February 2, 2025
Managing Partner
Generative AI is not just an incremental technology shift but a fundamental transformation in automation, and companies that leverage past automation expertise are pulling ahead in efficiency, cost savings, and competitive advantage.
Nvidia has built a formidable reputation for pioneering artificial intelligence (AI) and automation across its engineering operations. The company has consistently leveraged cutting-edge technology to enhance productivity and innovation in chip design. One of its most significant advancements in this space is ChipNeMo, a generative AI model trained on 30 years of Nvidia's proprietary engineering data. ChipNeMo's design is to streamline workflows by functioning as an AI-powered assistant-retrieving technical information, generating scripts for electronic design automation (EDA) tools, and summarizing complex bug reports to accelerate debugging processes.
At CES 2025, Nvidia expanded its generative AI footprint beyond chip design, unveiling Cosmos, a low-cost computing platform tailored for the development of physical AI. Cosmos provides foundational AI models to support next-generation autonomous systems, including self-driving vehicles and humanoid robots. This platform allows developers to train and refine AI models more efficiently by leveraging physics-based synthetic data. Early adopters like Uber are already integrating Cosmos into their AI-driven initiatives, underscoring the platform's potential to redefine automation in real-world applications.
Meanwhile, Nvidia also invests in AI supercomputing to advance generative AI capabilities. Introducing Project DIGITS, a specialized AI research supercomputer highlights the company's commitment to providing the infrastructure necessary for AI's continued advancement. This initiative strengthens Nvidia's position at the forefront of AI model training and optimization, enabling breakthroughs across industries.
In parallel, the emergence of DeepSeek, a rapidly evolving AI player, is reshaping the generative AI landscape. DeepSeek's R1 model has demonstrated advanced reasoning and self-correction capabilities, rivaling industry leaders such as OpenAI. Unlike many proprietary models, DeepSeek's open-source approach presents a significant shift in the AI ecosystem, offering increased accessibility for developers and researchers.
As generative AI continues to evolve, Nvidia's innovations and the rise of new AI disruptors like DeepSeek underscore a transformative era in engineering and beyond. Integrating AI-driven automation into technical workflows is no longer an experimental endeavor but a foundational shift shaping the future of industries worldwide.
Technology companies with a rich legacy in traditional automation, including robotic process automation (RPA) and analytical AI, are now applying the lessons from those early experiences to gain a competitive edge with generative AI. These companies have long understood that the path to success is not paved by isolated pilot projects but by transforming these pilots into expansive, enterprise-wide programs that deliver significant returns on investment. By building on years of expertise in automating repetitive tasks and streamlining complex processes, these organizations are well-positioned to exploit the potential of generative AI. Furthermore, companies that prioritize this now will be able to make significant headway in capturing market share from competitors who don't. The ability to reach a level of automation is critical in achieving success with AI. Organizations that lack this fundamental step will struggle to derive value from Gen AI Implementations and create more disparate systems that lack the ability to maximize shareholder value.
In practice, these companies are moving beyond the traditional confines of automation by integrating generative AI into their core operations. This evolution is marked by a shift from merely automating simple, repetitive tasks to enhancing more complex functions that require a nuanced understanding of data and decision-making processes. The deployment of generative AI tools, like OpenAI Operators, DeepSeek R1, or Agentic AI demonstrates how a legacy of technological innovation can be harnessed to meet new challenges. By combining historical data with modern machine-learning techniques, companies can now uncover insights and opportunities that were previously inaccessible. This proactive approach allows them to streamline operations, reduce costs, and foster an environment of continuous improvement. In essence, the move to generative AI is a natural progression for organizations that have long championed the benefits of automation, driving them toward a future where technological innovation and operational excellence go hand in hand.
A recent survey by Santiago & Company, which included nearly a thousand executives worldwide (14% of whom represent technology companies), revealed that heavy investment in automation makes a significant difference. Companies defined as leaders—those investing at least 20% of their IT budget in automation over the past two years—achieved an average cost saving of 22%. In contrast, firms classified as laggards investing less than 5% of their IT budget managed to save just under 8%. In 2024, automation leaders at technology firms reduced process costs by 17%, whereas lagging companies only managed an 8% reduction. Respondents also emphasized the importance of trimming low-value tasks, speeding up process completion times, and improving service quality and accuracy. (figure 1)
Microsoft offers an illustrative example of successful automation in finance. Between 2010 and 2020, the company increased its revenue by 145% while growing its finance headcount by 15%. Furthermore, AI innovations have made Microsoft's financial forecasts faster and more accurate. What once took 100 full-time employees an entire month now requires only two employees working for two days.
