Generative AI is now widely available to businesses, but knowing where to use it can still be difficult. Leaders need to separate viable use cases from hype and identify where the technology can address a genuine business need.
In this post, we look at how businesses are using generative AI, explain the benefits and challenges involved, and outline a practical approach to adoption.
What is generative AI for business?
Generative AI for business is the use of AI models to create or transform content such as text, code, images, audio, and structured outputs.
What makes modern generative AI models particularly useful in business is their ability to interpret natural-language instructions and, increasingly, work with different input modalities. This versatility allows them to perform a wide range of tasks without being specifically trained for each one, using instructions and context to shape what they produce.
How can businesses use generative AI?
The same core capabilities of generative AI can be applied to very different kinds of work across functions and industries.
Here are some of the main ways businesses are using the technology.
Knowledge search, retrieval, and synthesis
Businesses can use generative AI applications to search, retrieve, and synthesize information from internal and external knowledge sources, such as company policies, technical documentation, and academic research.
This often works through retrieval-augmented generation (RAG). A retrieval system first finds information relevant to the user’s query, then the generative model uses that information as context to produce an answer.
Content creation and transformation
Generative AI can create new content or reshape existing material for a different purpose, format, or audience. Businesses can use it to draft reports, write marketing copy, summarize meeting transcripts, translate customer communications, or reformat research findings into presentation slides.
Multimodal generative models can extend these capabilities beyond text, allowing businesses to generate or modify images, audio, or video.
Conversational assistance
Conversational assistants give customers and employees a natural-language interface for asking questions and working through tasks. Businesses can apply these systems to customer service, internal IT or HR support, or employee training and onboarding.
Compared with traditional scripted chatbots, generative conversational assistants can handle a broader range of free-form requests and produce responses that account for earlier exchanges.
Software development
Development teams can use generative AI to write and explain code, debug errors, generate tests and documentation, or refactor existing software.
Coding assistants can combine a developer’s instructions with relevant project context, including source code, dependencies, error messages, and technical documentation. This allows the model’s suggestions to account for how the software already works.
Further reading: Introducing North Mini Code: Cohere’s first model for developers
Data analysis and decision support
Generative AI can help business users query and interpret data using natural language. For example, users can ask questions about sales, financial, operational, or customer data to surface trends, compare cohorts and timeframes, or identify outliers. In these applications, a generative model often works alongside databases or analytics platforms that handle the underlying data retrieval and calculations.
Workflow automation and AI agents
Businesses can use generative AI to help automate multi-step processes. For example, a team might integrate AI into a predefined workflow, using it to interpret incoming requests, generate responses, extract information for record updates, or determine where cases should be routed.
AI agents offer more adaptive automation for open-ended workflows. Rather than relying entirely on a predefined sequence of steps, an agent determines what to do next based on the task, the tools and information it can access, and the results of earlier actions.
What are the benefits of generative AI for business?
Well-targeted use of generative AI can create value across multiple parts of a business.
Here are some of the main benefits:
Increased productivity and improved work quality
Generative AI can reduce the time and effort required for many knowledge-work tasks, which can increase overall throughput for the business. It can also improve work quality by helping employees apply standards more consistently, spot errors or omissions, and draw on relevant knowledge from multiple sources.
More personalized user experiences
Businesses can use generative AI to tailor content and interactions to user-specific context, such as job role, account information, or communication history. This capability allows businesses to scale personalization across customer and employee experiences, including through tailored marketing messages, sales proposals, account summaries, and role-specific briefings.
Faster innovation and expanded business capabilities
Teams can accelerate experimentation by using generative AI to brainstorm ideas, build prototypes, and test different concepts, designs, or approaches. The technology can also enable entirely new workflows, products, or services that would otherwise have been too costly or impractical to deliver.
What are the challenges of adopting generative AI in business?
Generative AI can create significant value for businesses, but realizing that value requires overcoming a range of practical hurdles.
Some of the main challenges include:
Business and technical viability
There is no shortage of possible business uses for generative AI. The challenge is identifying where the technology can address a specific business need well enough to make the investment worthwhile. Organizations that adopt AI without a clear business case risk introducing additional cost and complexity without creating enough value in return.
Technical requirements significantly affect the viability of AI initiatives. Production deployments often require access to reliable data, integration with existing systems, and infrastructure that can meet scale and performance requirements.
Reliability, security, and governance
Generative AI can produce plausible but inaccurate answers, and its behavior can vary based on the instructions and context it receives. This can make it challenging to maintain reliable performance across the range of inputs and edge cases an AI system may encounter.
Security, privacy, and operational risks can increase when AI systems are given access to sensitive data or permission to act in other business applications. An AI system that misinterprets instructions, relies on incorrect or incomplete information, or selects the wrong action can expose data or make unintended changes in connected systems.
Greater access and autonomy also make governance more demanding. Organizations need to set and enforce limits on what the AI can access and do, determine when human review is needed, and define who remains accountable for results.
Process change and workforce adoption
As AI takes on parts of existing workflows, roles and processes may need to change around it. Employees might shift from performing a task themselves to directing an AI system, reviewing its outputs, handling exceptions, or applying human judgment at key points. If the division of work between people and AI is unclear, some tasks may be duplicated, others may be missed, and accountability for the final outcome can become blurred.
Realizing the full value of AI in day-to-day work depends on employees knowing when and how to use it. Gaps in training and guidance can lead to low adoption and inconsistent or inappropriate use.
How to adopt generative AI in your business
While the path to AI adoption varies from one business to the next, most organizations still need to do the same foundational work.
Define objectives and assess readiness
Organizations should first identify the business priorities generative AI should support, set clear objectives around them, and define the initial scope of adoption. This clarifies what success should look like and keeps the adoption effort focused on the intended business outcomes.
Organizations should also assess their current capabilities and constraints to determine whether that initial scope is realistic. That assessment should cover the availability and quality of relevant data, existing systems and infrastructure, governance and security capabilities, and workforce skills.
Explore and select the solution approach
The right solution approach needs to fit both the organization’s existing capabilities and constraints and what it needs from the technology itself. That includes whether the solution can handle the intended use cases, integrate with the relevant data and systems, meet security and privacy requirements, deliver the necessary performance and scale, and do so at an acceptable cost.
Organizations can use packaged applications, build on configurable AI platforms, or work directly with foundation models to create more customized solutions. Depending on the solution type and requirements around security, performance, and control, the AI system may be provider-hosted, run in a private cloud, or deployed on premises. Organizations also need to decide how much implementation work to handle internally and where to draw on support from technology vendors, consultancies, or systems integrators.
Operationalize the solution and monitor value
Once the solution is selected, it needs to be prepared and tested before being put into production to confirm it performs as intended under realistic conditions. Preparing the system for deployment may involve connecting the solution to production data and systems and applying appropriate controls. It may also require organizations to adapt affected workflows and provide users with appropriate training and guidance.
After deployment, organizations should monitor the quality and reliability of the system, usage patterns, and running costs, and assess whether it is contributing to the intended business outcomes. These results can inform future improvements to the system and how it is used, as well as decisions about whether to expand, scale back, or replace it.
Final thoughts
As generative AI becomes more widely accessible to businesses, the technology itself is likely to become less of a competitive differentiator. The more durable advantage will come from how effectively organizations use it to streamline operations and turn their unique data and expertise into better decisions, products, and services.