Driving Productivity & Improved Outcomes with GenAI powered Assistants

Driving Productivity and Improved Outcomes With Generative AI-powered Assistants Mike Leone | Practice Director ENTERPRISE STRATEGY GROUP SEPTEMBER 2024 © 2024 TechTarget, Inc. All Rights Reserved.

Driving Productivity and Improved Outcomes With Generative AI-powered Assistants 2 Table of Contents Chapter 1: Introduction—Identifying and Leveraging a High-impact GenAI Use Case Chapter 2: How Goals and Challenges Drive Use Case Decisions Chapter 3: AI Assistants Leap to the Top of the GenAI Use Case Wish List Chapter 4: Specialized AI Assistants To Support the Entire Enterprise Chapter 5: Delivering GenAI That Adheres to Principles of Trust, Security, and Responsible Use Chapter 6: Conclusion © 2024 TechTarget, Inc. All Rights Reserved. Back to contents

CHAPTER 1: Introduction—Identifying and Leveraging a High-impact GenAI Use Case

Driving Productivity and Improved Outcomes With Generative AI-powered Assistants 4 Identifying and Leveraging a GenAI: Areas of Current Use vs. Future Potential High-impact GenAI Use Case Research 35% 39% Marketing 35% 45% New technologies come and go, but few in Software development 33% 43% recent memory have accelerated past the initial Product development 32% “industry buzz” phase into ideation, exploration, 37% experimentation, and endorsement faster than IT operations 28% 38% generative AI (GenAI). 26% GenAI adoption is surging: More than one Customer service 48% in four (28%) of organizations said GenAI Operations (non-IT) 22% 29% has become significantly more important Cybersecurity 21% to their future in just the past two years,¹ 32% putting it in the same upscale neighborhood Sales 20% 31% as cybersecurity, cloud computing, and data 14% management/data governance. Cloud infrastructure cost optimization 22% And why not? GenAI is seen as a critical area of Finance 13% 20% investment during the coming 12 months for HR (human resources) 11% such essential functions as cybersecurity (29%), 25% Currently Use customer experience (27%), and a wide range Supply chain 9% 19% of information management requirements (25%).² Not surprisingly, this makes GenAI Legal 6% 21% Future Potential particularly attractive across the full span of an Purchasing and procurement 5% organization’s value chain, including research, 19% marketing, software development, product Manufacturing 5% development, IT operations, customer service, 12% 8% and finance.³ Other 5% ¹Source: Enterprise Strategy Group, Research Report, 2024 Technology Spending Intentions Survey, February 2024. © 2024 TechTarget, Inc. All Rights Reserved. ²Ibid. ³Source: Enterprise Strategy Group Research Report, Beyond the GenAI Hype: Real-world Investments, Use Cases, and Concerns, August 2023. Back to contents

Driving Productivity and Improved Outcomes With Generative AI-powered Assistants 5 “ GenAI, coupled with other forms of AI such as predictive AI, causal AI, machine learning, and deep learning, is an unarguably transformative force.” GenAI, coupled with other forms of AI such as High-priority GenAI Business Applications predictive AI, causal AI, machine learning, and deep Data insights 31% learning, is an unarguably transformative force. In fact, more than a third (34%) of organizations pointed Chatbots 24% out that “AI is fully embedded in our culture and operations,” while more than a quarter (27%) said they Employee productivity and tasks 23% are “expanding AI more broadly across the business Content creation 23% and/or satisfying more use cases.”4 The catalyst for this rapid uptick in AI adoption and Business processes 22% Decision-making 19% GenAI’s growing importance is being able to find high- leverage opportunities for significant organizational Code generation 19% outcomes (i.e., use cases). In fact, numerous strategic initiatives already have been identified as Content summarization 18% key use cases for GenAI, including data insights (31%), AI assistants/chatbots (24%), employee Product development or innovation 17% productivity, (23%), and content creation (23%).5 Cybersecurity or fraud 15% These use cases are pivotal in demonstrating the economic value of GenAI investments. They provide Data generation 12% organizations with concrete benchmarks for making IT Operations 12% informed decisions and achieving transformative outcomes. Among these opportunities, AI assistants Personalized marketing 11% emerge as a particularly promising area for exploration, evaluation, and adoption. Search functionality 10% © 2024 TechTarget, Inc. All Rights Reserved. 4Source: Enterprise Strategy Group Research Report, Navigating the Evolving AI Infrastructure Landscape, September 2023. 5Source: Enterprise Strategy Group Research Report, Beyond the GenAI Hype: Real-world Investments, Use Cases, and Concerns, August 2023. Back to contents

Driving Productivity and Improved Outcomes With Generative AI-powered Assistants 6 CHAPTER 2: How Goals and Challenges Drive Use Case Decisions © 2024 TechTarget, Inc. All Rights Reserved. Back to contents

