With Autonomous Technologies

Dr. Ravi Changle, Director - Artificial Intelligence and Emerging Technologies, Compunnel. The era of physical AI and generative physical AI (GPAI) advancements is transforming automation from performing repetitive tasks to enabling adaptive, intelligent interactions in the real world. This transformation is exemplified by the GPAI-powered collaborative bots and rovers, which are designed to enhance operational efficiencies across various sectors including agriculture, retail, transport and manufacturing. Physical AI primarily focuses on enabling systems to interact with and manipulate their immediate environments through direct mechanical means. These systems, equipped with sensors and actuators, perform predefined, repetitive tasks that typically require human-like actions such as picking, placing or navigating. It excels in environments that demand consistency and precision such as on assembly lines or in autonomous vehicles operating in controlled settings. Its capabilities are centered on executing well-defined tasks efficiently without the inherent capacity for adaptation or learning from past interactions beyond their initial programming. GPAI represents an advanced evolution of AI technology, integrating the ability to autonomously generate and adapt behaviors based on accumulated experiences. Using machine learning, deep learning and large language models, GPAI systems can learn from and mimic human demonstrations and simulations, continuously improving their efficiency and adaptability. These systems are designed to handle complex, dynamic tasks that require decision-making in unpredictable environments, such as personalizing customer interactions in retail or managing diverse materials in manufacturing settings. Unlike traditional physical AI, GPAI systems thrive on flexibility and learning, making them suitable for applications where tasks and conditions are continually changing. Nvidia has entered the GPAI space through the introduction of its Omniverse and Isaac platforms. At Compunnel, meanwhile, we have become major contributors by filing a patent for our GPAI-powered collaborative bot. In agriculture, a bot could significantly enhance precision farming. By integrating with IoT sensors, it would collect real-time data on soil conditions, crop health and weather patterns. This data would be processed to optimize resource use such as water and fertilizers and to implement targeted interventions, leading to enhanced yield and sustainability. For instance, a bot deployed in a vineyard could monitor vine health and provide precise irrigation recommendations, improving grape quality and reducing water usage. In retail, a bot could transform inventory management and customer service. By integrating with store IoT devices, it could monitor stock levels in real time, predict demand and automate restocking processes. This would reduce inventory costs and ensure product availability. For example, in a supermarket, a bot could interact with customers to guide them to products, provide personalized recommendations based on shopping history and manage stock replenishment autonomously. In the transport sector, a bot can enhance route planning, fleet management and fuel efficiency. It would process real-time data from GPS and traffic sensors to optimize delivery routes, reducing travel time and fuel consumption. For example, a logistics company could use a bot to dynamically reroute delivery trucks based on current traffic conditions, ensuring timely deliveries and reducing operational costs. In manufacturing, a bot could streamline production processes and reduce downtime through predictive maintenance. By analyzing data from machinery sensors, it can predict potential failures and schedule maintenance before breakdowns occur. For instance, in an automotive factory, a bot can monitor the health of assembly line robots and alert technicians to perform maintenance, minimizing production interruptions and extending equipment life span. To fully capitalize on the transformative potential of GPAI, businesses must establish a sophisticated technical infrastructure capable of supporting the intense computing demands of GPAI systems. This foundation includes high-performance GPUs and TPUs for rapid model training and data processing, alongside edge computing devices like Nvidia's Jetson series to process data on-site, minimizing latency. Robust networking infrastructure is also vital, incorporating 5G or advanced Wi-Fi for high-speed data transmission coupled with secure networking solutions like VPNs and end-to-end encryption to protect sensitive data. Additionally, scalable storage solutions are required to handle the vast data outputs from GPAI applications, using a mix of data warehousing and hybrid cloud storage for flexibility and scalability. The development and simulation of GPAI benefit immensely from platforms such as TensorFlow or PyTorch and tools like Nvidia Omniverse for realistic simulations, which are crucial for training AI before real-world implementation. Moreover, stringent cybersecurity measures, regular software updates and energy-efficient power management systems ensure that GPAI systems are not only effective but also secure and sustainable. Investing in this advanced infrastructure is essential for businesses aiming to leverage GPAI for enhanced operational efficiency and innovation. With great power comes great responsibility. As GPAI systems take on more autonomous roles, ensuring their security from cyber threats becomes paramount. Businesses must implement stringent security protocols and continuously monitor their systems to protect against breaches. Additionally, ethical considerations around privacy and autonomy are critical. Companies must navigate these issues transparently to maintain trust and compliance with global standards. One of the most exciting developments in GPAI will be the use of digital twins and simulated environments for training and testing. Platforms like Omniverse can allow businesses to create detailed simulations that mirror real-world operations, enabling the testing of hypotheses and the training of AI systems in risk-free environments. This not only enhances the reliability of GPAI technologies but also accelerates their deployment and integration into existing systems. The generative wave of physical AI is redefining the possibilities within numerous industries, driving innovation and prompting companies to rethink how they operate. As this technology continues to evolve, I believe it promises to unlock new levels of efficiency, enhance customer experiences and solve complex challenges that were once thought insurmountable. Embracing GPAI is not merely an option for forward-thinking businesses -- it's becoming a necessity to stay competitive in a rapidly transforming world. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?