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Bringing GenAI Into Your Organization: The Time Is Now, But Caution Prevails
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Just over a year ago, ChatGPT ushered in a new era in technology with its generative AI large language model, turning decision-making for business processes, workflows and employment on its head.
The media and the public responded with concerns that generative AI, or GenAI, would put people out of work like many technological innovations in the past. However, as people took a deep dive into what the technology can do, the discussion changed. GenAI could potentially replace jobs but, more importantly, GenAI could enable people to be more productive, not replace them.
People began learning how to use GenAI, creating a new job skill – prompt engineering. Businesses began to talk about and investigate what GenAI could do and where it could fit into their business models and products and services. And the public conversation around GenAI died down.
GenAI provides a lot of opportunity for automation. It can do jobs comprised of routine or repetitive work or simplify processes that contain routine or repetitive tasks to free up people to focus on core business work. GenAI can give us smart, conversational chatbots, document summaries, and data organization and even some data analysis. But the big question is, with so many options, where and how do businesses put these processes to best use in their organizations and products?
GenAI Optimism vs. GenAI Challenges
Business leaders are eager to adopt GenAI, but, as with any groundbreaking technology, they face challenges. “An overwhelming majority (97%) of executives believe generative AI will transform their enterprises and industries, and will play a major role in their strategies over the next three to five years. Of those, only 31% have already made “significant” investments in their AI initiatives, but 99% plan to amplify their investment in this technology,” according to Accenture in a January 2024 report.
Major challenges include the risks of AI that can generate new, unvalidated content from datasets outside of the company’s control, the enterprise cost of adopting replacement technology, and developing a comprehensive, people-first strategy that makes best use of the technology across the organization.
Responsible GenAI
Responsible AI is following AI best practices when developing and using AI and includes ethical AI which encompasses three main areas of ethical concern: bias data privacy, and lack of explainability.
[SIDEBAR} Accenture reports in its Pulse of change 2024 Index, that “72% of C-suite leaders say they are approaching investments with caution because of societal concerns about the responsible use of AI. Respondents in North America and Asia Pacific were least deterred by backlash against generative AI, while more than three quarters of C-suite leaders in Europe are approaching it with caution.”
Several countries, including Italy, China and Belarus have banned the use of ChatGPT. The European Union passed the AI Act on March 13, 2024, which regulates AI, categorizes AI into levels of risk, and bans AI systems that present unacceptable risk. Examples of unacceptable risk include social scoring systems and manipulative AI.
Bias. GenAI may be trained on the internet, a closed universe of data by a vendor, or an organizations own datasets. Bias can arise from the dataset or the way the GenAI is prompted and trained. Responsible AI means oversight in the processes of designing, developing and training AI to root out or mitigate against biases and ensure inclusion.
Data privacy. Datasets used to for GenAI may contain data that is subject to data privacy and security laws. Responsible AI means oversight in gathering, storing and use legally sensitive data to ensure compliance and cyber-safety and training AI on compliance.
Explainability. Responsible AI can explain its results. Before we trust what GenAI delivers, we must be able to trust that it’s interpreting the data accurately and that means it must be able to explain how it arrived at its conclusion.
A blog article on Built In clearly explains why focusing on responsible AI is crucially important: “And there may not even be a clear explanation how or why an AI model is working in a particular way. These algorithms operate on immensely complex mathematical patterns — too complex for even experts to understand — which can make it difficult to understand why a model generated a particular output.”
Businesses, especially those outside of the tech industry, are understandably hesitant to quickly fold GenAI into their businesses. The stakes are high if things go awry.
The Financial Cost of Adopting GenAI
Concerns about the social response to using GenAI in business are not the only factor generating hesitancy. The actual integration of GenAI or any AI into products, services and business workflows comes with usually significant financial costs.
A Comp TIA report states that two of the major costs hindering early AI adoption are upgrading applications and building out infrastructure. The report also points out that, for all the enthusiasm around adopting AI, “just over 20% of firms surveyed are aggressively pursuing integration of AI across a wide variety of technology products and business workflows.”