Driving Productivity and Improved Outcomes With Generative AI-powered Assistants 7 How Goals and Challenges Drive Use Case Decisions All organizations share a common goal: to find ways to improve their financial results and their long-term competitive position. But achieving that goal and other related outcomes requires new ways of thinking, new processes, and new technology tools. All of these factors, done correctly and with the proper degree of urgency, help organizations make their workforce more productive and efficient. Whether we’re talking about giant multinational conglomerates, midsize manufacturers, or relatively small professional service firms, all employers strive to find ways to deliver more output for the same amount of budget and staff resources. Of course, there are a lot of issues that get in the way. These include limited availability of quality data for models (31%), high implementation costs (30%), difficulty measuring ROI/ business impact (28%), and concerns over data privacy and security (28%).6 Top AI Implementation Challenges 31% 30% 28% 28% 27% 1c+9f= 0c+0g= 8b+2g= 8b+2g= 7b+3g= Limited availability of High costs associated DifÏculty measuring ROI Concerns over data privacy DifÏculty scaling across quality data for models with implementation or business impact and security the organization 27% 27% 26% 25% 24% 7b+3g= 7b+3g= 6b+4g= 5b+5g= 4b+6g= DifÏculty integrating with Lack of regulatory or Ethical considerations and Lack of expertise and talent Lack of understanding or existing systems and processes legal framework their impact on society buy-in from stakeholders © 2024 TechTarget, Inc. All Rights Reserved. 6Ibid. Back to contents

Driving Productivity and Improved Outcomes With Generative AI-powered Assistants 8 One glaring challenge organizations face—and are turning to GenAI to help address—is the technical How can organizations address these and other challenges in order to get the most from their GenAI investments and use skills gap. That skills gap is often discussed in the bright lights of the global cybersecurity skills the technology as strategically and efÏciently as possible? One important step is to identify the right use cases. Specifically, shortage, but it extends equally to other areas organizations should select use cases that: where technology can be a driver of improved productivity, efÏciency, financial results, and sustained competitive advantage. These include data science, analytics, customer experience management, application development, cloud computing, IT operations, Drive important business Act as springboards Demonstrate the Achieve fast sustainability, and much more. outcomes and address key for other related GenAI organization’s ability to use time-to-value. performance indicators. use cases. GenAI responsibly, securely, and with confidence. 27% of organizations said “lack of skilled personnel” Many GenAI use cases are facilitated by increasingly important new tools: AI-powered chatbots, which are rapidly gaining is a top challenge experienced with their momentum among a wide range of users, from data scientists and line-of-business stakeholders to IT professionals and analytics and business intelligence platform.7 cybersecurity teams. In fact, as discussed in the next chapter, AI chatbots themselves represent a valuable GenAI use case. 25% Additionally, it is important for organizations to select the most appropriate AI model. Several models are available, and of organizations noted “lack of expertise and customers are often bound to use the large language models (LLMs) their cloud providers offer. That might not be the right talent” is a top challenge they have encountered choice every time, depending upon the circumstances shaping the use case. Be flexible and keep your options open; make while implementing AI.8 sure the model aligns with your desired business outcome. © 2024 TechTarget, Inc. All Rights Reserved. 7Source: Enterprise Strategy Group, Research Report, Unleashing the Power of AI in Analytics and Business Intelligence, May 2024. 8Source: Enterprise Strategy Group, Research Report, Navigating the Evolving AI Infrastructure Landscape, September 2023 Back to contents

CHAPTER 3: AI Assistants Leap to the Top of the GenAI Use Case Wish List

Driving Productivity and Improved Outcomes With Generative AI-powered Assistants 10 AI Assistants Leap to the Top of the GenAI Use Case Wish List AI assistants represent a high-impact use case because they address so many different organizational goals and challenges, such as productivity enhancements and bridging the AI skills gap. AI assistants support rapid learning curves and easy implementation, leading to fast time-to-value. This quest to reduce time-to-value is essential for organizations. Research from TechTarget’s Enterprise Strategy Group noted that 40% of organizations see value from AI initiatives within one month, and certainly all organizations hope to accelerate this time to value.9 One of the significant benefits of AI assistants as a key GenAI use case is their ability to incorporate enterprise data via different architectural approaches. These approaches, which can provide quick access to relevant data to help reduce hallucinations and improve accuracy, while helping improve contextual awareness, include: Retrieval-augmented generation to help improve the quality and Fine-tuning pretrained language models with organization-specific specificity of responses from AI assistants by incorporating data to improve the natural language understanding of AI assistants relevant information from an organization’s knowledge base. and make them more efÏcient at completing tasks. Multimodal inputs in addition to text, images, video, and voice, to Direct user-uploaded content, like presentations and spreadsheets, enhance the user experience and provide more comprehensive via prompt to enable better AI assistants with data analysis and responses from AI assistants. decision-making. Importantly, incorporating enterprise data can be used without a deep understanding of AI technology, while also promoting cross-functional collaboration among marketing, sales, operations, R&D, legal, compliance, and more. AI assistants are built upon the concept of simplicity, especially in connecting an organization with its own data sources. As a result, 24% of organizations already are using AI assistants/chatbots as a key GenAI use case.10 © 2024 TechTarget, Inc. All Rights Reserved. 9Ibid. Back to contents 10Source: Enterprise Strategy Group Research Report, Beyond the GenAI Hype: Real-world Investments, Use Cases, and Concerns, August 2023.