AT&T, on the other hand, AT&T began using robotic process automation in 2015, becoming one of the earliest adopters of the technology. Over the years, AT&T has expanded its use of AI across its operations. Today, AI helps the company optimize field technician routes, reducing fuel consumption while serving more customers. Additionally, AT&T employs AI to translate and simplify documents and to improve productivity among its coders and developers.
Leaders in the industry are rapidly moving to implement generative AI. On average, these companies plan to invest over three times more of their IT budget in generative AI than their more cautious counterparts. The continued wave of automation is not solely about cost reduction. For instance, companies that have successfully scaled traditional automation methods—such as workflow automation, RPA, scripting, and optical character recognition—have already embedded AI beyond LLMs. They integrate machine learning into document processing and natural language processing into job descriptions, invoice matching, and many other areas of the organization.
The gap between leaders and laggards is widening, and this trend will likely continue. According to the Santiago & Company survey, 33% of leaders plan to significantly increase their investments in 2024, up from 21% from publicly sourced data from 2022. In contrast, only 13% of laggards intend to boost their spending, a decline from 19% in 2022. As companies embrace generative AI, most survey respondents expect to apply it across three use cases. In the first wave, generative AI will enable new capabilities that were previously impossible, such as creating fresh marketing content. The second wave involves replacing existing technologies in current processes like order processing. In the third wave, companies will enhance established processes, such as accounts payable and receivable, rather than starting from scratch in areas where they have already invested resources.
Companies that master key principles will be well-positioned to capitalize on generative AI. First, they elevate automation from narrow pilots to cross-company strategic initiatives. Rather than crowd-sourcing a long list of small projects within individual departments, these organizations set bold, enterprise-wide goals with the potential for multi-million-dollar savings. They secure the sponsorship of senior executives and make automation a central pillar of their strategic agenda.
Second, successful organizations combine various automation technologies. When tasks are automated using different isolated technologies, the overall value is often limited, and process complexity can increase. Instead, leaders start by identifying business needs and determining the best mix of technologies to meet those needs.
Third, companies insist on realizing tangible value from automation. Before committing to software and implementation resources, senior executives require a detailed plan outlining the expected savings and benefits. Once automation is deployed, business processes are redesigned, and teams must provide evidence that the projected value has been achieved.
Finally, reaching full adoption involves effective change management. Companies must document new processes, educate employees on updated methods, invest in training, and track adoption rates. Continuous improvement ensures that teams fully embrace and efficiently use the latest technologies.
The sophistication and maturity of automation vary widely across companies. However, organizations willing to boost their investments and commit to long-term change can catch up with industry leaders. The valuable lessons learned from traditional automation technologies directly apply to generative AI. Techniques, governance issues, and process changes remain similar, making the transition to generative AI a natural progression. This fresh approach helps manage costs and significantly improves the customer experience. By following these proven strategies, companies are well-positioned to drive innovation, enhance productivity, and secure a competitive advantage in the evolving technological landscape.
Marketers are leveraging generative AI to produce personalized campaigns with unprecedented efficiency and measurable impact. Those prioritizing targeted implementations, user-friendly designs, and strategic collaborations will gain a decisive advantage in the rapidly evolving marketing landscape.
Generative AI is transforming the M&A landscape, offering companies that adopt it a significant competitive advantage. Early adopters are already reaping benefits in efficiency and deal-making capabilities, and those who delay risk falling behind.
As market volatility and technological disruption reshape the business landscape in 2025, executives are reimagining cost management as a strategic driver of competitive advantage rather than just an efficiency play. Through digital innovation and cultural transformation, organizations are achieving immediate cost savings and strategically reinvesting these gains to fuel growth and build resilience in an uncertain global economy.
Companies that have mastered automation are now applying those lessons to generative AI, scaling it enterprise-wide to unlock greater efficiencies, cost savings, and competitive advantage. Firms that hesitate risk falling further behind as industry leaders push forward with strategic, multi-layered AI adoption.
