The Complete Guide to Generative AI in Business

The business landscape is ruthless. Established organizations leverage advanced technology solutions at a rapid rate, while smaller firms with more limited resources struggle to keep pace. 

Generative artificial intelligence (AI), a powerful branch of AI solutions, offers a potential game-changer for small and medium-sized businesses (SMBs) to keep up and stay competitive.

Traditionally, AI has focused on analyzing data. Generative AI flips the script, creating new content – from product design strategies to marketing copy. This translates to increased efficiency, reduced costs, and a boost in innovation – benefits you can’t afford to miss out on.

This blog is your roadmap to understanding generative AI, its top benefits and current use cases, and how managed IT services, also called managed services (MSPs) can help you integrate it seamlessly into your operations and get value out of this transformative technology.

What is generative AI?

Generative artificial intelligence is a subfield of data & AI technologies with the unique ability to create original content, data or solutions based on user input. Think of it as a powerful tool that can supercharge your SMB’s creativity and efficiency in your processes.

Unlike traditional AI, which primarily analyzes and interprets existing data to make decisions or predictions, generative AI uses advanced algorithms to generate novel outputs. These outputs can range from text, images, and music to complex data models and simulations.

By leveraging vast amounts of data and learning from patterns, generative AI can produce content for its users that is not only unique, but also highly relevant and sophisticated, making it a powerful tool for innovation across various domains – or even just a useful starting point.

Generative AI is primed to make an increasingly strong impact on enterprises (and SMBs) over the next five years, with Gartner Research predicting that by 2025, 30% of enterprises will have implemented an AI-augmented development and testing strategy, up from 5% in 2021. In addition, by 2025, Gartner expects that generative design AI will automate 60% of the design effort for new websites and mobile apps.

How does generative AI work?

Generative AI models are trained on massive datasets of existing content, like text, images, or code. It scans underlying patterns and structures in data to then generate entirely original content that adheres to those patterns while mimicking human-like creativity and intelligence.

The core of generative AI lies in its ability to understand and replicate the underlying structures and nuances of the data it has been trained on. This is achieved through techniques such as deep learning (DL) and neural networks, particularly generative adversarial networks (GANs) and transformer models. These technologies enable generative AI to process and produce information in ways that are increasingly indistinguishable from human-generated content.

What are the origins of generative AI?

Generative AI is an exciting new modern technology, but its beginnings can be traced back further than you may think.

In 1906, Andrey Markov, a Russian mathematician, developed the concept of Markov chains, a statistical method for generating sequences based on probabilities. This early model laid the foundation for analyzing patterns in data, a core principle of generative AI. 

When the broader field of artificial intelligence began to take shape in the 1940s and 1950s, at that time called cybernetics, it took another decade for the term to emerge alongside the rise of text-based chatbots in the 1960s, which were early programs that attempted to hold conversations using basic language generation techniques. 

Generative AI began to emerge more distinctly with the development of neural networks inspired by the brain and deep learning techniques in the 1990s and 2000s, and hardware advancements that could provide the ability to compute at much higher rates than ever before. These factors provided the sophisticated algorithms and computational power necessary to create and train AI systems to be capable of generating entirely new content for end-users.

A pivotal moment in the development of generative AI was the introduction of GANs by Ian Goodfellow and his colleagues in 2014. GANs comprise two neural networks, the generator and the discriminator, working against each other in a sort of game. The generator produces new data instances, while the discriminator evaluates them against real data, driving the generator to produce increasingly realistic outputs. This relatively new approach allowed for significant improvements in the quality and realism of generated content, such as images, videos, and text.

Other architectures like variational autoencoders (VAEs) and transformers have also played crucial roles in advancing generative AI. Transformers, introduced in a paper titled “Attention is All You Need” by Vaswani et al. in 2017, have been particularly influential in the development of language models like OpenAI’s generative pre-trained transformer (GPT) series. These models have demonstrated an unprecedented ability to generate coherent and contextually relevant text, opening up new possibilities for AI-generated content.

There are many benefits to using and use cases for Generative AI.

What are the top benefits of generative AI solutions for SMB?

The implications of generative AI are vast and varied, offering potential benefits for SMBs (and larger organizations) across multiple industries, including automotive, finance, healthcare, marketing and more. 

For instance, in creative fields, gen AI can assist in generating art, music, and literature, while in business and science, it can help in generating models, predictions, and solving complex problems. This translates to several real-world benefits for your business, which we cover next:

Automation and efficiency:

Generative AI significantly reduces manual workload by automating operational tasks like content creation, data entry, and analysis. When used correctly, this can lead to faster completion of projects and allows your staff to focus on more complex, strategic initiatives. This efficiency is especially beneficial for small-to-medium businesses without the same level of personnel or resources as a large-scale enterprise.

Cost reduction:

Implementing generative AI can lead to certain cost savings for your SMB by reducing the need for large teams to manage and create content, perform data analysis, and conduct research. These savings can then be redirected to other critical areas of the business. Combined with the help of a MSP, you can significantly optimize IT spend across-the-board.

Creativity and innovation:

By generating novel ideas, designs, and solutions, generative AI can help foster innovation within your business. It can propose unique approaches and creative solutions that your workforce might not consider on their own, helping everyone push boundaries and open new avenues for product development and problem-solving.

Data analysis and decision-making:

Generative AI can process and analyze vast amounts of data more efficiently than traditional manual methods, providing your SMB with actionable insights for strategic decision-making. By leveraging these insights, SMBs can make informed decisions quickly, adapt to market changes, and identify opportunities for operational and revenue growth.

