Generative Artificial Intelligence Rising in Corporate Workflows

Since OpenAI’s latest large language model (LLM), ChatGPT debuted, generative artificial intelligence (AI) and LLMs have become ubiquitous in modern business. Compared to previous technological breakthroughs that promised monumental change but struggled to deliver–like the metaverse–generative AI and artificial intelligence, in general, are experiencing far more success in their implementation.  

There have been some stumbling blocks. Early in 2023, Microsoft and Google saw their fair share of missteps when their own AI chatbots produced erroneous information to an audience of millions on social media. Since those challenges, Microsoft and Google have forged ahead in their AI endeavors, partnering with AI companies to improve the accuracy and performance of their LLMs and other AI chatbots. Microsoft is currently working alongside OpenAI to refine and develop new LLMs.

The evolution and training of LLMs has occurred at lightspeed over the last year–and for a good reason. Most LLMs and chatbots like ChatGPT, Google’s Bard, and Microsoft’s Copilot must be trained with specific data that helps the AI accomplish its set task.  

With continued training past initial implementation, AI can grow more accurate and efficient in tasks, lowering the chances of flaws like AI hallucinations. These “hallucinations” are errors caused by insufficient training data, incorrect assumptions made by the model, or biases in the data used to train the model. This problem can be dealt with through additional rules and or giving the AI more specific roles. Human error, on the other hand, is not so easily fixed.

The decreased probability of human error, increased precision, and optimized workflows are just a few reasons companies across all industries are racing to develop generative AI strategies. In the last year, various reports have studied the growing use cases of generative AI within corporate workflows. Last year, McKinsey explored the possibilities of generative AI for business and the global economy.  

In a recent Deloitte report, it was discovered that company leadership believes that generative AI will transform businesses within three years and drive substantial organizational transformation. The results echo what was discovered in McKinsey’s report last year, with companies focusing on improving operational efficiency and reducing time waste.  

Those benefits are just scratching the surface of what generative AI and artificial intelligence applications can offer companies.  

The Transformative Benefits of Generative AI

One of the biggest impacts that generative AI can offer businesses is the ability to drive thousands in revenue due to increased productivity. When implemented strategically, generative AI in corporate workflows can free up resources and streamline tedious and often time-consuming processes. Companies are more open to growth and innovation through these improved workflows and automated processes.

Generative AI, while functional and already seeing incorporation over the past year, is still in its infancy. Its use cases are limited but are expected to become imperative to business success in the coming years. According to the Boston Consulting Group’s report, companies will require a compelling generative AI strategy to be an industry leader, or at least remain highly competitive, in the next five years.  

McKinsey reports that generative AI’s impact on productivity could add trillions of dollars in value to the global economy. This value could reach even higher if the effect of embedding generative AI into software used for other tasks is included in this equation. Generative AI doesn’t discriminate, as it is expected to impact all industry sectors significantly.  

The most common disruption is its ability to drive substantial organizational transformation with the potential to change the anatomy of work itself.  

In its latest study, Deloitte states that 79% of its over 2,800 Director to C-suite level respondents expect generative AI to drive transformation in less than three years. However, most researchers agree that the potential benefits of generative AI will likely take time to fully realize through further adoption and broadened leadership scopes. Most Deloitte study respondents prioritize generative AI’s tactical benefits, including improved work efficiency and cost reduction, over driving innovation.  

Generative AI and other AI applications can potentially automate work activities that rank highly for employee time focus. Work automation can redirect around 60% to 70% of staff time to high-level tasks, far more than the original potential of 50%. This is a direct result of generative AI’s acceleration in technical automation due to its increased ability to understand natural language.  

While click-bait headlines like to prey on fear that AI automation has the potential to steal jobs right out from under employees’ noses, the transformative benefits generative AI will not upend businesses by replacing humans with robots. If anything, workforce transformation will accelerate, allowing companies to slash product development periods as AI automates monotonous tasks while leaving high-level and innovative work to human coworkers.

McKinsey states that generative AI will substantially increase labor productivity across the economy, with annual growth through 2040. The extent of this is dependent on the technology's wide adoption rate.  

Beyond increased productivity, business leaders can expect many benefits from embracing generative AI. Four of the main benefits noted by McKinsey, Deloitte, and the BCG are:

1. Personalized Customer Experience: Generative AI can quickly and effectively transform customer interactions to curtail responses to individuals for an engaging and unique experience. With AI-driven recommendations based on collected data, applications such as chatbots can offer tailored support to elevate the customer journey. Customization has become a noticeable want among target audiences, from the purchased product to how the company treats them.  

2. Accelerated R&D: Automation has transformed manufacturing lines, and generative AI applications can do the same with product design. Predictive maintenance can save businesses manufacturing production downtime, uncover patterns in design to remove errors long before human staff can identify them, and aid engineers in discovering new avenues for future product designs.  

