In the past year, generative artificial intelligence (AI) has quickly become a key focus in business and technology. In fact, a McKinsey Global Survey revealed last year that one third of respondents organizations are already using generative AI regularly in at least one business function.
This surge in interest has raised many questions for businesses. How can we use this technology to gain a competitive edge? What are the potential risks and rewards? How will it reshape our workforce and operations?
Understanding the Generative AI
ChatGPT’s meteoric rise to a million users in just five days is a testament to the growing power of AI. But what exactly is generative AI, and how does it differ from large language models (LLMs) like ChatGPT?
To understand the full scope of generative AI’s impact, it’s important to understand the terminology. While large language models represent just one category of generative AI, focusing specifically on text generation, this technology is named for its ability to generate a much wider range of outputs. These include not only text, but also images, audio, computer code, and more.
In recent months, we’ve witnessed impressive examples of generative AI’s versatility in producing diverse content and informing experts in different fields. From composing distinctive music pieces and designing graphics to detecting diseases through medical images and generating code in multiple programming languages, this technology is opening up new possibilities across industries.
This versatility is a key advantage, allowing businesses to use a single model for a multitude of applications. Moreover, the accessibility of platforms like ChatGPT has democratized access to this technology, making it more readily available to businesses of all sizes.
On the other hand, LLMs, as a subset of generative AI, are specifically applied to language related tasks. They power software that aids in different functions, such as drafting business emails, helping students enhance essays, or summarizing long documents. When you interact with an AI system and receive a language based response that seems human-like, there’s a good chance an LLM is behind it.
Empowering knowledge workers
The use of generative AI in businesses is transforming the way knowledge workers operate, streamlining workflows, allowing access to information, and fostering creativity. A recent study by Siili Solutions indicated a potential productivity improvement of 30-50% currently achievable with generative AI among software developers.
At the forefront of this transformation is the automation of routine tasks. Generative AI models are adept at handling repetitive processes such as report generation, data analysis, and content summarization, allowing employees to focus on more complex tasks.
Furthermore, generative AI tools simplify the information exchange across departments and organizational levels, ensuring that workers can learn from each other, no matter where they are or what they do.
Besides making tasks easier and sharing knowledge, generative AI is also helping people be more creative and come up with new ideas. AI models can generate ideas, suggest alternative approaches, and help people see connections they might have missed. This means workers can try out new approaches and find new paths for a business to grow and change.
Learning new skills
While generative AI offers great potential, it also means workers need to learn new things to succeed in today’s workplace. As AI takes over routine tasks, workers need to develop uniquely human skills, such as:
- Critical thinking
- Creativity
- Problem-solving
- Emotional intelligence
Comprehensive training programs are essential to equip employees with both the ability to utilize AI tools, and to develop skills that will be useful as technology continues to advance.
Furthermore, switching to a workplace where AI is common isn’t easy. Workers might worry about losing their jobs or feel stressed about all the changes. To ensure a smooth transition, companies must proactively address these concerns through transparent communication, support systems, and plenty of opportunities for career growth and development.
Large enterprises and generative AI
Across numerous sectors, large companies are integrating generative AI into their core operations, demonstrating the technology’s potential impact. According to a Gartner report, large enterprises that have adopted generative AI have reported a 15% reduction in customer service costs and a 10% increase in sales productivity.
Coca-Cola, for example, is using AI for its marketing efforts. By analyzing consumer data, the company is crafting hyper-personalized campaigns that resonate with individual preferences, driving engagement and brand loyalty. Furthermore, leveraging AI driven insights, the company has optimized its supply chain, predicting demand fluctuations and adjusting production accordingly. This has led to a reported 10% reduction in inventory costs and a 5% increase in on-time deliveries. Additionally, AI powered chatbots have enhanced customer service interactions, resolving 80% of inquiries without human intervention, resulting in significant cost savings and improved customer satisfaction.
Besides consumer goods, other industries are also embracing generative AI. Microsoft’s integration of ChatGPT into Bing has not only transformed the search experience but also yielded tangible business results. The conversational search feature has led to a 15% increase in user engagement and a 10% increase in ad revenue. Furthermore, Microsoft’s use of generative AI to automate code generation in its development tools has resulted in a 20% reduction in development time for certain projects, accelerating product releases and improving developer productivity.
