Agentic AI: The Future of Autonomous Enterprise

The meteoric rise of AI in the past few years has had the entire world either ecstatic about its endless capabilities or skeptical of its eminent substitution of human value. Regardless of the position taken by businesses across the globe in terms of planning and implementation, evidence indicates that most business leaders view this technological disruption to inevitably impact their organizations. Enterprises are entering a paradigm where autonomous AI scales business productivity and operational efficiency beyond traditional measures for growth. By 2026, over 20% of business leaders will use AI.

Reinvention-focused enterprises will look to capitalize on cost savings, operational efficacy and expedient organizational design by leveraging autonomous systems. The result? A greater pivot towards creating value for customers and growing business potential beyond borders and verticals.

Agentic AI is the new face of operational excellence in 2025, as evidenced by its exponential market growth in the past year (see below). It will enable businesses to establish themselves as customer-focused, solution-oriented disruptors, at the forefront of innovation.

Agentic AI has pushed the envelope of autonomous systems, evolving large language models (LLMs) to go beyond singular task completion to resolve complex challenges for greater business value.

With autonomous agents, the paradigm shifts from passive information processing to active problem-solving, marking a significant turning point in the application of Agentic AI. Reinvention-ready enterprises understand the potential of these autonomous systems in terms of scaling growth; and through their implementations have experienced 2.4x increased productivity and a 2.5x revenue growth in 2019-2023 when compared peers with the lowest automation readiness ​(Accenture 2024)​.

Recognizing the critical role of Agentic AI in driving accelerated business growth is essential for enterprises seeking to maintain a competitive edge. Modern retail organizations operate within an increasingly complex landscape characterized by intricate global supply chains, rapidly evolving consumer expectations, and an exponential surge in data generation.

Effectively addressing these challenges not only mitigates operational inefficiencies but also fosters the development of core competencies that drive sustainable competitive advantage. Traditionally, leading global retailers have leveraged technology to establish market dominance; however, these advancements have largely been built on capital-intensive, proprietary systems, often limiting broader accessibility and scalability.

Today the computational power of AI has narrowed that gap. Businesses now can leverage artificial intelligence to create sustainable systems at a fraction of time and cost. This technological shift is actively reshaping the market hierarchy, allowing smaller businesses to compete by scaling rapidly. Most enterprise leaders therefore are rethinking conventional strategies to reform their business processes with AI integration, to achieve greater expediency and agility.

It is crucial to recognize that while this technological disruption will give rise to new business leaders, the market will continue to reward first movers - early adopters of AI are 3x more likely to report significant revenue growth​ (McKinsey 2022)​.

As technology becomes commercialized, it transitions from being a competitive advantage to an industry standard. Sustainable competitive advantage, therefore, is not merely derived from adoption but from innovation to create a clear differentiation. According to a study, companies developing proprietary AI technology are 40% more likely to maintain a competitive advantage over a decade ​(Harvard 2021)​.

Enterprises that are still in the planning stages of this shift will lose ground given the disruptive nature of this shift and are likely to play catch-up to reinvention-focused businesses. Implementation of autonomous intelligence agents can lead to significant changes operationally and therefore necessitates a shift in workforce roles, requiring employees to transition from traditional workstreams to orchestrating and overseeing AI systems.

Early implementation therefore allows businesses to assess skill gaps and provide required training to ensure that the workforce is ready to work (McKinsey 2022).​ Similarly, another study reveals that businesses now view the implementation of autonomous intelligence as a critical priority and therefore are looking to bring in talent to help design systems to accelerate decision making ​(Deloitte 2020).

In another report by Deloitte, 71% of early adopters had signaled that AI innovation led to changes the roles of human workers in their enterprises ​(Deloitte 2020)​. While concerns persist about autonomous agents and their potential for replacing humans, data indicates that is very likely that AI itself will require orchestration and therefore roles will evolve.

The productivity gains from the system stem from the capacity and capability scaling of the human workers, i.e. managing increased workloads without proportionally increasing human resources (IBM 2025). Agentic AI solutions are therefore likely to enrich job roles and create more opportunities such as enhancing productivity by enabling workers to focus more on creative and strategic tasks, ultimately improving operations to foster innovation and create greater value.

This paradigm shift offers business leaders a unique opportunity to leverage tools that augment human decision-making and actively integrate them into operational processes. Studies show 31% of workers anticipate AI will increase their productivity and efficiency (PWC 2024).

