9 Ways to Unlock the Power of Generative AI for Your Business

Generative AI (GenAI) is quickly becoming a force to be reckoned with. The emergence of generative AI tools like OpenAI’s ChatGPT, Google Bard, Claude, and Midjourney has presented immense advantages to businesses when used in tandem with human efforts. From automating operations for streamlining employee bandwidth to better strategizing, decision-making support, faster time-to-market, and rapid R&D, there are numerous benefits of generative AI for business.

GenAI can "generate" speech, text, audio, video, images, and more importantly, code by training on a large amount of collected data. And when combined with accurate human input, generative AI's benefits are transformative. According to a PwC report, AI has the potential to add as much as $15.7 trillion to the global economy by 2030. However, as companies increasingly strategize how to use generative AI for business success, many are realizing that there are various factors to consider, including ethical considerations, integration with existing systems, and monitoring outcomes.

This blog will cover nine ways to unlock the power of generative AI in business. We will also look at the current state of generative AI business applications to understand how companies already leverage this technology and how you can do the same.

Let’s dive right in.

Generative AI algorithms are built on top of foundational models trained on large quantities of unlabeled data to determine underlying patterns. Using this training data, GenAI generates realistic and seemingly new content—images, audio, or text—that revolutionizes business-customer interactions.

Generative AI tools can help leaders deploy unique outputs like videos or articles and streamline operational efficiency by automating tasks, summarizing documentation, and offering enhanced customer support through cutting-edge chatbots. According to a McKinsey report, about 75 percent of the value that generative AI use cases could deliver falls across four areas: customer operations, marketing and sales, software engineering, and R&D.

Let’s look at how companies are already capitalizing on the potential of generative AI.

Businesses are increasingly leveraging the power of generative AI to transform their marketing campaigns. Using generative AI tools such as ChatGPT, companies can create personalized, high-quality content at scale. These GenAI models craft compelling ad copies, generate product descriptions, and produce dynamic, data-driven visuals.

Generative AI algorithms analyze vast amounts of data, including customer preferences, demographics, and browsing behavior, to generate tailored marketing materials such as social media posts, email campaigns, and ads. A Hubspot report revealed that 85 percent of marketers find generative AI effective in personalizing their marketing content. Generative AI assists in automating the creative process, saving significant time and resources for employees to focus on other strategic initiatives. In fact, 63 percent of marketers report that AI-assisted content performed better than content created without AI.

Businesses use GenAI to accelerate the innovation process and unlock new possibilities. A McKinsey report indicates that GenAI has the potential to drive productivity gains valued at 10 to 15 percent of total R&D expenditures. Generative AI can optimize complex systems and processes by simulating multiple scenarios and identifying the most efficient outcomes.

For instance, in healthcare, generative AI analyzes vast amounts of data on existing drugs and diseases to generate potential new drug compounds. This significantly accelerates the drug discovery process, which traditionally takes years and a high degree of trial and error. Similarly, GenAI is used in other industries, including automotive, IT, and creative arts.

Generative AI revolutionizes software development by empowering businesses to create faster and more efficient applications. A Github report found that increased developer productivity due to AI could boost global GDP by over $1.5 trillion.

Generative AI algorithms assist in generating code snippets, automating repetitive tasks, and suggesting optimized architectures. GenAI also automates software testing and debugging by simulating different scenarios and identifying potential issues or vulnerabilities. A study revealed that software developers leveraging Microsoft's GitHub Copilot could complete tasks 56 percent more quickly than their counterparts who did not use the tool.

By integrating generative AI solutions, businesses accelerate software development life cycles, improve code quality, and deliver more innovative and reliable software products.

Accurate decision-making and data analysis are also significant benefits of generative AI in business. The very nature of generative AI makes it ideal for robust data analysis, as AI algorithms are highly equipped to summarize large datasets. This capability allows companies to extract meaningful insights and understand patterns and trends that lead to better decision-making.

Research reveals that generative AI integration can substantially boost productivity across a range of functions, including data ingestion, analysis, testing, and reporting. By automating these processes, generative AI enhances data-centric tasks' efficiency and facilitates agile decision-making.

By leveraging GenAI in customer service, companies enhance customer support capabilities and deliver personalized and efficient user experiences. Generative AI is used to develop intelligent chatbots and virtual assistants that understand and respond to customer inquiries in real time.

Research found that at one Fortune 500 company employing 5,000 customer support agents, the implementation of generative AI led to a 14 percent hourly increase in issue resolution and a 9 percent reduction in the time taken to handle each issue. By automating routine tasks and providing timely and accurate assistance, generative AI enables businesses to scale their customer service operations, reduce response times, and ensure high customer satisfaction.