3. New Business Models: Leveraging AI through product development, market and service delivery, and other areas can help set a business apart. Unique business models promote a company as a forward-thinker within the industry and attract customers. With distinctive workflows, staff can uncover new innovative solutions that propel the business forward.  

4. Advanced Data Analytics: AI can help collect invaluable data-driven insights that can become lost in translation in one-third of the time it takes for employees to locate the same. Similarly, because LLMs depend on quality data to operate successfully without frequent hallucinations, it gives the organization a solid foundation to establish and prioritize a focused data strategy. Armed with quality data, businesses can make better decisions in half the time it would typically take.  

In a way, these benefits support the same main desire that most leaders want: increased productivity, which generates more revenue. Generative AI implementation has a ripple effect throughout an organization, optimizing workflows and allowing companies to become more flexible and capable in the face of shifting market demand.  

“We’re in the early days of a major technological transformation with Gen AI beginning to drive a wave of innovation across industries,” said Joe Ucuzoglu, Deloitte Global CEO. “The speed, scale, and use cases of Gen AI are breathtaking. Business leaders are under an immense amount of pressure to act, while ensuring appropriate governance and risk mitigation guardrails are in place.”  

With the pressure to adapt, and for good reason, organizations must form a proactive implementation strategy for generative AI. With a proper plan, companies can avoid challenges arising from improper training and execution.  

How to Prepare for Generative AI

In Deloitte’s “The State of Generative AI in the Enterprise” survey, respondents, while optimistic about AI's benefits and higher levels of trust in the technology, noted their lack of technical talent and skills as a significant barrier. Only 22% of Deloitte’s respondents believe their organization is “highly” or “very highly” prepared to address talent-related issues with generative AI adoption.  

Only 47% of organizations agree that they are sufficiently educating employees on generative AI's capabilities, benefits, and value.  

“Today, Gen AI is at an inflection point where organizations are just beginning to recognize its potential but are yet to see it as a growth catalyst for business. Organizations need to consider Gen AI in conjunction with other AI and technology tools to drive business growth,” says Deborshi Dutt, Artificial Intelligence Strategic Growth Offering Lead and Principal at Deloitte Consulting.  

“To help accelerate their path to Gen AI value, organizations should start to reimagine and reinvent how they conduct business to stay ahead in this transformative landscape, while managing risks appropriately. This will require increased collaboration across organizations to foster trust in the responsible and widespread adoption of Gen AI, and a strong focus on education and reskilling people for how this technology is expected to alter how we each work, learn, and collaborate.”

Strengthening generative AI education is the best way to understand the technologies comprehensively. Becoming more well-versed in this technology can present new opportunities for all organizational levels and how to implement it effectively. Armed with a greater understanding of its use, leadership, and staff can work alongside each other to accomplish business goals by utilizing generative AI where it can make a difference.  

Some of the best practices for instituting generative AI are:

• Use verified, credible, and approved data for training. The data must be semantically rich and deep to provide sufficient learning and nuances for precise extraction by AI models.
• Develop a focused AI use policy and training plan that outlines how employees can apply generative AI tools.
• Research and review the generative AI models available, their cost, cybersecurity policy, and vendor-building models to identify which tools best fit within your business practice.

Generative AI is still in its early stages, and it will take time to fully understand its benefits. Because it is still new, determining what skills staff require to utilize generative AI to its maximum potential is still unknown. No syllabus details precisely what boxes to check, but that doesn’t mean organizations should wait for one to be created.

Companies that invest in education early, pioneer its use, may end up lightyears ahead of the competition when wider adoption begins.  

Digital Tools that Utilize Verified Data

One of the best ways to begin preparing for generative AI in your organization is through digitalization. The electronic components industry is notable for relying on manual tracking of market trends, industry shifts, and other technical data. Unfortunately, these manual processes require time and are prone to human error.  

Manually gathered data may not be semantically rich or deep enough to provide valuable analytics for future generative artificial intelligence model training. Worse, it can lack verification and credibility, requiring approval and investigation before it can be used for AI.

Digitalization is vital in preparing your business for a strong AI strategy. With the excitement around generative AI, the number of use cases by companies is rising, infiltrating every layer of business for a more productive workforce.  

For companies that don’t know where to begin their digital journey, Sourceability’s digital tools offer an excellent foundation for digitalizing their supply chains. Likewise, for organizations well into the digital age, Soureability’s data goes back nearly a decade and contains historical and technical information on millions of components, verified and curated by our data team. Since this data is used to support our numerous tools, Sourcengine, Quotengine, Datalynq, and our partnerships, Sourceability strives to improve its quality continually.  

Artificial intelligence is expected to reign supreme as a breakthrough technology for the second year in a row. With continued advancements arriving in the coming year and new components to power them, industries are on the precipice of a new dawn in the digital age.  

It’s time to start improving productivity and workplace efficiency with Sourceability.