Executives in the US view implementation of AI more favorably with 72% anticipating productivity increases (KPMG 2023).​ By forging a symbiotic relationship between workers and autonomous intelligence agents, businesses can transform their workplace into a hub of ingenuity and adaptability, paving the way for sustainable innovation.

The need for agentic AI assimilation into existing operational systems requires a steady but strategic shift from existing structures to more sophisticated ones. Most of the operative functions in place right now will become irrelevant faster than businesses will be able to replace them, so catching up is essential. Incorporating AI structures into business operations will allow businesses to improve customer experiences, and brand vitality by fully utilizing the capabilities of AI to automate and accelerate productivity.

Take the smart television platform TiVo, for example. It streamlines IT operations by using a machine learning platform which fast-tracks its information processing allowing it to automatically detect, classify, aggregate, and route IT incidents. Through this AI-aided process, TiVo has been able to reduce actionable events from about 2,500 to 150 daily, allowing professionals in their network operations center to conveniently manage highly complex operations around the clock ​(Deloitte 2020)​.

Enterprises are replacing legacy systems and man-powered operations with autonomous agentic infrastructure. Imagine a world where products reach customers with unmatched speed and precision, thanks to an ecosystem where packaging, assembly, and delivery are orchestrated by intelligent agents. This translates to enhanced customer satisfaction, reduced lead times, and a competitive edge.

Businesses that were the first to adopt intelligent systems are seeing the results of their decisions faster than those hesitant to change their organizational structure, because they prioritized the improvement of their talent, assets and platforms along with their methods and processes to achieve a holistic organizational stability, with AI-led reinvention ​(Accenture 2024)​.

This is but one instance of how Agentic AI can transform the way enterprises operate. Industry leaders predict that 70% of organizations will implement AI to assist decision-making processes by 2025. The new creed of Agentic AI has evolved beyond automating routine tasks and singular-task functionality to autonomously plan and resolve operational challenges before they escalate ​(Gartner 2021)​.

Consider the implications of multi modal agents working intuitively within a clothing manufacturing facility: Multi-modal AI agents can revolutionize production by seamlessly integrating data from design systems, fabric quality sensors, cutting machine interfaces, and inventory tracking platforms. These intelligent systems continuously monitor intricate manufacturing variables like fabric tension, color consistency, pattern alignment, and material waste; enabling real-time optimization of the production workflow.

How does an Agentic AI infrastructure expedite these processes? It analyzes complex parameters like fabric performance, machine precision, and design specifications. A network of autonomous AI agents can dynamically adjust cutting patterns, predict potential material inefficiencies, and ensure each garment meets precise quality standards.

This approach allows manufacturers to significantly reduce fabric waste, minimize production errors, and accelerate design-to-market lags while maintaining exceptional product quality and consistency.

While autonomous agents can proliferate industrial potential and change the face of operational efficiency, the deep learning capabilities of Agentic AI are greater than what industry leaders have been willing to, or able to explore.

The current untapped potential of AI is a source of both excitement and concern. The transformative power of Agentic AI can create new opportunities but can also simultaneously redefine what we know now as organizations.

As enterprises grapple with the promise of autonomous systems, the underlying technological potential of generative AI agents tells a compelling story of exponential advancement. From single-task algorithms to multi-modal intelligent systems capable of processing, interpreting, and acting across diverse data domains, the journey of AI agents reflects a profound shift in how we conceptualize machine intelligence. What we know of Agentic AI will likely change a few times over as we begin to grasp what's been explored already.

Consider the groundbreaking success of ChatGPT in 2022, the advanced LLM took the world by storm reaching 100 million users in the first 60 days of launch. Setting a new benchmark with GPT 3.5, the LLM redefined the potential of AI effectively sparking a new race for innovation. Just 2 years in the technology that seemed so disruptive at the time is now an indispensable part of 300 million users, including professionals who utilize it to learn and curate their lives. While ChatGPT was not the first commercial AI application, it did bring the potential of agentic AI to the public periphery and accelerate the open-source AI innovation around the world.

With the creation of new Gen AI models every two and a half days​ (Gartner 2024)​, enterprises have a fertile ground to integrate their operations with AI infrastructure to proliferate their operations and achieve unprecedented productivity and outcomes in record time. To successfully inculcate Agentic AI modalities into your workforce and operational design, it is important to understand its value within your business ecosystem.