The retail sector has also been quick to adopt generative AI to enhance the shopping experience and drive sales. E-commerce giants like Amazon and Alibaba have deployed generative AI algorithms to personalize product recommendations, optimize pricing strategies, and create targeted marketing campaigns. These efforts have produced clear outcomes, such as more customer interactions, better sales rates, and improved stock control.
Scaling AI for maximum impact
Regardless of the various benefits, one of the primary challenges in large enterprises is the need for robust infrastructure. AI models require significant computing power and storage capacity to operate effectively, and large enterprises need to ensure they have the infrastructure in place to support these demands. This often involves investing in high-performance computing clusters, cloud-based infrastructure, or a combination of both.
Another challenge is data management. AI models thrive on data, and the more data they have to learn from, the better they perform. However, managing large datasets can be complex and time-consuming. Enterprises need to develop effective strategies for collecting, cleaning, and labeling data to ensure that AI models have access to high-quality training data.
Measuring and demonstrating ROI from AI investments
To justify the investment in generative AI, large enterprises need to be able to measure and demonstrate a return on investment (ROI). This can be challenging, as the impact of AI is often hard to measure. However, there are several approaches that enterprises can take to track the impact of AI on their business.
One approach is to focus on key performance indicators (KPIs) that are directly impacted by AI. For example, a customer service team that implements an AI powered chatbot might track metrics like customer satisfaction ratings, resolution times, and call volumes. By comparing these metrics before and after the implementation of the chatbot, the company can get a sense of the impact that AI is having on its customer service operations.
Another approach is to conduct controlled experiments to measure the impact of AI on specific business outcomes. For example, a marketing team might run a test where one group of customers receives personalized product recommendations generated by AI, while another group receives generic recommendations. By comparing the conversion rates of the two groups, the company can determine whether the AI powered recommendations are having a positive impact on sales.
Midsize enterprises and generative AI
In business, being small can mean being flexible, which is a big advantage when using generative AI. Although they are often overshadowed by larger companies, midsize enterprises (MSEs) are showing that they can use AI technology to improve their operations and gain an edge over competitors.
Some of the biggest advantages of mid-sized companies when integrating generative AI into their daily operations are:
- Agility and adaptability – MSEs often have an advantage because they are agile and adaptable. They can quickly change direction, try new technologies, and fit them into their workflows with less hassle than larger companies. This flexibility allows MSEs to adopt generative AI solutions quickly and customize them to their specific needs.
- Cost-effective solutions for targeted impact – Unlike large companies that might choose custom AI solutions, mid-sized companies can use a growing number of affordable, easy to use AI tools and platforms made for their size. This means they don’t need to spend a lot of money upfront and can slowly and strategically add AI to their operations.
- Data-driven insights – A Forrester study found that generative AI is likely to influence a grand total of 11 million jobs by 2023, making the tech 4.5 times more likely to reshape a role than eliminate it altogether. While larger enterprises might have more extensive datasets, MSEs can still benefit significantly from the data-driven insights that generative AI can provide. By analyzing customer data, market trends, and operational metrics, AI algorithms can uncover patterns, predict outcomes, and guide strategic decisions.
Duolingo, a language-learning platform, is a great example of how midsize companies can use generative AI for growth and innovation. Although Duolingo is now a larger company, it was midsize when it first started using AI. The company’s smart use of GPT-4 powered chatbots to provide personalized language lessons and conversations has been key to its success.
The personalized language lessons and conversations have led to a 25% increase in daily active users and a 15% improvement in user retention rates. This increased engagement has translated into a 30% increase in subscription revenue for the company.
Additionally, the use of AI to generate personalized feedback on language exercises has improved learning outcomes, with users reporting a 20% increase in their language proficiency scores after using the AI powered features. While many things contribute to Duolingo’s success, their early and effective use of generative AI has been a major factor in helping them stand out in a competitive market.
Resource and expertise constraints
While the potential benefits of generative AI are clear, midsize enterprises must be mindful of the challenges associated with its implementation.
Firstly, MSEs typically have smaller budgets than large enterprises, limiting their ability to invest in expensive AI infrastructure, talent acquisition, and training programs. As a result, they may need to prioritize their AI investments and focus on areas with the highest potential for ROI.
