Generative AI Trends For Business: Why, When, And Where To Begin

Generative AI (genAI) is fundamentally changing the way we interact with technology — and the world. It is the fulcrum that businesses rely on to enhance, empower, and engage employees and customers. While genAI has been around for years, the emergence of consumer-facing AI tools like ChatGPT has catapulted the groundbreaking technology to a new level. This evolution marks the first productivity-centric wave of genAI’s impact on companies — revolutionizing workflows, streamlining operations, and redefining the boundaries of enterprise innovation.

Forrester defines generative AI as “a set of technologies and techniques that leverage massive corpora of data, including large language models, to generate new content (e.g., text, video, images, audio, code).” Over the next few years, generative AI trends are expected to reshape every knowledge-fueled task and role, reconstruct knowledge practices and resources across every industry, and deliver a tenfold increase in task productivity. This is not just speculative; it’s already becoming reality for businesses such as KPMG that recently saw a 50% boost in productivity after implementing Microsoft Copilot, a set of productivity applications built on OpenAI’s large language model (LLM).

Despite the profound advancements and emergence of new generative AI trends, we’ve only just scratched the surface. Forrester believes that the evolving generative AI landscape will ultimately help businesses generate new revenue streams, extensions, and channels to achieve profound growth and reinvent themselves, forcing entire industries to restructure around the value generated by machines.

A set of technologies and techniques that leverage massive corpora of data, including large language models, to generate new content (e.g., text, video, images, audio, code). Inputs may be natural language prompts or other non-code and non-traditional inputs.

How Forrester defines genAI

Why GenAI Now?

According to Forrester’s May 2024 Artificial Intelligence Pulse Survey, 67% of AI decision-makers say their organization plans to increase investment in generative AI in the coming year. This burgeoning field of AI is not just a trend; it presents an opportunity to accelerate innovation and close the performance gap. Those that use it to augment and enhance their existing business processes benefit in ways that were previously impossible, with the ability to:

  • Expand the breadth and depth of human creativity across all skill levels. Using genAI, professional creators can push the limits of their imagination to craft new and innovative works, while those without formal training can easily create high-quality visual content.
  • Produce quality content at scale. Marketers are leveraging genAI to create vast amounts of diverse content quickly in dozens of languages and formats, catering to various audiences.
  • Bolster data science practices and app development. GenAI is software development. For example, tools such as TuringBots are being used to write and refine code, though these tools are in their early stages across the entire software development lifecycle, from requirements to deployment.
  • Supplement data with synthetic equivalents. Using genAI, businesses can generate synthetic data to augment their data sets while preserving confidentiality and privacy. Synthetic data is being used today in financial services, healthcare, and other industries where the development of AI applications has been hindered by the need to maintain data privacy. Synthetic data is being used today in financial services, healthcare, and other industries where the development of AI applications has been hindered by the need to maintain data privacy. Synthetic data is being used today in financial services, healthcare, and other industries where the development of AI applications has been hindered by the need to maintain data privacy.

Businesses that embrace generative AI technologies to deliver better experiences, create more innovative offerings, and boost productivity will realize outsized growth and outpace their competition.

According to Forrester’s May 2024 Artificial Intelligence Pulse Survey, 67% of AI decision-makers say their organization plans to increase investment in generative AI in the coming year.

Generative AI has the power to be as impactful as some of the most transformative technologies of our time. The mass adoption of generative AI has transformed customer and employee interactions and expectations. As a result, genAI has catapulted AI initiatives from ‘nice-to-haves’ to the basis for competitive roadmaps.

Srividya Sridharan
VP and Group Research Director at Forrester

GenAI Risk: Potential Pitfalls And Solutions

The benefits of generative AI are numerous and growing. Like any paradigm-shifting advancement, however, it also comes with a series of drawbacks that, if overlooked, could result in costly mistakes and setbacks. Yes, generative AI makes doing good things easier — but it also comes with its fair share of risks.