Dealing with legal documentation is always challenging for businesses. However, generative AI is helping companies address these concerns via natural language processing models that automate and enhance various facets of legal contract management.

A report by Mordor Intelligence suggests that the AI software market in the legal industry is expected to rise from $0.94 billion in 2023 to $3.29 billion by 2028, at a CAGR of 28.50 percent. As per a Thomson Reuters survey, 82 percent of in-house legal departments want to integrate generative AI into their legal processes.

The increasing adoption of generative AI in legal contract management reflects a growing recognition of its transformative potential within the legal industry, and how it is reshaping the way businesses handle their legal documentation.

Project management is another emerging application of generative AI for business. Generative AI solutions can streamline project management and save resources. Gartner, Inc. predicts that by 2030, AI will take over traditional project management functions such as data collection, tracking, and reporting, eliminating 80 percent of today's project management work.

The following are the most common generative AI business applications for project management being implemented by companies, irrespective of industries:

• Automated documentation
• Project selection and prioritization
• Project timeline management
• Project documentation summarization
• Resource allocation and forecasting
• Communication enhancement
• Budget tracking
• Automated reporting
• Project change management

A study from the International Labour Organization (ILO) suggests that generative AI will likely impact the quality of jobs, specifically work intensity and autonomy, more significantly than leading to job destruction. Although generative AI excels at generating innovative solutions and processing vast data, human expertise brings critical thinking and contextual understanding that AI models lack.

With GenAI, businesses can automate repetitive and routine tasks, freeing up human resources to make higher-level strategic decisions and implement ethical guidelines to ensure that generated outputs align with business goals and values. Combining generative AI and human oversight is essential for businesses to augment their productivity and refine business output, leading to superior outcomes.

Generative AI business applications work at their full potential alongside accurate, information-rich human prompts. However, even with suitable prompts, companies may only be able to fully utilize generative AI if they implement the right strategies.

Here are nine ways you can effectively leverage generative AI for business.

Implementing generative AI begins with identifying which business areas can benefit the most from its implementation. Identifying use cases includes evaluating the quality, accessibility, and availability of your data. Large and high-quality datasets are often necessary for generative AI models, and you must ensure your organization has this data or a concrete plan to collect it.

After determining the use cases, it’s essential to prioritize. You must rank potential use cases based on their alignment with expected ROIs, business goals, and project feasibility and consider factors like data availability, potential AI risks, and implementation complexity. This allows your business to efficiently implement GenAI for rapid scalability and secured development, avoiding common pitfalls.

Rigid technology systems can stagnate your business, while flexible systems foster evolution and growth. You can dismantle data silos, foster cross-functional collaboration, and build a data-driven operating model by integrating generative AI into your existing systems.

Harnessing generative AI for business relies massively on seamlessly integrating AI into current systems. Here’s how you can ensure a robust integration:

• Choosing suitable models: After defining your integration objectives, you must select a generative AI model that aligns with your integration objectives and the data your business uses. You can choose from GPT series, LLaMA, Claude, PaLM 2, and other prominent models.
• Assessing data compatibility: Because GenAI relies heavily on data, assessing data compatibility is critical to implementing generative AI in business. You must evaluate existing data formats and sources to ensure they’re compatible with your chosen GenAI models.
• Selecting the correct integration approach: To implement generative AI, you must also decide whether to integrate it via API services or custom in-house solutions. The former is quicker and involves working with API providers to establish API access. The latter is more flexible, but you must assemble a robust team of AI engineers and software developers experienced with the chosen AI models.

Fully leveraging generative AI for business includes building secure, transformative, and business-ready GenAI solutions to ensure maximum data security, transparency, and privacy.

To practice maximum data privacy and security, your AI development team must follow these guidelines:

• Understanding data privacy and regulations: Teams handling GenAI initiatives must be fluent in industry-standard privacy regulations like HIPAA, CCPA, and GDPR. Understanding legal obligations will ensure maximum data security and compliance for your deployed generative AI solutions.
• Access control: Implement strict access control protocols to limit data access to authorized members only. Role-based access control (RBAC) is an excellent way to maintain access control and practice secure GenAI implementation.
• Secured data storage: Since implementing generative AI in business means dealing with a lot of data, you must store data in secure environments, whether in the cloud or on-premises. Teams can regularly patch and update data systems to address vulnerabilities and maintain maximum security.
• Vulnerability assessments: Regular vulnerability assessments are crucial to maintaining data integrity when using generative AI. Teams must conduct thorough assessments and frequent penetration testing to determine and mitigate potential threats in business systems and generative AI applications.

Generative AI requires high-quality data to train and generate actionable outputs. To fully unlock the power of generative AI for business, you must ensure that your business has a robust data infrastructure for gathering, storing, and processing data.

