Five generative AI tips for every business leader
Generative AI gives organizations the unique ability to glean fresh insights from existing data and produce results that go beyond the original input. Companies eager to harness these benefits can leverage ready-made, budget-friendly models and customize them with proprietary business data to quickly tap into the power of AI.
The right generative AI solutions can unlock a world of opportunities for business leaders aiming to increase efficiency, drive productivity, and boost performance. But first, they need to consider where it fits in their organization, which processes will benefit the most, whether to buy or build it, and what it’ll cost.
Business leaders ready to embrace AI can consider these five key points.
1. Enhance Job Functions and Operations
Generative AI harnesses data, automates processes, and is reshaping the fundamental approach to businesses and job functions. Creating new insights from data lays the groundwork for a range of applications, from optimizing operations to driving innovation and creativity.
Every organization has specific datasets that are crucial to its operations. By using retrieval-augmented generation (RAG), enterprises can tap into their own data to build AI applications designed for their specific business needs. By connecting generative capabilities to carefully selected data sources, RAG enables customized generative AI deployment that can support workers in their job roles.
Take healthcare, for instance. RAG-equipped generative AI can analyze patient data, summarize key points, and draft custom care plans to support physicians and improve the patient experience.
2. Build or Buy?
Business leaders should decide whether to develop their own generative AI solution from scratch, implement a pre-built one, or fine-tune foundation models. Building a tailored solution might be the way forward if an organization has specific needs, unique data, or specialist knowledge, but this generally requires more time. If an enterprise needs AI functionality for general purpose tasks, purchasing it is often the better choice. Ready-made solutions can be set up faster and provide immediate value.
If there’s flexibility in the timeline, creating a custom solution can provide greater value.But the skills required to develop in-house generative AI tools are specialized and in high demand, so getting the right people on board is a must. Additionally, connecting open-source solutions or foundation models to handpicked proprietary data with RAG can provide a shortcut to customized AI.
Integration with existing infrastructure systems also needs careful evaluation, as businesses must determine whether current vendors can adequately support them. A clear understanding of procurement processes and timelines can help prevent delays.
With in-house development, long-term costs associated with staffing, development, and maintenance can add up. These costs can be justifiable if the AI tool provides a distinct advantage. However, end-to-end in-house development might not be economically sensible if existing or off-the-shelf tools can perform similar functionalities.
3. Business Needs
AI applications cut across industries and different business areas. Chatbots can transform customer service with prompt responses; translation features facilitate access to information in any language; and in software development, AI drives productivity by generating code comments and functions.
To increase the chances of AI success and reduce time to value, business leaders should select high-impact use cases for generative AI implementation. Consider the organization’s specific needs, employee and customer pain points, and which processes are ripe for AI enhancement. To build consensus, it’s helpful to gather feedback from stakeholders who will interact with AI systems to understand their needs and potential resistance points.
Learn and build confidence with pilot projects that can reveal how well AI integrates with existing systems and how significant improvements are. Consider whether the solution can be scaled across the organization to maximize impact. For example, can a customer service bot supporting call center staff be connected to a different data source to support the sales team, too?
To more quickly identify use cases and develop an implementation plan, consider consulting with AI experts or technology providers who can share peer examples and help pinpoint areas where AI can provide the most value.
4. The Cost of AI
Successful AI implementation requires investment in infrastructure, expertise, software components, the collection of necessary data, and ongoing maintenance.
But not every company has to start from zero. Many opt for pre-trained or foundation models, which are more economical to train. These models only require customization with business-specific data and the implementation of brand guidelines. Although initial costs can be high, with expected improvements in efficiency and innovation, executives are enthusiastic about AI adoption.
5. Safety and Security
As generative AI becomes more encompassing, it must be used safely and responsibly. Guardrail tools and data governance for large language models (LLMs) ensure that AI systems adhere to intended functions and prevent deviations. They help set boundaries in several areas:
- Topical guardrails keep AI models focused on specific topics and prevent them from addressing unrelated subjects
- Safety guardrails help deliver accurate and appropriate responses. They filter out inappropriate language and ensure that references are reliable
- Security guardrails limit the ability to connect with third-party applications, ensuring models only interact with secure sources
- Data Governance rules that control access to data based on business priorities and security concerns help ensure ethical and safe AI implementation
Generative AI boosts innovation, efficiency, and problem-solving capabilities while managing risk. Learn how business leaders can capitalize on AI to compete in a fast-changing market.