The preliminary versions of generative and assisted AI were processing singular information streams to respond to individual queries and resolve singular tasks. This is but one tiny aspect of intelligent AI's diverse capabilities.

Enhanced Productivity

Agentic AI is capable of automating time-consuming, labor-intensive tasks that require multiple processes and interaction with external systems. They can minimize human intervention from production processes by adapting to changes and addressing errors for optimized outputs. They don’t require breaks and can work around the clock, allowing businesses to scale their product development as much as they like. Consider Siemens utilizing AI-powered simulations and digital twins to optimize their production through real-time data capture and autonomous agentic decision making to adjust their production speed and output ​(Siemens 2024)​.

Resource Optimization and Cost Saving

While AI has certainly reduced labor costs for companies by absorbing manual labor tasks conducted by human staff, that is the most apparent cost saving benefit. It allows businesses to predict energy and material waste and adjust processes accordingly, as well as respond to any downtime errors by allocating resources like equipment, energy or inventory to make up for operational lags.

The advantages of Agentic AI optimization for logistics and shipping companies are huge. For example, Maersk optimizes delivery times and shipping routes with Agentic AI forecasting, to manage end-to-end shipping processes, by preemptively assessing weather conditions and customer demand and change course as needed for improved customer experience ​(Maersk 2024).

Enhanced Customer Experience

The one key function most businesses are looking to optimize through use of Agentic AI is customer service. Autonomous agents can significantly improve user service journey by reducing wait and response time, tackle issues or dissatisfied customers while personalizing interactions through real-time analysis using data on buying patterns and preferences for an enhanced customer experience.

Apart from the obvious brand reputation management enabled by agentic AI with this shift, it allows business owners to reorient their workers and resources to create more value by fully utilizing their creative potential.

The leading beauty brand Sephora tackled its cart abandonment challenges by partnering with Glimpse Group’s Agentic AI tool to create Virtual Artists that allowed customers to visualize makeup products to increase their buying intent with visual proof. The implementation resulted in spiking Sephora’s sales by 35% due to personalized customer experience optimization ​(Glimpse 2024)​.

Agility and Scalability

Another underrated benefit of leveraging Agentic AI is furthering the cause of scientific research, by enabling industries like pharmaceuticals, healthcare, and chemical manufacturing to improve the impact and efficacy of their research. With purpose-driven modeling, autonomous agents can be trained to assist researchers validate their hypothesis through use of AI-based simulations and data analysis. This helps businesses scale capacity, For example in the case of a pharmaceutical business, autonomous agents can optimize preclinical and clinical research, enhance regulatory compliance, drive personalized medicine, and uncover new therapeutic applications—all while reducing R&D costs and improving the success rate of new treatments.

Take the case of IBM’s AI for Drug Discovery track that enabled researchers to collaborate with AI experts to discover groundbreaking implications using autonomous agents. They designed new drugs and tested a wider pool of candidates in record time, cutting down costs and risks for the healthcare industry ​(IBM 2024).

The disruptive nature of Agentic AI ensures that innovation becomes accessible and possible across all industry verticals. As companies pivot towards generating value in their products and establishing trust in their customers, AI could lead the charge in realizing ideas faster.

The role of agentic AI has shifted from comparatively rudimentary structures to sophisticated and incredibly intricate data ecosystems, capable of reacting to their environment, and making decisions based on real-time challenges with curated and resolution-focused actions.

Businesses can train them to use tools that access real-time feedback and suggest a viable, actionable solution. By leveraging advanced machine learning algorithms and interconnected neural networks, these AI agents can comprehend contextual environments, predict potential challenges, and autonomously develop adaptive strategies to address issues that businesses may not even be privy to. Various combinations of these agentic models, and their diverse capabilities can be grouped to solve complex challenges and ease decision making. To understand exactly what drives this level of comprehensive data processing, this study looks towards the core foundational components that make up the agentic architecture. There are three components that make up the agent; the model, the tools and the orchestration layer.

The Agentic Framework

Think of this vast infrastructure as the connection between a puppeteer, the puppets and the play.

The Play - The Overall Business Goal

The play represents the overarching business objective or task the Agentic AI system is designed to accomplish. This could be optimizing a supply chain, automating customer support or managing a manufacturing process. The play has a narrative and character that lead to the desired outcome; the business goal.

