Key Trends and Insights from G2’s Survey

We often think of artificial intelligence (AI) as a tool for automating tasks or crunching numbers. But the truth is AI is reshaping businesses in ways we couldn’t have imagined. 

According to a new AI adoption survey from G2, nearly 75% of businesses already use multiple AI features in daily operations. A majority of companies – 79% – prioritize AI capabilities in their software selection. 

From chatbots that handle customer inquiries to predictive analytics that forecast market trends, our survey reveals the current state of AI adoption and the unexpected ways AI technologies are transforming businesses. Businesses have to understand these trends and obstacles in order to harness AI’s full potential. 

Survey Methodology

In July and August 2024, G2 conducted an online global survey of professionals who left reviews on G2.com in software categories relevant to AI. The data reflects responses from nearly 130 professionals across the industry from companies of varying sizes. 

AI adoption landscape: widespread, but selective 

The release of ChatGPT threw generative AI into the spotlight in 2022 and sparked a wave of interest and enthusiasm among business leaders. Now that the dust has settled, companies have a more nuanced understanding of AI’s capabilities and limitations. This has resulted in more strategic and measured use of AI technologies. 

We see the shift in our survey findings, which indicate a strong preference for software solutions with built-in AI functionality.

75% of professionals already use generative AI tools for their daily tasks, according to G2’s The State of Generative AI in Workplace survey. The top ten most trafficked AI products in the last year include generative AI components. All these signal a shift toward a maturing AI environment where organizations want more sophisticated, integrated AI solutions.

“Five years ago, AI was still hype because it mostly existed behind-the-scenes. It wasn’t accessible or transparent. Now, vendors are accelerating the development of AI products that can make a real difference – but buyers want to see ROI.”

Bryan Brown
Founder and Chief Analyst, GTM Partners

AI frontrunners: Chatbots and virtual assistants

Businesses are adopting AI tools with a focus on practical applications that deliver immediate value. 

• AI chatbots and virtual assistants lead the race with nearly 70% of organizations using them. This widespread adoption isn’t surprising given their high satisfaction scores – a remarkable 93% for ease of use and setup, according to G2 market report data. These tools offer a blend of simplicity and tangible benefits for many businesses venturing into AI.
• 62% of organizations use intelligent searching to find insights from their unstructured data.
• 43% of companies have deployed predictive analytics tech and personalized recommendation engines to make data-driven decisions. Machine learning (ML) tech and natural language processing (NLP) also shine at 42%.
• Nearly 40% of companies use automated data entry to make their data entry process faster and more accurate.
• Specialized AI technologies, like image recognition software, and fraud detection systems, are used at over a third of companies surveyed. It’s important to note that these specialized tools see widespread adoption in specific industries like finance, where they are critical for operations.

This rapid ROI is a significant trend, according to Matthew Miller, Research Principal for AI, Automation, and Analytics at G2. He notes that across all of G2’s ~2000 categories, the average ROI is closer to 13 months. 

Depth of AI adoption: a gradual journey 

The depth of a company’s integration has been found to align with its operational needs. 

• 75% of businesses have adopted between two and five AI features, which could indicate a cautious but committed strategy. 17% have integrated six to eight AI features across their operations.
• 8% of organizations are timid adopters with only one AI-enabled feature.

Marketing and operations: the fastest adopters 

Not all teams are in the race to embrace AI, but our survey results show marketing and operations currently lead the charge.

• Marketing emerges as the clear frontrunner. 53% of organizations report it as the quickest to adopt AI-enabled software.

“AI is appealing to marketing teams because it’s an agility tool for the entire department. It offers time-saving and insight-gathering support – which is likely why adoption is so high.”

Victoria Blackwell
Research Principal, marketing and advertising software, G2

• Close behind is the operations department, at 47% employing AI for business process optimization and predictive maintenance.
• Customer service takes the third spot at 36%, likely driven by the proliferation of AI-powered chatbots and sentiment analysis tools.
• Sales follow at 23%. AI enhances various aspects of the sales process, like lead scoring and outreach automation.
• Traditional back-office functions like human resources and finance show moderate adoption rates at 15% and 11%, respectively.
• The most surprising finding is IT’s position at the bottom, with only 2% of organizations reporting quick AI adoption.

G2 take

The adoption patterns and G2 data on ease of use, setup, and ROI for these AI technologies indicate that businesses prioritize AI features that integrate easily and deliver concrete results. 

