Unleashing Generative AI: A Transformative Force for Businesses

What Is Generative AI?

To gain a competitive edge, business leaders first need to understand what generative AI is.

Generative AI is a set of algorithms capable of generating seemingly new, realistic content—such as text, images, or audio—from training data. The most powerful generative AI algorithms are built on top of foundation models trained on vast quantities of unlabeled data in a self-supervised way to identify underlying patterns for a wide range of tasks.

How Is Generative AI Governed?

Generative AI systems are democratizing AI capabilities that were previously inaccessible due to the lack of training data and computing power required to make them work in each organization’s context. The wider adoption of AI is a good thing, but it can become problematic when organizations don’t have appropriate governance structures in place.

Ways to Use Generative AI in Your Company

Generative AI models are being used for a variety of industry-specific enterprise use cases that range from simple customer service support and coaching to more complex tasks like medical imaging and drug discovery.

But generative AI can also be trained to handle more generic business tasks that are relevant to all businesses, regardless of industry or size. Below are seven of the most useful ways business leaders can incorporate generative AI into their business today:

1.) Smart, Secure Data Analytics

• Generative-AI-powered data analytics solutions make data analysis smarter, speedier, more scalable, and more secure. Depending on the model type and size you select, you can use generative AI to generate synthetic data for more secure and compliant data analysis and QA testing practices.
• Generative AI models allow users to create narrative explanations of their data, making it easier for non-data scientists to understand the data they have in front of them.
• Generative AI assistants are being embedded and used in common data tools like Power BI, which enables less technical users to comprehend complex datasets and create accurate data visualizations and reports.
• Example solutions: SparkBeyond Discovery, Dremio, Narrative BI, Power BI Copilot

2.) Customer Service and User Experience

• A variety of generative AI tools are springing up to mentor your existing customer service agents and, in some cases, replace them with chatbot, voice generation technology, and AI-powered search engines that are designed with customer queries and natural language requirements in mind.
• In more specialized industries, like insurance, generative AI can also be used to better assess risk and achieve optimal outcomes for customers’ plan rates and claims.
• Example solutions: Gridspace, IBM Watson Assistant, UltimateGPT, Zendesk Advanced AI, Forethought SupportGPT

3.) Assistive Coding and Product Design

• Regardless of your tech teams’ levels of expertise, generative AI can be incorporated to support coding and quality assurance tests for digital product design. Generative AI models like ChatGPT can fix bugs, generate test code, and write documentation for programs.
• Generative AI models can help developers of all skill levels solve problems; even non-technical team players can generate code through natural language queries.
• Example solutions: Code Snippets AI, ChatGPT, Google Bard, Tabnine, MOSTLY AI, Stability AI

4.) Content Creation

• Generative AI models have already proven their ability to quickly generate natural language content affordably and at scale, which has made these models particularly enticing for organizations that want to outsource content writing.
• Several generative AI tools are able to write blog and social media content based on simple prompts. Others are designed to create more specific content, like legal documents or marketing materials.
• The area of generative AI content generation that is growing most quickly is multimedia content, with AI models used to create content like marketing videos with AI-generated voices and avatars.
• Example solutions: Jasper, Notion AI, Phrasee, HubSpot Content Assistant

5.) Document Summarization

• A simple but effective way to use generative AI in your business is to summarize long and complex documents.
• This can be a quick and easy way to identify the most important points in a long contract, legal document, conversation transcript, or other long-form content type.
• Example solutions: Cohere Summarize, Anthropic Claude, PatentPal, AI21 Studio

6.) Project and Workflow Management

• Several of the most popular project management platforms have or are currently adding generative AI assistive capabilities to their features lists.
• These AI assistants can help with administrative tasks like note-taking during meetings, email writing, and document summarization, freeing up time for your employees to focus on more strategic tasks.
• Example solutions: Process Street, Wrike, Notion AI, ClickUp, Asana