The Puppets - The Models

The puppets are the models. This can include large language models or other AI/ML models trained on specific data and use-cases. They therefore are the individual actors performing specific actions within the play. Each puppet has its own specialized skills and limitations. Some are good at performing complex calculations, others at generating conversational text or systematic reports.

The models are the core intelligence, responsible for understanding instructions, processing information, applying reasoning (as per training and self-learning), and generating favorable outputs.

They are the brains behind individual actions. If we imagine the supply chain layout as an example, one puppet (model) might be adept at forecasting demand based on historical sales data. Another might be skilled at optimizing delivery routes based on real-time traffic and weather conditions.

The Strings and Props - The Tools

The strings and props are the tools. They are how the puppets (models) interact with the stage (the real world). Tools bridge the gap between the agent's internal capabilities and the external environment. They allow agents to access external data, perform actions through integrated and/or connected systems, and interact with the real world.

The strings allow the puppeteer to control the puppets' movements. Props, like a telephone or a map, enable the puppets to perform specific actions within the play's narrative. Without strings and props, the puppets are just inconsequential objects, unable to participate in the play. To optimize the supply chain process, tools could be APIs to access inventory databases, track shipments, communicate with suppliers, or retrieve weather forecasts.

The Puppeteer – The Crucial Orchestration Layer

The orchestration layer is the central control system that encapsulates the entire structure and directs the component-to-component Agentic AI process. It determines which model to use, when, and how to use the tools, and in what sequence to perform actions to achieve the overall goal of the play, which is the ultimate business objective.

The puppeteer ensures that all the individual actions of the puppets come together to create a cohesive and effective performance. They read the script, the overarching goal and understand the desired outcome. They then decide which puppet to use for each scene, how to maneuver their strings, and which props they should interact with.

Recall the supply chain management scenario again. The orchestration layer might recognize a sudden spike in demand (the first information intake). It then instructs the "demand forecasting" puppet (model) to analyze the data. Based on the forecast, it directs the "inventory management" puppet to check stock levels using the inventory database API (tool). If inventory is low, it uses the "supplier communication" tool to contact suppliers and initiate a new order.

Based on the business function or goal at hand, agents can engage one or more of the language models to establish communication across their infrastructure. These models possess various levels of capabilities in following instruction-based reasoning and logic frameworks commonly divided among these three; ReAct, Chain-of-Thought, or Tree-of-Thoughts.

ReAct combines the logical though process, reasoning, with action, by executing tasks in the real world, based on the data or input they receive from their environment. They plan the next step using reasoning and then execute it within the environment as an action.

For example, in an E-commerce business environment, the goal is to resolve a customer query using a chatbot.

• The chatbot receives a customer query like, “I need to return a defective item.” The system identifies the steps to resolve the issue.
• Either it will check if the customer is within the return window, or provide the return policy, or process a refund if eligible.
• The chatbot responds, “Your item is eligible for return. Here’s your return label. Please ship it back within 7 days.” The customer raises additional concerns, like “I didn’t get the label.” The cycle repeats to resolve further issues until the goal is met.

Chain of Thought (C-o-T) is the model that breaks down complex problems into a series of intermediate reasoning steps. They ensure correctness and due diligence by addressing each subsequent step. They focus on the question or problem at hand, then divide it into smaller actionable steps, solving one at a time in a sequence. As the last process, they would synthesize the solution by considering the viability of each step.

Imagine an inventory management challenge for a manufacturer. If they mobilize an agentic structure to predict restocking parameters for a particular product line. A C-o-T model will conduct the following steps: 

• Analyze sales data for the products, to see sales patterns in relation to historic trends or through external markers.
• Use real-time analysis to adjust current inventory levels with predicted demand. Let’s assume that the agent assesses that the inventory for a newly introduced product will deplete in a particular region within 2 weeks at the predicted sales rate.
• Check and assess the supplier’s lead-time and generate procurement function in accordance with the requirement, e.g. the agent assesses that it will take 1.5 weeks to deliver new stock and define the action to be to restock immediately to avoid stockouts and meet growing demand.

Tree-of-Thoughts (T-o-T) is the most evolved model out of the three that explores multiple reasoning paths simultaneously in a tree-like structure, evaluating multiple hypotheses or solutions at each step. It is ideal for creative tasks that require ideation or detailed decision making, by finding the most diverse and suitable solutions.