  • Pitfall: GenAI will increase the frequency of traditional attacks
    Generative AI technologies such as ChatGPT and Stable Diffusion are making it easier for malicious actors to con their targets and compose their attacks on a greater scale. With the ability to create compelling and believable text and images, genAI improves the quality of phishing websites and emails considerably.
    Solution: SOC practitioners must be more vigilant
    Security operations center (SOC) teams are already becoming well versed in identifying, resolving, and mitigating these threats — and they must continue to keep their finger on the pulse of this evolving landscape. While we expect cyberattacks to increase in number, the threats themselves are not novel. And because attackers are still successfully launching attacks using traditional techniques, they have little incentive to invest time and resources in experimenting with genAI to create more sophisticated avenues of attack.
  • Pitfall: GenAI may cause trust and reliability issues
    Because these advanced generative AI models are capable of producing outputs that closely mimic human behavior, users may be lulled into a false sense of security, trusting their outputs at face value. In reality, these advanced tools must be evaluated with a critical eye and held to the same stringent standards as any other black-box AI.
    Solution: Incorporate explainable AI
    Explainable AI has emerged as a way to demystify the inner workings of complex AI systems, offering a window into AI’s decision-making process. Using explainable AI, users can better understand the rationale behind AI-generated outcomes to deliver more reliable outputs, engender stakeholder trust, and meet auditing requirements.
  • Pitfall: Employees fear job loss
    The emergence of genAI and its seemingly infinite capabilities has left many wondering if their job is at risk. One Forrester survey found that 36% of workers fear losing their jobs to automation or AI in the next 10 years.
    Solution: Leaders must set the record straight
    Leaders must communicate that AI’s full potential is realized when used in conjunction with human creativity and decision-making — and actively promote its adoption as a means for employees to boost their job performance and professional development. While genAI brings substantial advantages, it is crucial to acknowledge its limitations without human insight and oversight. Additionally, Forrester forecasts that by 2030, only 1.5% of jobs will be lost to genAI, while 6.9% will be influenced by it. With this in mind, leaders should prioritize transparency about these ideas and statistics to motivate their workforce about the future of AI in the workplace.
  • Pitfall: Generative AI creates intellectual property and copyright challenges
    As LLMs become more common, there’s a risk that they inadvertently create content that infringes on existing copyrights, even if the input data was used under fair-use provisions. This raises questions about the legal implications of using such AI-generated content and how it might affect the original content creators’ IP rights.
    Solution: Stay informed about related case-law development
    To navigate the complexities of copyright and intellectual property in the age of generative AI, businesses must stay informed about evolving case law. As precedents are established and regulations are updated, organizations can adapt their use of generative AI for content creation, ensuring compliance while harnessing the technology’s full potential.

Worried that AI might stifle innovation and creativity within your organization? In fact, the reality is quite the opposite. GenAI technology acts like an assistant — aiding creative teams with idea generation, content development, and evaluation. Get our e-book, Avoid Common AI Myths And Focus On Best Practices, to learn more.

Where And When To Start: Eight Generative AI Use Cases

Generative AI is no longer a nice-to-have; it’s a strategic priority. But given its complexity and the broad range of potential applications, integrating it into business processes may seem overwhelming. To embrace the full potential of generative AI, businesses should begin by identifying areas within their current operations where this technology can bring improvements and explore practical, measurable use cases.

Generative AI For Customer Experience

Use Case 1: Enhanced CX Interaction And Accessibility
In today’s market, customers expect more personalized attention and services that reflect their unique preferences and behaviors. Generative AI arms customer service agents with sophisticated tools to interpret and distill extensive customer information to create more meaningful interactions that resonate deeply with customers’ needs — without repetitive requests for information. Plus, with the ability to quickly analyze and synthesize large volumes of feedback, generative AI helps service teams respond to concerns with greater accuracy while protecting customer privacy and data integrity. This proactive approach supports a more intuitive and responsive customer experience (CX) that fosters loyalty and satisfaction.

