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The rise of GenAI in the enterprise

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Generative AI is transforming how we work. Accessible. Intuitive. Adaptable. GenAI has the potential to overcome long-standing business challenges and revolutionize how we use and think about technology. 

Companies like OpenAI and Google have developed intuitive GenAI platforms that empower users to generate meaningful responses to hard questions instantly. This rapid usability has captured the imagination of individuals, governments, and business leaders around the world.

Nearly 60% of companies have purchased or plan to buy at least one GenAI tool this year, according to a survey by Writer, an enterprise-focused GenAI company. Business leaders must now figure out how they will use those tools to spread the benefits of AI throughout their organizations.

To unlock the full power of GenAI, enterprises must foster an environment of creativity and experimentation. Encouraging teams to explore novel use cases and take intelligent risks will lead to innovative applications and positive outcomes. But embracing generative AI requires a proactive approach to organizational transformation and the adoption of scalable solutions that meet unique business demands.

While the benefits of GenAI are immense, business leaders must navigate this evolving landscape carefully. Prioritizing security and privacy measures, as well as addressing legal and ethical concerns is essential. Collaborating with experts and professionals is also crucial for successful AI implementation—leveraging their knowledge and experience can ensure solutions align with human values. 

Every organization must develop its own approach to GenAI by focusing on business-specific needs, experimenting, exploring, and striking a balance between the risks and rewards of this transformative tech.

Moving from science projects to industrial-grade applications

GenAI is already transforming how we work, and OpenAI's ChatGPT is the fastest-growing web platform ever.

The key is accessibility.

"[This technology] wasn't accessible to 99% of the population, but now it's accessible to anyone," says Ryan Ries, PhD, Practice Lead for Data, Analytics, and Machine Learning at Mission Cloud, a managed cloud services provider that specializes in AWS.

Advancements in cloud platform technologies have also played a major role in improving accessibility. 

"A few years ago, you'd have to go build half a data center in order to start tinkering," says Cameron Turner, Vice President, Data Science at Kin + Carta. That's no longer the case, as cloud service providers (CSPs) such as Azure, Amazon Web Services, and Google Cloud Platform scale seamlessly from free tier to enterprise-grade.  

This ability to explore and experiment, Turner says, will have a massive impact on innovation.

"Across AI, whether it's computer vision, generative AI, machine learning or recommendation engines, you can start to tinker with these things immediately, then put them together in unique ways, you can create novel applications that generate real revenue."

One of the most exciting elements of GenAI is that it offers a fresh approach to addressing long-standing challenges, such as content generation, personalization, and predictive modeling. For example, Bloomberg has developed its finance-focused BloombergGPT, while Salesforce unveiled Marketing GPT and Commerce GPT offerings.

Research from Gartner suggests that GenAI is a powerful tool for content development. By 2025, GenAI is expected to account for 10% of all data produced, a huge leap from less than 1% in 2021.

Chatbots and content development are only the beginning. GenAI can help financial institutions sort through unstructured data, grade risk levels for loan defaults, or identify potentially fraudulent activities. While in healthcare and insurance, GenAI can streamline claims processing by scoring resubmissions to insurance companies based on predefined criteria. 

In many situations, GenAI tools can provide better responses than humans. Chatbots, for example, can be tailored to focus on business-specific information, such as customer or HR inquiries, supporting the roles of receptionists, customer service agents, or HR specialists. The efficiency gains from GenAI are about making human jobs easier and reducing the amount of labor required.

"Successful generative AI in any human enterprise enhances and enables those humans with new superpowers to do their jobs better, versus trying to replace them," Turner says.

Experiment to find the business value

The sweet spot for enterprises lies in quickly adopting, experimenting with, and learning from GenAI tools without being reckless. 

Executives who approach GenAI with a simple "AI-in, people-out" formula are missing the bigger picture, given the sheer magnitude of the changes this technology will bring, May Habib, Writer's co-founder and CEO, explains. "The change management piece is a fact of life; however, only thirty percent of change management and org transformations succeed."

Organizational transformation is challenging. Organizations must be willing to embrace experimentation and risk-taking to make the most of GenAI. This isn't the time to be timid, Habib says. ‌"Give me the biggest, baddest, most ROI use cases you can bring to the table, and that will be rewarded."