• It starts with the problem or question, then expands the possible solutions into branches.
• It evaluates each branch to decide which path to pursue further while pruning unviable branches and continue growing the promising paths.

If the challenge is optimizing a logistics network for a shipping company, the goal for this model is to decide the best distribution strategy for shipping the goods. It will identify the root problem first; minimizing shipping costs while ensuring timely delivery. Based on its dynamic information processing it will provide one or more branches of solutions, to provide detailed decision-making support with reasoning and evaluations.

Branch 1 (Solution A): Consolidate shipments into fewer, larger batches.

• Sub-Branch A1: Reduced cost per shipment but increased delivery time.
• Sub-Branch A2: Potential delays may impact customer satisfaction.

Branch 2 (Solution B): Use multiple smaller shipments for faster delivery.

• Sub-Branch B1: Higher costs due to frequent shipments.
• Sub-Branch B2: Improved customer satisfaction and loyalty.

It then provides a solution that will have the best outcome for your logistics challenge; choose a hybrid strategy where high-priority items ship fast while others consolidate for cost efficiency.

Industry leaders have zeroed in on the potential for agentic models to reform their operational and organization design. In a short amount of time, studies have predicted that the faith in Agentic AI has soared and continues to get stronger as businesses witness higher returns on their investments in leveraging generative agentic AI models into their systems.

What is noteworthy, however, is that the assimilation of AI into global work structures is neither new nor novel. It has been an underlying structure to businesses and global operations for a decade and is now emerging in the open-source domain with greater velocity. AI is not going to be the next big thing; it is already here.

The hyper-competitive global marketplace has created a new conundrum for business, as competitiveness is deeply connected to an organization’s ability to innovate consistently. Therefore, leaders are rapidly recognizing that traditional operational models are heavily dependent on human capital for routine tasks no longer optimal for sustainable growth and success.

The integration of agentic AI represents a strategic pathway to improved customer experience and brand vitality through intelligent automation and companies are surely gaining momentum to beat the race. The number of AI-integrated and fully modernized companies have doubled from 9% to 16% in 2024 ​(Accenture 2024)​.

Early adopters across sectors report significant operational transformations; reduced costs, enhanced customer satisfaction, and unprecedented market responsiveness. This shift extends beyond automation—it fundamentally reimagines how enterprises leverage intelligent systems for sustainable competitive advantage across diverse industry verticals.

But the race is far from won for now. Despite the enthusiasm of corporate leaders on embracing the innovative capabilities of AI, there is still considerable discrepancy between the speed with which AI is evolving versus how quickly companies are designating and developing them at scale. McKinsey relayed in an April survey that only 6% of organizations have achieved scale in 2024, despite 45% of them piloting new gen AI projects ​(McKinsey 2024)​.

Enterprise excellence is contingent upon ensuring additional value creation on a consistent basis. The inherent and sustainable advantage of agentic structures echo in the way companies respond to real-world challenges of customer experience, supply chain optimization and gaining competitive market advantage, to name a few.

Mitigating Risk in Financial Services

Perhaps one of the most valuable aspects of agentic AI is easing and regulating decision making processes for industry leaders. Agentic AI models can process data in real-time and respond with curated problem solving to optimize decision making in the long run with constant learning and improvements.

JP Morgan Chase decreased fraud losses by leveraging AI models to detect fraudulent transactions, that saved them from significant financial losses. They also are exploring agentic AI capabilities to identify and predict fraudulent patterns in payment screening, and customer analysis in advance to prevent them ​(JPM 2023)​.

Despite the dramatic shift in retail strategies in the past decade, customers still inspire and dictate how brands approach them and sell their products. Innovative retailers have changed the game with Agentic AI to not only transform inventory management for expediency, but customer experience by orchestrating dynamic pricing, stock optimization, and personalized engagement. The retail giant Walmart has witnessed significantly positive outcomes in their inventory management processes by incorporating autonomous agents.

Their proprietary intelligent inventory system coordinates with 4,700+ stores, reducing out-of-stock instances by 30% ​(Walmart 2023)​. Meeting customer demands is paramount to remaining on their radar, and Walmart can utilize autonomous systems to achieve that as such:

• Predicting potential out-of-stock situations before they occur.
• Agents autonomously make pricing and promotion decisions to optimize sales and minimize waste.
• Adjusting replenishment orders based on these dynamic factors so that each store has the right amount of inventory at the right time.
• Coordinating different aspects of the supply chain to ensure efficient and timely delivery of products to stores.