Beyond practicality, companies are strategically using AI to enhance their core functions. The most significant impact is seen in customer-facing and operational areas. 

For businesses at the start of their AI journey, our advice is simple: practicality wins. Focus on AI solutions that solve immediate problems and offer measurable benefits. As your AI maturity grows, explore more complex AI applications.

Say you’re a B2B company owner facing customer service challenges. Try out a small AI chatbot to help answer your customers’ most frequently asked questions. This simple beginning addresses an immediate pain point and reduces the workload on your customer service representatives. 

Key drivers of AI investment: efficiency and innovation

While practicality drives initial AI adoption, broader strategic motivations shape long-term investments. Our data shows companies put AI investments first in areas that directly impact costs, revenue streams, and resource allocation. This has resulted in significant improvements to the bottom line. 

• Operational efficiency drives AI investment, according to 39% of respondents. The dominant focus on efficiency suggests that AI is moving from experimental to essential for core operations. 

“We’re seeing a shift from rule-based heuristic systems to self-learning AI agents. In the future, an operations specialist might work with multiple AI agents, potentially increasing their productivity 10x.”

Vignesh Kumar
AI evangelist

• 27% of respondents cite product innovation and research and development (R&D) as their primary motivation for using AI. This means AI is actively being used to create new products and solutions. 
• 20% of organizations use AI to stay competitive, indicating that it’s a market differentiator for these companies.

Surprisingly, only 13% of organizations note superior customer experience as the primary motivator for AI investment. Yet the high adoption rate of customer-facing AI technologies like chatbots and personalized recommendation engines suggests that improving customer interactions is an indirect driver. This is further supported by customer-facing departments like marketing being the quickest to adopt AI tools.

G2 take

The current focus on operational efficiency and product innovation cuts costs, simplifies processes, and accelerates product development. However, the long-term implications of these investment priorities are even more profound. Concentrating on these areas may very well redefine business models and create new economic opportunities.

However, the hyper-focus on internal improvements, innovation, and near-term gains could be a double-edged sword once enterprise AI adoption peaks. Companies could find themselves in an “efficiency trap” that sees all organizations achieving similar levels of AI-driven optimization. They might get stuck in innovation echo chambers with diminishing competitive advantages. 

To avoid this, forward-thinking companies should see efficiency and innovation as a means to reimagine business models to solve customer-centric problems. Then, they can use AI as a springboard to make entirely new business models that redefine customer relationships and industry boundaries instead of as a crutch that just props up broken creativity. 

The most valued AI features: chatbots, NLP, analytics 

Understanding why companies invest in AI provides context, but you also have to identify which specific AI features deliver the most value. 

• Chatbots and virtual assistants stand out as the most valued AI features for their diverse applications, based on weighted average scores. 
• Closely behind is NLP. Predictive analytics and machine learning algorithms are also highly valued, which underlines their importance in informed decision-making and task automation. 
• Intelligent search slightly lags behind in terms of value, possibly because its benefits often enhance other workflows rather than just standing out on its own.
• Automated data entry also demonstrates significant value, particularly in automating administrative tasks and reducing manual input errors.
• Personalized recommendations, image recognition, and fraud detection rank lower due to their specialized applications in specific sectors like retail, healthcare, and finance.

G2 take

The clear preference for conversational AI and NLP points to a broader trend: the humanization of AI interfaces is redefining AI’s role from a backend tool to a front-line collaborator. AI features that mimic human interaction and thought processes are rapidly becoming the new interface between businesses and their stakeholders. This fundamentally changes how organizations engage with customers, employees, and partners.

This trend has two profound implications for businesses: one, ensuring widespread “AI literacy”–teaching people how to effectively communicate with and use AI systems; two, creating cohesive, multi-functional AI ecosystems within organizations. Consider how conversational interfaces could serve as a frontend for your analytics, search, and specialized AI tools and develop a roadmap.

The goal is integrating AI features strategically into your business operations and culture.

Barriers to AI efficiency: lack of employee awareness 

Here’s a complete breakdown of all the challenges organizations face on their road to successful AI adoption.

• More than a third of organizations note that lack of employee awareness is the biggest barrier to AI adoption. 
• Low data quality and data silos come in as the second most significant challenge, affecting 23% of organizations. Poor data management hinders AI’s ability to deliver accurate insights.
• 21% of respondents count inadequate automation integration as an issue.
• 12% of respondents found a disconnected tech stack impeding their AI efficiency. In fact, 17% of respondents reported that AI features are poorly integrated with their tech stacks.
• 1 out of 10 respondents note a lack of strategic direction blocks their AI adoption.

