Enterprise AI Adoption: The Rapid Shift In Attitudes Towards GAI

Generative AI (GAI) is the most talked about technology in recent years and has broken all records in terms of its widespread adoption in both the consumer and enterprise domains. Since its mainstream debut in November 2022 the attitude towards Generative AI has changed significantly. On the consumer side we have gone from wide curiosity driven adoption to solid reliance on GenAI as people use it to augment their day to day activities. On the enterprise side the shift has been much more radical. 

In this blog we will discuss how enterprise AI adoption has shifted and the underlying factors driving this change. We will also cover key accelerants that enterprises can leverage to minimize their risks while maximizing their returns from their GenAI programs.

The Evolution of Enterprise Attitudes Towards Generative AI

Early Resistance to GAI

The Generative AI Genie has left the proverbial bottle! According to the Salesforce State of IT report 86% of IT leaders expect Generative AI to soon play a prominent role at their organizations, 57% believe Generative AI is a “game changer”, 67% of IT leaders surveyed said they have prioritized Generative AI for their business within the next 18 months, and 33% said it was a top priority. The C-Suite has an even more aggressive perspective.  In Accenture’s recent Pulse of Change research 2,300 C-suite leaders nearly all (97%) believe generative AI will be transformative and a game-changer for their company and industry. Right now, 44% are making big investments.

Enterprise AI adoption was initially quite slow as business leaders immediately recognized the significant security, privacy, and legal risks posed by GenAI. Many organizations responded by blocking employee access to external AIs and as a result employees moved to utilizing GenAI on their personal devices. Fast forward a few months and we are seeing an incredible reversal of direction. It has become clear in recent months that those that embraced GenAI early are already seeing large increases in productivity and those that are blocking its use are likely to fall behind. In the last six months, boardroom conversations have gone from “How effectively can you block access” to “How fast can you enable adoption?”

The Salesforce State of IT report outlines some of the key reasons why Enterprise AI adoption was slow initially. According to that report, nearly two in three CISOs (65%) said they can’t justify implementing generative AI yet because: 

• Organizations have no tools that offer visibility into GenAI usage or guardrails for safe usage
• Generative AI will introduce new security threats to their data
• Their employees lack the skills to use it successfully 
• Generative AI can’t yet integrate with their security stack
• Their organization lacks a unified data strategy 

Gradual Enterprise AI Adoption, Acceptance and Exploration

Over the last few months, however, enterprise GenAI adoption has greatly accelerated. There are three primary drivers for this shift:

• First, the productivity gains are enormous and undeniable
• Second, GenAI brings a distinct competitive advantage
• Third, GenAI and AI in general is now a club to which business leaders want to belong!

Early adopters of Generative AI, such as Expedia who was lightning fast in introducing AI as part of their travel services or GitHub who introduced Copilot to enable AI assisted programming, have created a sense of comfort with the technology and also paved the way for broader acceptance.

In the last few months every organization has begun the process of exploring ways in which to include GenAI into their business both internally for their employees as well as externally for their customers.

The Gen AI Genie Unleashed

We are all witnessing one of the greatest paradigm shifts in our time. The AI movement is in full swing and just like the discovery of fire, the invention of the wheel and more recently, the Internet, we will look back on this time as a decisive milestone in human evolution.

In the business world, the GenAI Genie has been unleashed across enterprises large and small. And just like Genies don’t go back into bottles, there is no turning back. Whether an organization has an official policy or not, employees are using it. If it is not allowed at work, then it is being used on personal devices for work purposes. Even if organizations themselves are not building AI powered apps, they are using SaaS applications that have AI built into them. Even if it is blocked internally, employees have access to AI in hundreds of other ways.

AI as a general  technology has been around for over a decade, in academic institutions and the research labs of large enterprises. Pivotal supporting innovation in the areas of high-performance computing, special search technologies such as vector databases, Large Language Models (LLMs) that make AI accessible to day-to-day people who are not scientists, have all come together to create this moment where AI has become mainstream.

Key Factors Driving the Rapid Enterprise Adoption Of Generative AI

Internal Productivity

A recent study from Accenture finds that enterprises that are not sanctioning the use of AI are seeing approximately 40% increases in employee productivity. An informal Portal26 survey found that the average employee utilizes GenAI in at least four ways to assist them in their job – the leading applications are

• To generate a starting point for presentations or documents
• To generate a starting point for code or to review code
• To replace traditional web research
• To assist in laborious tasks such as generating letters or structured responses

Competitive Pressure

Competitive pressure is a very strong driver of enterprise AI adoption, both in terms of wanting to be perceived as a leader and also because of the very real uplift that products and services are seeing with the inclusion of AI, especially generative AI.

An interesting measure of how competitive pressure is fueling enterprise GenAI adoption, is the number of industry events where AI has dominated the agenda. All major security conferences including RSAC, Google Next, and most recently Dreamforce saw a clear domination of the AI agenda and correspondingly saw record turnout of people interested in learning about AI powered solutions as well as effective ways to utilize them. Analysts such as Gartner, Forrester and IDC have begun rapidly publishing research and analysis that further fuels adoption. 

Changing Market Dynamics

AI has significantly altered market dynamics in two specific ways:

• First, AI actually improves the user experience  and so customers are favoring AI powered products and services
• Second, investors are providing significantly higher valuations to AI powered businesses both in private and public markets

When business  economics strongly support adoption, then what would otherwise be a technological innovation, becomes a movement of grand proportions. This is what we are witnessing with AI and GenAI.

Embracing Enterprise AI Adoption

Overcoming Challenges

The good news is that in the last few months, innovators have gone to work and created solutions that enable safe and effective adoption of generative AI technology in the enterprise. There are six key areas of focus that have enabled business leaders to overcome their initial resistance and jump on to the GenAI train. These are:

1. Visibility – Invest in tools that enable you to know what AIs are being utilized across the organization, who is using them, and for what purpose. This will give valuable insights into effective ways to build AI into the business
2. Policy – Create and enforce acceptable use policies. Ensure that they are granular enough to be useful in a variety of use cases and scenarios
3. Risk Management – Catalog AI specific risks and determine appropriate mitigation strategies
4. Data Privacy – Invest in data security and privacy controls to ensure that employees do not intentionally or inadvertently exfiltrate sensitive data; and also ensure that internal AIs are trained in accordance with data security and privacy rules
5. Education – Invest in training employees on both how to use GenAI and also on the policies themselves
6. Model quality – Utilize models that have been tested by independent third parties and certified as bias-free, soc2 certified, and compliant with regulations pertinent to your company