Adopting Generative AI
The power of Artificial Intelligence experienced a significant advancement through Generative AI, a subset of AI technology that focuses on generating new content, data, or media that is similar to what humans might create. This has revolutionized our work processes because rather than relying on predefined rules or patterns, Generative AI uses deep learning models to create new and original content.
Tasks like drafting memos and reports, crafting website graphics, devising personalized marketing strategies, and curating employee training programs can now be accomplished using Generative AI. The potential applications for simplifying tasks are limitless, with the capacity to drive innovation, boost productivity, and improve outcomes. However, the emergence of Generative AI can pose potential challenges for organizations and the workforce, eliminating jobs, redefining roles, and serious ethical considerations.
So, how has the emergence of GenAI impacted the industry so far, and what are the future opportunities and risks to consider?
Feedback From Execs
Deloitte conducted a survey interviewing leaders of large and midsize tech companies exploring how they are phasing generative AI into their work streams and what benefits or issues they see thus far. One executive said that “Generative AI really changed the game and it was, hands-down, an edict by our CEO.” Another executive discussed how it is driving a new wave of experimentation and helping the company automate operations, accelerate development cycles, and improve customer service.
Although many executives shared primarily positive feedback, there were concerns raised around the ethicality of the new technology. Additionally, with new data management systems that Generative AI provides, many roles can become obsolete. According to a study from Gartner, by 2026, over 100 million people will engage robo-colleagues (synthetic virtual colleagues) to contribute to enterprise work. While the elimination of jobs can increase a company’s bottom line, it can also remove the human-ness from organizations.
Benefits
According to a study by Deloitte, 94% of leaders recognize the critical role of AI in the next five years. Although the long term effects of the adoption of Generative AI are still uncertain, there are many benefits we are already seeing. This tool can reshape an organization and the productivity throughout the customer journey. Below are the main benefits we are already seeing from early adopters:
1. Customer Engagement and Customer Service
Generative AI enhances customer engagement by creating interactive and immersive experiences, such as chatbots, virtual assistants, and personalized avatars. According to this survey, in a company with 5,000 customer service agents, employing Generative AI-powered solutions raised issue resolution by 14% per hour. It also reduced issue-handling time by 9% and cut manager requests by 25%. These experiences drive customer interaction and retention, leading to increased revenue and brand loyalty.
2. Cost Reduction and Time Efficiency
Generative AI offers substantial benefits for cost reduction and time efficiency. By automating repetitive tasks, businesses can redistribute resources to more critical areas, enhancing overall efficiency and competitiveness. According to a different Deloitte survey, 82% of leaders anticipate that AI will enhance their employees' performance. The same survey found that on average, employees leveraging Generative AI save 1.75 hours daily, which equates to a full workday each week.
3. Content Generation
Generative AI can create vast amounts of content, including text, images, videos, and music, quickly and efficiently. This capability can be leveraged for marketing campaigns, content creation, and creative projects. By automating information generation, companies can maintain a consistent brand voice and style. An article by Salesforce, determined the most common use case for marketers (76%) and sales specialists (82%) is basic text piece creation and copywriting. By replacing these tasks with Generative AI, companies can save valuable time and resources.
4. Data Analysis and Insights
Generative AI can create synthetic data that closely resembles real-world data, addressing privacy concerns and data scarcity issues. This synthetic data can be used to train machine learning models, improve model performance, and develop robust algorithms (Harvard Business School). Generative AI demonstrates exceptional proficiency in data analysis, making it valuable for organizations with extensive datasets. It possesses the capability to discern trends, patterns, and irregularities within the data. This empowers organizations to make data-driven decisions and gain profound insights into their operations, customer interactions, and market trends.
Generative AI holds immense potential to transform industries, drive innovation, and create new opportunities for businesses and individuals alike. However, it also raises ethical, legal, societal implications, and risks that must be carefully considered and addressed.
Potential Risks of Generative AI
Generative AI presents several potential risks for organizations. Even Sam Altman, the CEO of OpenAI, made a recent testimony in Congress when he called on the government to regulate artificial intelligence. He stated, “we think that regulatory intervention by governments will be critical to mitigating the risks of increasingly powerful models.” Highlighted below are the key concerns of adopting Generative AI:
1. Data Privacy
Generative AI relies on large datasets, raising concerns about data privacy and security breaches. This is evidenced by cases where Generative AI tools have incorporated copyrighted material without the creators’ permission. The terms of use for Generative AI applications often lack clarity on the usage of user interaction data for model improvement. This raises privacy and security concerns, proven by an incident where Italy temporarily banned ChatGPT over concerns about consent, privacy, output accuracy, and age verification.
2. Bias and Fairness Issues
Generative AI models may inadvertently perpetuate biases present in the training data, resulting in unfair outcomes or discriminatory practices. This can lead to ethical dilemmas and damage to the organization's reputation. According to a recent study by MIT, researchers found that popular AI language models, including GPT-3, tend to generate toxic or biased text when prompted with certain questions related to gender, race, or other sensitive topics. These biases can be damaging to organizational culture, customer experience, and brand identity.
3. Misinformation and Fake Content
The potential to produce realistic but false content leveraging Generative AI is huge. Content like fake news articles, reviews, or social media posts are just a few examples that can undermine trust in information sources and lead to misinformation spreading rapidly. A recent report by Sensity, a deepfake detection company, stated there was a 330% increase in deepfake videos online between 2022 and 2023. These deepfakes can be used to create misleading or false information, posing significant risks in various domains, including cybersecurity, politics, and journalism.
Early adoption of Generative AI has proven to be extremely beneficial to organizations, but it is still critical to be aware of the potential risks and impacts. Leaders must implement rules and regulations that ensure ethical usage and accurately identify when the technology is being misused. The future of Generative AI will heavily depend on human involvement. While this technology has the power to make positions redundant, the human touch will always be important.