7.) Cybersecurity Management

• Although generative AI can pose major risks to cybersecurity postures, these AI tools are also valuable for automating business cybersecurity management workflows and handling data security requirements.
• Some of the most common ways businesses are taking advantage of generative AI in cybersecurity include smarter threat intelligence and hunting, contextualized security environment recommendations, and attack simulations.
• Example solutions: Google Cloud Security AI Workbench, Microsoft Security Copilot, CrowdStrike Charlotte AI, Airgap Networks ThreatGPT

Benefits of Leveraging Generative AI in Your Business

Depending on how (and how much) you choose to leverage AI capabilities in your organization, expect the following generative AI benefits from your efforts:

1.) Quicker delivery times on projects:

• Generative AI models can quickly generate responses, new content, useful data, and other value your organization may need at scale. This leads to more efficient production and allows your team to complete projects on tighter timelines, which is ultimately more cost-effective.

2.) Fewer hands-on employee requirements:

• Whether you lack the in-house staff expertise to complete a certain task or find that certain tasks are too tedious to hold employee attention, generative AI tools can pick up the slack and deliver consistent, accurate, and repeatable results.

3.) Support and QA for employee-driven tasks:

• Particularly with coding and product development tasks, but also with data analytics and other content forms, generative AI tools can act as quality assurance analysts, checking employee work for errors and sometimes correcting those errors without human intervention.

4.) Enhanced user experiences:

• Many generative AI tools are embedded in software that your employees or customers already use. With the help of generative AI, search engines, enterprise knowledge bases, project management platforms, and other user-facing tools are optimized for natural language inputs and an overall better user experience.

5.) Industry-specific solutions:

• Several generative AI models have been fine-tuned to address industry- and function-specific requirements across sectors like healthcare and pharmaceuticals, insurance, manufacturing, and more. If you’re operating in a specialized industry — even with strict compliance requirements — there’s more than likely a generative AI model that will address your needs and simplify your workflow.

Challenges of Using Generative AI

Many risks come with using generative AI, and especially because this technology is still so new, not all ethical use dilemmas have been worked out. If you choose to use generative AI in your business, be on the lookout for these possible challenges you’ll face:

1.) Limited regulatory requirements and guidance:

• Generative AI is not currently regulated on a larger scale, and at this time, there are few comprehensive usage frameworks that cover how your organization can use generative AI compliantly and effectively.

2.) Lack of data and training transparency:

• Although many generative AI vendors are beginning to improve in this area, most are still not transparent with the types and sources of the training data they use, which leads to additional compliance and usage concerns.

3.) Cybersecurity concerns:

• In the wrong hands or in the hands of an ill-trained employee, sensitive data may be exposed to generative models and become part of the training set. Bad-faith actors can also train these models to work around their rules to complete unauthorized tasks.

4.) Identifying inaccurate outputs:

• Generative AI models use natural language so well that it’s sometimes difficult to determine if a model has generated an inaccurate response. Users will need to have the skills and nuanced knowledge to check their own work when using this type of technology.

5.) Employee usage errors:

• It’s difficult to regulate and control how employees use generative AI in their work. Coming up with an AI use policy is a good step toward mitigating these errors, but there’s still no consistent way to enforce usage rules, control what data employees use as inputs, or monitor multiple employees’ actions at once.

What Are Generative AI Best Practices for Business Usage?

Establishing best practices and procedures for generative AI use is the best way to manage internal business use of these models. Follow these best practices for better generative AI outcomes in your business:

1.) Use data from verified, credible, and approved sources as inputs.
2.) Develop an AI use policy and training plan that clearly outlines how and when employees can use generative AI tools.
3.) Partner with a generative AI vendor building models that can scale or be fine-tuned as your business requirements evolve; it’s also beneficial to identify vendors with products that integrate with your existing tool stack.
4.) Do your due diligence and research generative AI vendors’ policies, terms, and reputations before committing.
5.) Consider your budget and what tools fit into that budget; many foundation models are not that expensive but can quickly go up in price depending on your usage volume.
6.) Consider any generative AI models you use as part of your cybersecurity threat landscape and protect accordingly.

THIS ARTICLE WAS ALSO FEATURED IN BOTTOM LINE VOL. 28. READ THE ARTICLE AND MUCH MORE RELATED CONTENT HERE!