Enterprise AI tools: A new era

Artificial intelligence (AI) continues to rapidly advance, particularly in the realm of generative AI, AI algorithms that create outputs based on data they have been trained on. AI is transitioning from individual consumer excitement to playing a pivotal role as one of the primary and future drivers of competitive advantage in the business landscape. The enterprise use of generative AI is evolving, and parallels can be drawn with other breakthrough technologies like the personal computer, email and the advent of cloud computing. Let’s explore how some organizations are preparing for an AI-driven future.

Enterprise AI evolution

Historically, implementing AI at an enterprise level remained financially challenging, with data usage and security concerns often limiting AI adoption to isolated pockets within organizations. Scott Kaufman, Baker Tilly's chief analytics and data officer, notes, “In addition to the financial challenges driven by the cost of the infrastructure, another challenge was for talent. [Organizations need] people who know how to implement it [AI] and know not only the differences of machine learning (ML), natural language processing (NLP) and Vision but can apply it to a business problem.” However, the landscape is shifting as essential technology stacks mature, mitigating past adoption hurdles. With more accessible infrastructure and tools, combined with growth in practical AI expertise, the foundation is coming together to unlock AI's immense potential.

Significant investments in commercial solutions are propelling the enterprise AI transformation. Microsoft's OpenAI has launched ChatGPT Enterprise, and Google released its suite of Enterprise AI tools. Furthermore, strategic partnerships between industry leaders such as Amazon, NVIDIA and VMWare aim to advance cloud infrastructure and mitigate barriers to AI adoption. These collaborations enable access to enormous data processing capabilities, offer seamless integrations with AI applications and provide the added advantages of native security and performance. Lastly, with accessible open-source options, companies have more flexibility in developing generative AI aligned to their specific strategic needs.

Consumer-to-business transformation

As previously discussed in the World Economic Forum Davos annual meeting article, generative AI serves as the catalyst for the fourth industrial revolution. But what insights can we draw from the history of technological innovation to anticipate AI's future integration into the business world?

The evolution of enterprise AI mirrors transformations seen in past technological breakthroughs. Consider products such as the personal computer, email, and the advent of cloud computing. Initially designed for individual users, these innovations eventually became enterprise solutions. This pattern highlights the role of individual adoption in proving market demand and validating use cases for enterprises. As individual demand stabilizes within the AI landscape, companies naturally expand their offerings or innovate new products and services tailored to meet the needs of enterprise-level expectations.

What now?

With enterprise-level generative AI becoming more accessible, organizations face a pressing need to integrate and responsibly harness its potential to remain competitive in the market. Many companies are already navigating the “crawl, walk, run” methodology with pilot programs. However, while 42% of companies are exploring AI technology [1], on average, only 54% of AI projects make it from pilot to production [2]. Overcome these challenges and prepare for the future of enterprise-level AI by:

• Developing data strategies: Building robust data management practices focused on quality, security and ethics provides the foundation for AI success.
• Identifying AI opportunities: This involves pinpointing areas where AI can provide tangible benefits aligned to strategic goals and address specific pain points.
• Training and user support: Investing in programs to empower and upskill employees and foster responsible AI usage will enable successful adoption.
• Instituting responsible AI principles: When forming ethical AI guardrails, it’s critical to align to your organizational values around transparency, accountability and privacy.

Final thoughts

As the AI ecosystem evolves towards enterprise-level tools, we enter uncharted territory, where the enduring impacts remain elusive. What exciting possibilities await us on the horizon? While only time will reveal the answers, we’re continuing to monitor and provide ongoing insight to our clients.