Ensuring robust data infrastructure usually involves data practices such as:

• Data collection and integration: Gather data from various sources and integrate it into centralized data repositories. Teams can use ETL (extract, transform, load) processes to ensure data consistency and quality while implementing GenAI strategies.
• Data access and APIs: Provide data and API access to generative AI models, allowing them to retrieve and interact with the data.
• Data cataloging: Create data catalogs that provide metadata information about datasets, including their schema, source, and usage to allow users to easily discover and understand data assets.
• Data quality assurance: Implement data quality assurance processes that clean, enrich, and validate data for generative AI implementation.
• Data governance: Establish data governance frameworks that define roles, policies, and responsibilities for data management, including data access control, compliance, and ownership.

Understanding the effectiveness of generative AI for business also means monitoring outcomes and making improvements. You must establish clear KPIs aligned with your business goals and the objectives when implementing GenAI. Additionally, defining baseline metrics before deploying your generative AI models is a good practice for evaluating the impact of GenAI-related outcomes and changes.

Monitoring AI outcomes is also driven by key practices like continuous data collection, real-time AI model monitoring, quality assurance of outcomes, sentiment analysis, and performance metrics. Following these practices will allow your teams to accurately assess how GenAI is transforming your business, identify areas of improvement, and streamline the overall process for better results.

Unlocking the power of generative AI for business is only possible with the right talent on board. You can do this in two ways: building in-house GenAI expertise by recruiting AI professionals or partnering with expert solution providers.

Although in-house team-building is flexible, it also requires more time and money investment. Alternatively, you can opt for industry-leading providers with a proven track record of delivering AI-powered scalability, engineering excellence, and tailored solutions for maximized business growth.

One of the most critical aspects of efficient GenAI implementation is human-AI collaboration. As mentioned earlier, generative AI in business isn’t meant to replace the people involved. As a business leader, you must foster a collaborative culture where your teams and GenAI solutions work together.

Fostering trust involves building interdisciplinary teams that unite individuals with various skills and backgrounds, including creative and AI professionals.

One of the best real-world examples of fostering trust within generative AI implementation is Toyota’s Toyota Research Institute (TRI). This institute is dedicated to creating robotics and AI technologies with an explicit focus on building AI that collaborates with humans. To take this further, TRI has also built the Toyota Partner Robot Program, where engineers and researchers can work together to build AI and GenAI-powered robotics.

Companies must follow ethical regulations and standards to successfully deploy generative AI solutions and achieve business goals. Teams must implement the following guidelines to abide by ethical protocols when implementing generative AI for business:

• Understand and abide by ethical guidelines applicable to AI in your business’s industry and region, such as GDPR (Europe) and HIPAA (healthcare).
• Appoint compliance teams or officers responsible for ensuring your GenAI initiatives align with ethical regulations.
• Conduct rigorous ethical impact assessments to understand the generative AI solutions' potential risks and ethical implications.
• Ensure your business’s data governance practices align with regulatory requirements. For this, teams can implement data security and privacy measures like encryption, anonymization, and access controls to protect sensitive data.
• Obtain explicit and informed user consent when collecting data to train GenAI algorithms.
• Enforce data retention policies to specify how long the collected data will be stored in systems and when it should be securely removed to comply with legal regulations.

Lastly, companies must stay updated with generative AI's evolution in business. GenAI’s impact on creative work and the overall business landscape is still uncertain. New developments will emerge, and as a business leader, you must stay informed and remain flexible to adapt and retain a competitive edge.

To this end, teams must stay current with emerging GenAI developments, tools, and technologies. Appointed team members must monitor developments in the ethical and legal landscape and remain willing to experiment with new methodologies to push time-to-market further and deploy cutting-edge solutions.

The capabilities of generative AI are almost endless. It’s still in a nascent phase where companies are just discovering what GenAI can do and learning more ways to unlock the potential of generative AI for business. According to Salesforce's recent State of IT report, 86 percent of IT leaders anticipate that generative AI will play a crucial role in their organizations.

By following best practices such as understanding and choosing ideal use cases, practicing data transparency and security, monitoring outcomes, and acquiring AI capabilities, businesses can equip themselves to capitalize on GenAI. However, that’s easier said than done, as generative AI is a complex technology to master.

At Turing, we offer expert enterprise generative AI services built on our in-depth experience driving top-down transformation for seamless tech implementation. Our technology experts and AI-powered expertise, scalability, and reach offer companies the competitive edge they need for their business endeavors. Turing has helped 1000+ companies across 40+ industries build cutting-edge products, and you can do the same.

Talk to an expert today and share your generative AI business needs!