Some of the costs involved in adopting genAI are time and effort costs of moving from old applications to new, cloud vs. hardware costs, talent costs for skills, costs of data collection and training, and maintenance costs.
Accenture talks about enterprise reinvention around GenAI but not all companies are ready to transform their entire business model. Even when a company wants to transform, developing the right strategy takes time. A company’s sustained financial health requires holding excitement at bay in favor of prudent decision-making.
People and Talent
A third important consideration when implementing GenAI, is managing disruption to an organization’s workforce. In other words, your company’s people.
GenAI replaces tasks. A role in an organization may be comprised of routine or repetitive tasks that can be replaced by properly trained GenAI. We’ve already seen this in customer service. The challenge is how to replace those tasks while still leveraging the employee’s knowledge into functions that continue to add value.
Human resource and people teams will have to prepare a company’s workforce for AI adoption, as well as its impact. They will need to participate in job redesign and focus on upskilling and finding new talent. Effective change management from HR will be crucial for a smooth AI business transformation no matter how small or large. The cost of time will be a big factor in a successful move to GenAI tech, and HR can assist to properly manage and reduce that cost.
The organizational costs of adopting GenAI can be high, and companies must be confident the returns will be there to justify a decision to invest in and implement it.
Where to Start with GenAI?
Many companies see places in their organizations where they can adopt GenAI but making a decision on where to start isn’t so easy.
Some large companies were early adopters. In the summer of 2023, Forbes reported on big companies that immediately took up GenAI to enhance their product stacks and create new products to leverage GenAI, including companies like Cisco and Dell. In December, the Wall Street Journal covered how Wayfair uses GenAI in its design tool to help customers with interior design when planning purchases and how Schneider-Electric helps customers understand their carbon emissions with a GenAI chatbot.
When evaluating how to adopt GenAI in your organization, a top, if not the first, question to ask is: where can GenAI immediately bring value? But then consider the difficulty of bringing gen AI into that product or workflow and the ability to measure the impact of adoption.
In response to McKinsey’s last annual global survey, 10% of respondents reported using GenAI in customer service operations. GenAI has been put to use in chatbots and forecasting service trends and anomalies. According to the IBM Institute for Business Value, “63% of CEOs said that, by the end of 2023, they will have already invested in generative AI use cases to serve agents directly, including deploying generative AI for agent training, and enabling agents to interact directly with tech applications to deliver improved instant assistance.” If we go by the IBM Institute’s declaration that “[n]o single area of an organization provides a better foundation to demonstrate generative AI success than customer service,” businesses everywhere should be enhancing their customer service with GenAI or be left behind.
You may have noticed that Amazon already has used genAi for its customer reviews. Each reviewed product now has a summary of those reviews and indicates what customers like best and least about the products. How does helping customers sift through reviews better help Amazon? It can reduce the cost of product returns by clearly telling customers what people like and don’t like about a product. It can help weed out sellers with low-quality products or difficult return policies, improving the platform as a whole. Also, I’m guessing here, but I’m sure it helps Amazon weed out fake reviews as well.
Competition Drives GenAI Adoption
While adoption of GenAI is touted as urgent, costs can remain a barrier, and risk management is essential. Taking the time to understand your core business and its goals and where GenAI can have a strong impact on value for the future will help justify costs and get a business in the game. Further, companies can educate themselves on the risks of GenAI tech in their businesses and develop smart, compliant policies for its use.
An equally important consideration for a company, wherever it starts its GenAI journey, is operating from a people/AI collaboration mindset. Your people will tell you where GenAI will best serve your organization. Your people will implement GenAI, both integrate and train it. They’ll also ensure that GenAI is doing its job right and that risk management policies aren’t violated. To quote Mercer: “If you want to get the most out of generative AI, know that humans plus AI deliver the real advantage. Generative AI’s true strength lies in augmenting (not replacing) employees’ work.”
The introduction of GenAI kicked off a true technology revolution that can and will transform our world in ways we can’t yet predict. There’s no avoiding the competitive need to integrate GenAI into your organization. Understanding what GenAI can do and relating that to your workflows, processes and products is crucial to both integration and being part of the revolution.