Vision-enabled agentic AI is perhaps the most contemporary example of how AI can actively and dramatically enhance business operations for some industry verticals over others. Vision AI has revolutionized quality control and process optimization in manufacturing. These systems enable real-time defect detection, predictive maintenance, and automated quality assurance, significantly reducing waste and improving product consistency. Agentic AI optimization has the potential to create a paradigm shift in vehicle manufacturing, among others, on the same scale as the disruptive “assembly” line introduced by Ford in 1913.

BMW's vision AI quality control system as an example can inspect 600 vehicles daily with 99.8% accuracy​ (BMW 2019)​. These systems enable real-time defect detection, predictive maintenance, and automated quality assurance, significantly reducing waste and improving product consistency through these possible processes:

• Surface level defect detection by identifying scratches, dents and inconsistencies in paint, or imperfections in materials like leather or wood trim.
• Detecting misaligned parts, gaps, or flushness issues that might affect functionality or aesthetics.
• Beyond aesthetics, autonomous agents can analyze the operation of moving parts (like doors, windows, sunroof) to identify subtle malfunctions.

The Bid to Harness AI Potential for Universal Operability

With every new technology, there is uncertainty and skepticism. The debate around the commercialization of AI is something that brings with it its own set of ethical dilemmas. For example, most of the commercial LLMs have been created by utilizing the private and public web. Furthermore, closed-source AI models can be problematic given the unpredictability of the technology and biases created through data consumed and/or the training process implemented. Bringing AI within the public domain, with diverse contributors in an open-source environment has fueled invention and ideation to unprecedented levels since OpenAI released its first open-source tools to the public back in 2016.

But that growth only accelerated with the provision of pre-trained NLP and LM models by Hugging Face the same year. Why is that important? It granted agency to researchers like Rhyght, a health sciences organization, to empower their own models for greater scalability, that improved inference speeds and eliminated rate limit concerns​ (HuggingFace 2024)​.

The open-source framework of Hugging Face allowed real-world change makers to access AI development capabilities without heavy licensing or bureaucracy; and it hosts more than 50,000 openly accessible models, enabling positive change and collaboration in the community.

Not very long after, Tesla introduces its proprietary Autopilot model with deep learning ability that mobilizes a 48-piece neural network of autonomous agents capable of performing highly complex and time-sensitive computations​ (Tesla 2023)​. This agentic infrastructure has groundbreaking implications; not only can it acquire and decode real-time data from its temporal space, but it is also capable of developing complex algorithms and calculations to predict uncertainty and pitfalls well before they occur. Now consider the sheer potential of such sophisticated technology, and it makes sense why Tesla would want to establish guardrails around the system they have created so painstakingly.

Accessibility of AI

Embedding AI into the fabric of the business world, and beyond is more than just about providing access and fueling invention. Among the top companies in the world, the implications of AI are being measured holistically.

There are primarily two pertinent challenges that AI adopters must resolve:

• With the rise of open-source AI platforms making AI more accessible, how can compliance be effectively enforced for non-commercial private models? 

The growth of opensource AI platforms will enable innovation and reinvention on a global scale, where diverse contributors can access opensource models and modify them to fit their specific use cases, and realize their efforts in record time. While it may seem challenging to regulate mass-development of AI models at scale, with more people accessing public AI projects, spotting biases, discrepancies and security vulnerabilities ensures a shared responsibility of creators regulating the use of AI within a public domain and resolving issues of data compliance, intellectual property rights, and ethical AI usage

• Will the dominance of proprietary AI models enable companies to monetize their AI platforms while strategically controlling the distribution of their innovations?

A critical point to consider is the cost of developing AI models, and their subsequent monetization. Companies prefer to use opensource models to bypass licensing fees and develop their projects without committing to one proprietary model. They can empower their internal teams by collaborating with independent developers for nuanced innovation, while retaining the flexibility to scale their projects economically, and effectively to fit various use cases across diverse industry verticals. Boston Scientific was able to utilize an existing open-source AI model for its stent inspection processes, securing $5 million in savings against a budget of $50,000, while achieving the desired accuracy in their stent inspection process​ (IBM 2024)​.