Use Case 2: Customer Service
Though using AI in customer service settings such as call centers and automated self-service systems (like chatbots) is a well-established practice, these traditional AI systems follow strict, predefined rules or scripts, which can lead to inflexible and sometimes frustrating interactions for users. GenAI, on the other hand, is dynamic and contextual, capable of providing sophisticated and articulate responses that significantly enhance the customer experience. By augmenting customer service team members’ capabilities behind the scenes, generative AI facilitates better customer support, allowing agents to excel across the top drivers of CX: answering questions faster and better, resolving problems on first contact, communicating clearly, and leaving the customer feeling respected. To ensure that new customer service tools powered by generative AI align with and enhance overall customer experience goals, leaders should carefully plan, implement, and assess the impact of these innovative solutions within their customer service operations.

Generative AI For Technology

Use Case 3: Knowledge Sharing And Next-Gen Search
Generative AI helps create a more connected and informed workforce where knowledge is dynamically shared and updated. Businesses can tap into their own wealth of proprietary data just as easily as asking ChatGPT for information to streamline efficiencies and boost productivity. By cutting down the time employees spend hunting for data, they not only make faster decisions but also free up time to focus on more strategic activities that add value to the business. GenAI can summarize meetings, pinpoint action items, recommend resources tied to discussion points, and draw on past projects to inform current work — and can even auto-generate knowledge articles from interactions with employees or customers. By integrating genAI, companies foster a workplace that’s continuously learning and evolving, keeping knowledge fresh and accessible for everyone.

At Forrester, we’ve built our own generative AI tool, Izola to help clients get quick insights from our research to make progress on their top initiatives. Learn more about Izola.

Use Case 4: Developer Productivity
TuringBots are transforming enterprise software development by enhancing several stages of the development process. Coding TuringBots, such as GitHub Copilot, generate code from basic prompts that can be directly used for testing. They are also capable of identifying and rectifying vulnerabilities in the code, optimizing the unit testing phase. Additionally, many businesses are now using mature tester TuringBots to automate visual testing of thousands of GUIs across browsers, mobile devices, and other platforms.

Use Case 5: Data Innovation
Data scientists are seeing similar benefits from generative AI as developers (particularly in using TuringBots to support ModelOps and DataOps), along with a range of niche benefits. It’s already a significant part of the synthetic data landscape, offering tools and methods for generating extensive data sets for analysis or machine-learning model training. Today, data scientists can use vendor tools and LLMs to create synthetic data sets of structured data, text, images, or entire 3D environments. Tomorrow, they will use these advancements to enhance data visualization and storytelling, making insights more accessible and actionable through sophisticated, AI-generated infographics and interactive data representations.

Use Case 6: Threat Intelligence
Security professionals are using genAI to strengthen their defenses. For example, generative AI can be used to create biometric data for penetration testing, to sift through emails to spot signs of phishing or other social engineering attacks, and to self-document system behaviors and functionality. In the future, we expect that generative AI will be used to detect and document the deficiencies of IT systems and proactively suggest resolution scenarios to security analysts.

Generative AI For Sales

Use Case 7: Sales Optimization
Using genAI, sales teams have a tremendous opportunity to ’s profile and behavior to improve response rates, generate customized sales scripts to foster more impactful conversations, and quickly develop pitch decks to succinctly convey value to their audience. GenAI is also being used to create content outlines, come up with ideas for contact emails, and even ensure that language being used is inclusive in nature.

Generative AI For Marketing

Use Case 8: Content Creation
Generative AI is revolutionizing the marketing function by streamlining content creation and design processes. Marketers are using genAI to generate massive sets of content that consistently embodies their brand voice and engages customers — from blog posts and social media updates to email newsletters and more. Meanwhile, graphic designers are leveraging the technology to iterate more quickly, come up with innovative ideas, and automate routine design tasks. As a result, marketing teams can get more done in less time and reallocate resources to other revenue-generating priorities. In the future, marketers will use generative AI to quickly generate multiple content pieces from a single source and even plan and execute better-targeted messaging and campaigns.

To learn more about generative AI trends and how to secure your use of generative AI with a framework for success, watch our webinar: Close Gaps In Your Generative AI Security.

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