Secondly, while MSEs may have a more agile workforce, they might lack the specialized AI expertise found in larger organizations. Consequently, this can make it difficult to develop and implement AI solutions in-house, requiring them to rely on external vendors or consultants.
Generative AI and intellectual property
The rise of generative AI has thrown the concept of intellectual property (IP) into uncharted territory, raising questions about ownership, originality, and the very definition of authorship. When AI models generate text, images, or music that rival human creations, who holds the rights?
Traditionally, IP rights are granted to human creators. However, the advent of generative AI has blurred the lines of authorship. The legal framework is grappling to catch up with this new reality, creating uncertainty and potential disputes. Is the IP owned by the AI model itself, the developer who created the model, or the user who provided the prompts? These are open questions that courts and legislators are beginning to address.
While the U.S. Copyright Office initially maintained that AI-generated works, without significant human involvement, were not eligible for copyright protection, its stance has evolved. The office now recognizes that works created with AI assistance may be copyrightable, but only if they involve sufficient human creative input and control over the final product.
Furthermore, the issue of derivative works makes things more complicated. If an AI model’s training data includes copyrighted material, does its output violate those copyrights?
Lawsuits against companies like Stability AI and Midjourney, claiming they breached copyrights by using such data, highlight the legal challenges and uncertainties. The results of these cases will have notable effects on the future of generative AI and its connection to intellectual property law.
Safeguarding intellectual property in the age of generative AI
As generative AI models learn from large amounts of data, including copyrighted material, protecting intellectual property (IP) is very important. Using this data raises worries about possibly copying existing works too closely and breaking copyright laws.
A big problem for the legal system now is figuring out how to decide what counts as “original” work when AI is involved, as old ideas about who created something are changing.
Identifying and classifying IP
The first step in protecting IP in the age of generative AI is to clearly identify and classify all intellectual assets. This includes not only traditional forms of IP like patents, trademarks, and copyrights but also new forms of IP that may arise from AI generated content, such as algorithms, models, and datasets.
A comprehensive IP audit can help enterprises catalog their intellectual assets, assess their value, and identify potential vulnerabilities. This process should involve collaboration between legal, technical, and business teams to ensure a holistic understanding of the company’s IP landscape.
Implementing robust data security measures
Data is the foundation of generative AI, and protecting it is crucial to safeguarding IP. Enterprises should implement robust data security measures, including encryption, access controls, and regular backups, to prevent unauthorized access, theft, or misuse of sensitive data.
Additionally, companies should consider using watermarking or other techniques to track and identify AI generated content. This can help establish ownership and prevent unauthorized use of the content by third parties.
Clear ownership and usage policies
Establishing clear ownership and usage policies for AI generated content should include guidelines on who owns the rights to the content, how it can be used, and what happens if it’s shared or modified. These policies should be communicated clearly to all employees who interact with generative AI tools to avoid misunderstandings and potential disputes.
It’s also important to address the issue of third-party data used to train AI models. Enterprises should ensure that they have the necessary licenses and permissions to use this data and that they are not infringing on any intellectual property rights.
Staying ahead of legal and regulatory developments
As the legal and regulatory landscape surrounding AI and IP is constantly changing, enterprises need to stay informed about the latest developments to ensure that their practices comply with relevant laws and regulations.
This may involve seeking legal counsel to navigate complex IP issues and ensure that the company’s AI initiatives are conducted in a legally sound manner.
Investing in employee education and awareness
By educating employees about IP rights, the risks of data breaches, and the importance of adhering to company policies, enterprises can foster a culture of IP awareness and responsibility.
Partnering with reputable AI vendors
When choosing AI vendors and platforms, enterprises should prioritize those that have strong IP protection policies in place. This includes clear ownership and usage agreements, robust data security measures, and a commitment to ethical AI practices.
Generative AI vendor landscape
The generative AI vendor landscape offers many choices, each suited to different needs, budgets, and technical abilities. This diversity allows businesses to choose the most suitable approach for using generative AI.