CHAPTER 4: Specialized AI Assistants To Support the Entire Enterprise

Driving Productivity and Improved Outcomes With Generative AI-powered Assistants 12 Specialized AI Assistants To Support the Entire Enterprise One important and highly leverageable aspect of GenAI assistants is their ability to work equally across and within multiple specialized functions and departments. Specialized AI assistants’ development is simplified and accelerated by using GenAI agents across a wide range of different applications in order to encourage collaboration and data sharing on a cross-functional basis using GenAI assistants. Some of the most promising and high-gain applications where GenAI assistants can make a big difference include: Marketing Legal Sales Human Resources 1. Content creation: Generating blog 1. Contract review: Assisting in the initial 1. Lead qualification: Automating initial 1. Chatbots for first contact: posts, social media content, and review of contracts to identify standard stages of lead qualification using Engaging with job candidates email marketing campaigns. clauses and potential red flags. natural language prompts to assess to answer questions about potential customers’ needs and fit. an organization and its job 2. Customer engagement: Automating 2. Legal research: Automating parts of opportunities. responses on chat platforms, social legal research, locating relevant cases, 2. CRM: Automatically updating CRM/ media, or customer support tickets to and finding statutes quickly. Salesforce records with customer 2. Employee onboarding: Automating increase engagement and response interaction data and insights. paperwork, policy discussions, and time. 3. Document automation: Generating meeting colleagues. legal documents such as 3. Market research: Analyzing nondisclosure agreements, contracts, 3. Sales scripts: Generating customized customer feedback and social and agreements based on predefined sales scripts and communication 3. Automated reporting: HR metrics- media data to identify trends and templates. based on customer profiles and past tracking reports can be generated sentiments. interactions. for faster decision-making. 4. Compliance monitoring: Monitoring 4. SEO optimization: Generating SEO- and alerting about changes in legal and 4. Competitive analysis: Assisting in 4. Compliance document friendly content and suggestions for regulatory frameworks relevant to the compiling and analyzing competitor management: Automating storage keywords and meta tags. business. information to aid strategic planning. and retrieval of relevant documents. 5. Personalization: Creating 5. Risk assessment: Evaluating legal personalized content and risks associated with business recommendations based on user decisions and new ventures. behavior and preferences. © 2024 TechTarget, Inc. All Rights Reserved. Back to contents

CHAPTER 5: Delivering GenAI That Adheres to Principles of Trust, Security, and Responsible Use

Driving Productivity and Improved Outcomes With Generative AI-powered Assistants 14 Delivering GenAI That Adheres to Principles of Trust, Security, and Responsible Use It is critical to make employees, partners, and customers comfortable that GenAI tools are being used in a trustful, secure, and responsible manner. It is not just a matter of compliance, legal, or governance, but it goes to the very heart of an organization’s brand: Can I trust doing business with you and that you are using my data in a responsible manner? This is especially important as employees “experiment” with GenAI usage on their own, given how intuitive these tools often are and how cost-efÏcient they are to acquire and use. It is important for organizations to take necessary steps to avoid “rogue/shadow AI” or AI sprawl, which entails discovering and mitigating LLM usage to ensure conformity with best practices, corporate governance, and other usage requirements. AI assistants need to be designed and deployed with these principles in mind, including strategies to ensure fair and ethical use of machine learning models. Research from Enterprise Strategy Group noted that 54% of organizations feel it is important to establish and follow ethical guidelines, while 51% indicated they seek to incorporate explainability and transparency.¹¹ Naturally, challenges abound: Most organizations (51%) said they struggle with balancing competing goals of accuracy, performance, fairness, and ethics; 42% said they lack diverse and representative data; and 40% reportedly lack clear guidelines.¹² Challenges Organizations Face When Ensuring Fair and Ethical Use of Machine Learning Models 51% 42% 40% 35% 31% 1e+9d= 2d+8e= 0d+0f= 5c+5f= 1c+9f= Balancing competing goals Lack of diverse and Lack of clear guidelines DifÏculty assessing fairness Lack of standards of accuracy, performance, representative data fairness, and ethics © 2024 TechTarget, Inc. All Rights Reserved. 11Source: Enterprise Strategy Group Research Brief, Navigating the AI Ethics Landscape: The Case for Governance, January 2024. Back to contents 12Ibid.

CHAPTER 7: Conclusion

Driving Productivity and Improved Outcomes With Generative AI-powered Assistants 16 Conclusion For organizations to achieve their most important goals in adopting GenAI solutions and integrating them into critical workflows and business processes, organizations need to start with a viable, extensible, and multibenefit use case. GenAI- powered assistants is a great place to start. AI assistants enable organizations not only to achieve key goals such as enhancing user productivity, spurring collaboration, and accelerating innovation, but also to address AI-related challenges such as filling the AI skills gap and reducing risks in security and compliance. © 2024 TechTarget, Inc. All Rights Reserved. Back to contents

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