Personalized customer experiences:

Generative AI can analyze your customer data to deliver highly personalized content, recommendations, and services. This customization enhances customer satisfaction, loyalty, and engagement, directly impacting sales and brand reputation. It also can help your user experience team develop and refine their processes with new ideas.

Rapid prototyping and development:

Generative AI accelerates the prototyping and development process, and can enable your small business to quickly iterate and refine products, services or solutions based on realistic models and simulations. This agility is crucial in today’s fast-paced market, allowing you to stay ahead of shifting trends and new competitors. Combined with the help and support of the right IT partner, your SMB can drive innovation at a rapid pace. 

As this technology continues to evolve, the potential applications of generative AI can potentially reshape how we interact with and leverage technology in our daily lives and work.

What are the key considerations for generative AI?

The emergence of generative AI marks a significant milestone in technological advancement, presenting substantial opportunities for expedited product development, improved customer engagement, and enhanced workforce productivity. 

However, the realization of these benefits is intricately tied to your application context. Your business needs to maintain a pragmatic approach towards these technologies, especially when deploying ready-made solutions that inherently possess considerable limitations.

Generative AI is susceptible to generating outputs that may be flawed or exhibit bias, which requires careful human oversight to avoid incorrect or inappropriate content. This requirement for validation not only reaffirms the critical role of human intervention with emerging AI tools, but may also mitigate the efficiency gains anticipated from these technologies. 

It is recommended to align your SMB’s specific use cases for generative AI with your key performance indicators (KPIs) to ensure your AI initiatives improve operational efficiency and contribute to the enhancement of your business processes in a tangible, measurable manner. If you require help, managed services can help assess the best way to align your IT objectives.

What are the best practices for using generative AI?

To ensure the responsible and effective use of generative AI within your SMB, we recommend the following best practices for your executive leadership team:

Internal testing first:

Before public deployment, it is vital to rigorously test generative AI with your internal teams to prevent any potentially misleading outputs from impacting your brand.

Ensure transparency:

Clearly disclose the use of gen AI in interactions, ensuring that your users are aware they are engaging with a machine, across all communication touchpoints.

Establish safeguards:

Implement robust mechanisms to monitor, identify, and correct biases and inaccuracies, continuously validating the AI’s performance to maintain trustworthiness.

Protect data privacy:

Ensure sensitive information is not used by the AI, confirming with providers that it remains confidential and is not used for external machine learning purposes.

Proceed gradually:

Maintain new AI functionalities in beta for an extended period to manage expectations and refine performance based on feedback and operational insights.

It is crucial to create an AI policy for your business.

How do I draft an AI policy for the workplace?

With the adoption of generative AI by your workforce, whether for experimental purposes or operational tasks, it’s crucial to regulate its use to prevent unauthorized “shadow” usage and ensure adherence to data compliance standards. One suggestion from Gartner is to formulate a concise usage policy rather than impose a blanket prohibition.

A simple and effective policy could encapsulate key guidelines, such as three prohibitions and two mandatory practices when utilizing ChatGPT or similar pre-built models. Here is an example of a generative AI policy:

Prohibitions
Mandatory Practices

For companies deploying their proprietary large language models, while privacy concerns related to input data may be mitigated, the need to meticulously scrutinize any generated content remains unchanged.

Which companies have used generative AI?

Today, several businesses, both large and small, across multiple industries are leveraging generative AI tools to enhance productivity and innovation or help their clients do so, demonstrating many real-world practical applications and benefits of the technology.

Finance:

Yooz, a software development SMB servicing customers in the finance sector, provides an automated AI solution aimed at assisting accounting and finance leaders with managing invoices by streamlining and automating the invoice processing workflow, using generative AI to reduce manual effort and enhance efficiency in financial operations​ by handling much of the data entry process.

Marketing:

Sudowrite is a software development company that helps its customers ideate content, concepts and solutions using its generative AI writing assistant solution, which rewrites submitted content in different ways to provide a tool for interactive, creative brainstorming.

Technology:

Microsoft has used generative AI to reinvent its Bing search engine and Edge Web browser using advanced language models, such as GPT-3 and GPT-4, providing their substantial global user base with a conversational search experience and dynamic interactions.

Travel:

Expedia entered a partnership with generative AI company ChatGPT to integrate conversational AI into its travel planning software platform, streamlining the planning process for its customers while introducing a sophisticated virtual travel agent to engage in life-like conversation with, enhancing its unique user experience.

While publicly documented use-cases of generative AI are still emerging, these examples show the real potential for the emerging technology in today’s business. 

Implementing generative AI into your own processes in a meaningful way, however, may seem complex for your SMB, especially if you have limited technical resources. That’s where outsourced partners, such as managed IT services, come in. These providers offer expertise in integrating AI technology solutions seamlessly into your existing IT infrastructure, allowing your SMB to harness the power of generative AI without the burden of managing it all yourself.

Understanding generative AI in business: Next steps

As an SMB with a smaller IT presence, it can understandably be a challenge to adopt or implement sophisticated technology solutions without the right in-house expertise or resources.

By engaging with a certified MSP such as SparkNav, you can leverage generative AI solutions and begin harnessing these cutting edge tools in your business operations, while also benefiting from our proactive support and breadth of IT services across cloud, security, data and more.

Robert Griffin
Robert Griffin
As COO, Robert Griffin plays an instrumental role in aligning operational excellence with strategic goals by leveraging his decades of experience in enterprise leadership. With deep knowledge and expertise in security, governance, risk, and compliance (GRC), and AI, his insights are often shared through thought leadership channels, including LinkedIn and blogs. → Follow Robert on LinkedIn.
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