As businesses integrate GenAI into their workflows, they often encounter vast amounts of data that need to be processed and analyzed. Whether AI tools are generating text, images, or other outputs, scalability is essential to handling growing data loads without experiencing performance bottlenecks.

Illustration of 3D cubes forming a structure

While free or open-source tools offer accessibility and affordability, they often lack the robustness and scalability required for enterprise-level deployments. Businesses need custom solutions that can handle the unique demands of their operations, including security and support requirements.

Although GenAI represents a leap forward in AI technology, some of its functions, such as entity extraction and sentiment analysis, are already embedded into organizational tech stacks. Enterprises that have invested in existing working solutions, such as enterprise search and document automation, can accelerate time to value by enhancing these solutions with GenAI.

Despite building on existing technology, GenAI represents new ground for many businesses. Now is the time to explore and experiment to find the most effective use of GenAI for your enterprise.

Legal risks, security flaws, and AI 'hallucinations'

Security and compliance are critical factors in developing GenAI projects for organizations. Whatever AI tool you use, security teams must thoroughly inspect it and audit usage. Understanding this ‌technology and its associated risks makes it possible to protect sensitive data and ensure proactive compliance rather than reacting after a breach or misstep.

Although this rapidly evolving technology could revolutionize businesses, there's still skepticism about GenAI capabilities and worries about security. Security concerns are a significant barrier to business adoption as companies assess the data protection, privacy, and legal ramifications of GenAI.
There are also legal and ethical concerns, including copyright infringement, intellectual property considerations, and the potential for training data that introduces bias and discrimination. There's also the possibility that output is simply wrong. "Hallucinations"—when AI models introduce factual-sounding fiction—have already sparked controversy, including a legal case where a filing cited nonexistent court cases provided to a lawyer by ChatGPT. 
Enterprises are justifiably concerned about risks related to privacy and security. Unauthorized access, data leaks, and targeted attacks are just some of the severe risks to the confidentiality and integrity of GenAI systems. What's more, traditional security features like passwords and firewalls don't work with these tools. Businesses need to supplement existing security features with new technology to remain protected.

Because GenAI is new and rapidly evolving, solving security challenges is an ever-moving target. Writer's Habib recommends business leaders focus on two key aspects of any AI tool when it comes to privacy and security: the current use cases and foundational model use.

Tools should provide users with control over their security risk levels, Habib says. Some tools allow users to inspect the code which generates large language models, data and weights, and offer the option to self-host data. The data used to train any model also should be self-hosted or segregated if a business chooses to host.

"The Writer model is proprietary, but it's open, it's inspectable, and it's auditable," Habib says. "By breaking down the actual stack and making as much of that as possible inspectable and auditable by a company's security team and security policies, we've really been able to build a lot of trust with enterprise CIOs and security teams."

What GenAI brings to the enterprise

GenAI is a transformative force that surpasses human capabilities in tasks such as pattern detection and call center automation.

By leveraging AI capabilities, businesses can create personalized experiences and services—elevating customer satisfaction to unprecedented levels. Advanced algorithms and machine learning enable AI to understand and adapt to individual preferences, delivering tailor-made solutions.

However, bias in AI remains a concern. Inclusive and diverse perspectives are essential in designing AI systems that embrace different viewpoints and experiences. Inclusive AI technologies cater to diverse user needs, fostering better experiences and promoting ethical considerations. The future of AI is inclusive, empowering every individual.
Successful AI implementation is a collaborative effort involving both technology and people, and engaging GenAI experts is essential in preventing errors and ensuring tech solutions perform as they should. Prioritizing the human side of AI taps into a wealth of knowledge and experience, creating AI solutions that align with human values.
For example, a car dealership developed a GenAI system that answered 86% of inbound customer questions without human interaction. The remaining 14%, who required escalation to a human agent, got white-glove service, Turner notes. Before GenAI, the dealership was trying to give white-glove service to everyone, which meant few were getting it. Now the dealership's customer satisfaction is higher. "It's not always the case that human interaction is going to deliver the best human experience," Turner says.  

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