But before diving into the vendor landscape, it’s helpful to understand the underlying technology that powers generative AI. Various models and algorithms contribute to its capabilities:
- Generative Adversarial Networks (GANs) – GANs employ a unique architecture where two neural networks, a generator and a discriminator, compete against each other. The generator’s role is to produce synthetic data (e.g., images, text), while the discriminator’s task is to distinguish between real and generated data. This adversarial training process pushes both networks to improve continuously. The generator learns to produce increasingly realistic outputs to fool the discriminator, while the discriminator becomes more adept at identifying fakes.
- Transformer Models – Transformer models, like the GPT series powering ChatGPT, are excellent at understanding and generating text. They use self-attention mechanisms to weigh the importance of different words in a sentence, allowing for nuanced language understanding and generation.
- Variational Autoencoders (VAEs) – VAEs, on the other hand, operate on a probabilistic framework. They consist of an encoder network that compresses input data into a lower-dimensional latent space and a decoder network that reconstructs the original data from the latent representation. Unlike traditional autoencoders that simply learn to encode and decode data, VAEs introduce a constraint on the latent space distribution. This constraint forces the latent space to be organized in a way that allows for smooth interpolation and meaningful sampling, enabling the generation of new data points similar to the training data.
Now that we’ve established that, let’s explore the different types of vendors and the key considerations for selecting the right partner.
Major cloud providers
Leading cloud providers like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) offer comprehensive AI services, including pre-trained models, adaptable infrastructure, and user-friendly development tools.
Their platforms provide a convenient entry point for enterprises of all sizes, enabling them to leverage generative AI without extensive in-house expertise. AWS’s Amazon SageMaker, for instance, streamlines the building, training, and deployment of machine learning models, including those for generative AI.
AI startups
These companies often bring innovative approaches, offering unique solutions that may not be available from larger vendors. For instance, Jasper and Copy.ai cater to marketing and sales teams with AI powered content generation, while Synthesia focuses on creating realistic AI generated videos.
Open source frameworks and models
For organizations with the technical expertise, open-source frameworks like TensorFlow and PyTorch, coupled with pre-trained models like GPT-3, provide a foundation for building custom generative AI solutions. This approach not only makes generative AI accessible to more people but also allows for great flexibility and customization. By using these resources, companies can skip the process of creating their own models from scratch. However, it’s essential to have a skilled team of data scientists and engineers.
Consulting firms and service providers
Many consulting firms and service providers specialize in helping enterprises navigate the complexities of implementing and integrating generative AI. They offer expertise in data preparation, model training, deployment, and ethical considerations. Companies like Accenture and Deloitte have dedicated AI practices to guide enterprises through their AI implementation.
Choosing the right vendor – a practical guide
Selecting a generative AI vendor is a strategic decision, and it’s crucial to ask the right questions:
- Specific use cases – Does the vendor’s solution align with your specific use cases? For example, if you need AI for content creation, does the vendor excel in natural language generation?
- Model performance – How does the vendor evaluate their models? What metrics do they use for accuracy, bias, and explainability? Can they provide independent validation of their performance claims?
- Customization – To what extent can you customize the models to your specific data and requirements? Is the vendor open to co-development or fine-tuning of their models?
- Data security and privacy – What measures does the vendor have in place to protect your data? Do they comply with relevant regulations? Where is your data stored and processed?
- Cost and scalability – What is the pricing model? Does it align with your budget and projected usage? Can the solution scale as your needs grow?
- Ethical considerations – Does the vendor have a clear policy on ethical AI use? How do they address issues like bias in their models?
Future enterprise trajectories with generative AI technology
As AI technology evolves, its integration into everyday workflows is set to enhance human capabilities, enabling us to work faster and smarter. The future of enterprise lies not in replacing humans with machines, but in fostering a collaborative environment where AI’s strengths in data processing, pattern recognition, and content generation complement human creativity, critical thinking, and empathy.
This AI enhanced workforce promises increased productivity, innovation, and adaptability, equipping businesses to navigate the challenges and opportunities of the digital age. As generative AI continues to evolve, we can expect the emergence of entirely new roles and jobs, such as AI prompt engineers, AI trainers, AI ethicists, and AI auditors. These positions will be crucial for organizations aiming to utilize the full potential of this technology while ensuring ethical and responsible AI deployment.
However, to realize this potential, we must address critical challenges such as bias in AI algorithms, data privacy and security, and the ethical implications of AI generated content.