A great example of both these nuances balanced in one sophisticated AI model is IBM’s Granite model series. While staying committed to opensource development of AI, IBM ensures that their platform remains accessible to independent developers by providing detailed insights on their key model components, and training datasets on platforms like GitHub and Hugging Face. But they also offer proprietary versions built with their Granite models like the IBM watsonX, with specific training sets and codes meant for enterprise AI applications to monetize their invention. The key is balance, that allows commitment to opensource development, while fueling enterprise growth with proprietary models (IBM 2024).

The accelerated development of AI models in the past few years, both within the opensource and commercial realm has opened the door for innovation. There are though some key considerations that can determine which model suits your development needs.

With organizations like national defense, transportation, aerospace and even the chemical industry, robust security measures and complex data management requirements often dictate the need for creating proprietary models. These models can be trained to service acute problems specific to the use case.

As highlighted earlier, one such example for an AI Model is the BMW's vision AI control system. These systems enable real-time defect detection, predictive maintenance, and automated quality assurance, significantly reducing waste and improving product consistency through these possible processes:

The Autonomy of Proprietary Models

Exclusivity allows companies to create true competitive advantage. With strategic gatekeeping of their codes, and data sets, companies developing proprietary models also have greater control over market use. While innovation on opensource is much more agile, with proprietary models, organizations can orchestrate the narrative and the functionality of niche AI platforms.

The growth and impact of the AI model remains within the control of the creators, ensuring consistency and longevity of their market performance.

Community Support for AI Development

With opensource AI models, innovation cycle quickens. Companies, therefore, quickly realized the value of crowd-driven research and initiatives that can empower AI development. Developers use platforms like GitHub and Llama to share their models and access other projects providing feedback and seeking expertise without gatekeeping where needed. It can be challenging to find specific technical support, with varying opinions on the opensource platforms. That is the slight edge with commercial AI projects, because they work with a specialized, dedicated team of experts who can provide targeted support for greater operability.

Beyond Automation – The Symbiotic AI Advantage

While commercialization is inevitable, open-source platforms will continue to drive innovation and inspire new breakthroughs. After all, human creativity remains the highest barrier for AI to surpass (MIT Technology Review 2024).​ The unique attributes of human intelligence such as intuition, emotional intelligence, creative problem-solving, and nuanced decision-making, become increasingly valuable as AI handles computational tasks.

The successful application of AI should allow for a symbiotic relationship between man and machine, augmenting operations that were contingent upon time and manual effort; its assimilation into organizational design should not diminish the value of human creativity, but rather be a resolute attempt to reallocate human and machine resources, and improve operational efficiency by transforming routine, task-execution into high-performing strategic processes.

Secondly, to achieve maximum operability with AI, enterprises should troubleshoot their current processes to assess where AI can strengthen their productivity the most, and reduce cost and labor-intensive to maximize human capital in more strategic business functions.

Enterprises need to cast a wider net with what they can achieve with AI integration into their business. Most companies focus on short term goals of achieving greater productivity or agility, but might be missing the long-term impact beyond selected use cases. If AI is to shape the future of work, it will do so with human competency navigating it for long-term productivity; which opens an exciting window into the transformation of the workforce through hybrid intelligence, where uniquely human capabilities complement and guide these powerful tools.

Conclusion

There is no denying that AI has leveled the playing field for major industry players to gain ground on their competitors. More importantly, it is allowing a new creed of inventors and researchers to manifest projects that create tangible change in our world. While early adopters celebrate this transformation, a large portion of the global workforce echoes the fear that they will be replaced by autonomous, hyper-intelligent systems capable of taking over their core competencies, rendering them redundant as assets to businesses (McKinsey 2018).

It’s imperative that the potential of AI should be harnessed in a way that creates more opportunities for value creation. With machines taking over the routine tasks that consume the lives of otherwise productive and creative employees, enterprises have a genuine responsibility to shift the paradigm of human competency. The goal is to not create human redundancy but create solutions which can further empower intelligent decision making at the gate of AI output. AI has non-exhaustive computational abilities, that can be coupled with human intuition, and an eye for nuanced rapport-building to provide the best customer experience through your business. The future of work is high-performing operability aided by AI efficiency, and an enhanced organizational infrastructure abetted by human creativity to dominate and proliferate industrial transformations looming at the periphery of the business world.