The market for generative artificial intelligence (GenAI) is growing exponentially. GenAI, a concentration of AI primarily responsible for computing human-like content, is continuously becoming more popular across organizations. As noted by Bloomberg, corporate spending on GenAI reached $67 billion in 2023 and is forecasted to exceed $1 trillion by the end of the decade.1
Despite increasing adoption, the Harvard Business Review notes that many companies are hitting roadblocks in GenAI deployment because these tools are an entirely new way to interact with, explore, and analyze data (instead of being repackaged versions of familiar automation processes).2
To make the most of the golden opportunity—and the disruption—triggered by GenAI, business leaders need to carefully consider their approach. This starts with identifying where current data management and analysis output fails to meet demand, selecting an organization's most likely use cases for AI implementation, and finally implementing a GenAI framework capable of increasing benefits and reducing risks.
Common Challenges With Data
Traditional data analysis methods excel at producing actionable output from finite, structured data sets. The exponential rise of both data sources and types has created a new challenge for companies: data overload.
In a cloud-connected, mobile-enabled world, available data is effectively infinite—and often doesn’t follow predictable patterns. Along with structured information, such as spreadsheets and tables, organizations also should collect unstructured data, including consumer sentiment, behavior patterns, survey responses, and social media posts. However, this often results in an overload of data, both in volume and variety.
Organizations also may face challenges with staffing and underdeveloped skills. While leadership teams may recognize that current data analysis methods can’t keep pace, there may also be hesitations on how to effectively leverage new solutions such as GenAI. In many cases, there is a lack of internal knowledge to implement and manage new solutions effectively.
This leads to common questions and concerns: What if data is exposed? What if outcomes are inaccurate? What are the consequences?
Risk & Reward: The Rise of GenAI
What began as a curiosity has become a mainstream technology. As a result, many companies are now scrambling to deploy and integrate GenAI solutions so that they aren’t left behind.
The challenge? These self-learning technologies come with both potential rewards and risks.
On the positive side, GenAI tools make it possible to automate highly repetitive, cumbersome tasks. This type of automation involves relatively low complexity to implement, making it a great starting point. In addition, generative solutions excel at extracting data from multiple sources and then creating meaningful representations of this data, such as charts, tables, or lists.
Large language models (LLMs) combined with natural language processing (NLP) also allow employees to communicate more naturally with AI tools. Rather than following specific guidelines for interaction, users can speak conversationally with these solutions, which helps them get cohesive answers quickly.
Meanwhile, when it comes to potential risks, three concerns are common.
Data Security
In theory, as the amount of available data increases, GenAI can produce more refined results. However, for businesses, this creates a paradox: If data access is too narrow, the output may suffer; but if access is too broad, protected data could be exposed. As a result, the current choice for many organizations is to prolong, limit, or block the use of GenAI tools.
However, organizations won’t be able to avoid these solutions forever.
Output Accuracy
Ask any leading GenAI platform a question, and it’ll likely produce a seemingly well-reasoned and plausible response. The problem? Accuracy is not guaranteed. Depending on the data used and the question posed, AI may deliver entirely confident—and entirely incorrect— answers. GenAI hallucinations refer to instances where a GenAI platform produces information, images, or text not based on factual data or reality. These hallucinations can range from subtle inaccuracies to entirely fabricated content items that can appear plausible.
Platform Complexity
Leadership teams also are concerned about potential platform complexity. It makes sense: As AI uses more data sources, it requires more connections, making transparency and visibility harder to achieve.
GenAI Implementation Best Practices
While deployments differ in scope and scale, keeping a “human in the loop” is a universal best practice that applies to any effective AI implementation.
This is critical for AI maturation because generative tools aren’t like their automated process predecessors. Where traditional tools were designed to work with specific data sources using defined rules, AI solutions are capable of learning over time.
As a result, the output of GenAI tools can steadily improve as the models are exposed to more data sources and create new connections.
This evolving output creates a new operational condition. Instead of automated data collectors, AI tools are comparable to specialized “team members.” Rather than treating GenAI like a static tool, users can leverage the technology for dynamic dialogues. However, the dynamic nature of this technology necessitates increased oversight. While traditional automation processes were effectively “set and forget,” regular review of both input and output data is critical to help GenAI tools stay on track.
SAFE & Sound: GenAI Framework
Forvis Mazars can help organizations make the most of GenAI with the SAFE AI Framework™, focusing on secure, adaptable, factual, and ethical AI deployment.
Organizations aiming to deploy GenAI technology should consider several critical factors.
- Secure: Compliance with data privacy regulations and governance standards is essential. In addition, managing access permissions aids the appropriate use of AI tools.
- Adaptable: Adoption requires seamless integration with existing systems and processes. Effective change management can help smooth the transition to AI tools without disrupting workflows.
- Factual: Factual accuracy hinges on regular quality assurance checks. Reviewing questions, answers, and data sources helps to maintain accuracy and consistency.
- Ethical: Organizations should create safeguards that address fairness, bias, and transparency in AI decision making. Remember that ethical AI is a continual effort. Organizations should revisit and and improve their practices on an ongoing basis to align with appropriate societal values and norms.
In summary, a robust AI implementation framework should encompass security, effective adoption, factual accuracy, and ethical considerations. Prioritizing these aspects can help drive the responsible and effective use of GenAI tools.
Playing It SAFE With Forvis Mazars
GenAI can help your organization take a strategic leap toward operational efficiency, innovation, and competitiveness when properly implemented. It can equip your organization with new tools to streamline processes, enhance decision making, and improve customer experiences—while demonstrating your commitment to agility and relevance in a landscape where AI is a pervasive and evolving influence.
Navigate your GenAI journey with Forvis Mazars. Discover the enormous potential of AI for your organization, while following best practices for security, adaptability, factual integrity, and ethics. Play it SAFE with Forvis Mazars and execute your organization’s digital transformation precisely and with foresight. Connect with us today to get started.