G2 take

The most significant barrier to AI efficiency comes from our shortcomings. You can’t deploy AI first and train later. Organizations that rush to implement without adequately preparing their workforce with AI skills often find themselves grappling with underutilization, resistance, and missed opportunities. The key is to cultivate an AI-fluent workforce.

“Training employees, both within the company and through product-specific resources, are key. Over half of reviewers of generative AI products don’t use or don’t even know about the features!”

Matthew Miller
Research Principal, AI, Automation and Analytics, G2.

The other significant challenges organizations face relate to technical and operational aspects: data quality, automation, or integrations with the tech stack. The prevalence of these challenges also suggests that many organizations may be underestimating the depth of transformation required for effective AI implementation. 

Implementing AI shifts operations. This involves viewing the entire organization, including data, technology, people, and processes, through the lens of AI. A holistic approach involves:

• Data strategy. Develop a comprehensive data strategy that ensures data quality, accessibility, and governance.
• Technology infrastructure. Build a flexible, scalable tech infrastructure that can support AI integration.
• People development. Invest in ongoing training and development to build AI capabilities across the workforce
• Process reengineering. Rethink and redesign processes to use AI capabilities fully.

This approach accelerates the path to AI proficiency and guarantees that the technology combines capabilities to help people achieve more and get more out of their efforts.

Trust in AI security and privacy: businesses aware of the risks

While organizations grapple with efficiency roadblocks, trust in AI systems’ security and privacy measures comes into play. The data about organizations’ confidence in the security and privacy measures of AI-enabled business software paints an intriguing picture. 

• 67% of respondents express moderate to high confidence in their AI systems’ security measures, but there’s a notable disparity at the extremes. Only 15% of executives feel highly confident, while a combined 17% express low or very low confidence.

This distribution suggests a “confidence gap” in AI security and privacy measures. Many businesses recognize the potential of AI, but they’re also acutely aware of its risks, ranging from bias and other ethical concerns to data privacy and security. So while plenty of standards still need to be improved, advocates also have to do a better job of assuring stakeholders that everything is being done to keep data safe under the workings of AI.

G2 take

Companies need to invest in understanding and addressing the risks most responsible for the trust deficit in AI systems. Try out the following steps.

• Develop a comprehensive view of AI-related risks across domains and use cases. Make sure it covers both risks to your organization and risks your AI usage might pose to others.
• Build a range of options to manage the risks, including technical measures, like enhanced security protocols, and non-technical measures such as policy changes or new approval processes. 
• Create and train your workforce on responsible AI practices and establish a governance structure to oversee the use. 
• Be transparent and open about the way AI is built and the way it’s used with all stakeholders: employees, customers, partners, and vendors. 

Navigating the AI learning curve: a double-edged sword 

As organizations navigate these initial hurdles, they find themselves faced with the AI learning curve. The journey to AI proficiency looks different for every employee.

• 21% of respondents say they achieved proficiency with an AI tool within a month. 
• The majority find themselves on a little longer learning journey. 36% take one to three months to become proficient, followed by a quarter of respondents requiring three to six months. 
• 17% need more than six months to fully grasp AI-enabled features.

AI is also changing workforce development.

• 62% report an increased need for specialized training. This trend underscores the complexity of AI systems and the new competencies required to use them effectively.
• Conversely, 16% of respondents report that AI has reduced the need for some types of training, predominantly observed in engineering, operations, and IT departments. This could indicate that AI is taking over certain technical tasks, thus eliminating the need to teach humans these skills.

This dichotomy makes us infer that while AI is creating new learning demands, it’s simultaneously reducing training needs in certain areas.

G2 take

The extended learning period suggests many AI-enabled features require a shift in work processes or thinking patterns, necessitating time for adaptation. The range of learning times also hints at a potential “proficiency gap.” 

For business leaders, this data highlights the importance of patience and persistent support to employees on their AI adoption journey, as well as the need to foster a base level of AI literacy across all departments. Companies should also rethink their training strategies from traditional, short-term modules to long-term, personalized, and hands-on learning approaches. 

Remember, the ultimate goal extends beyond the mere adoption of AI tools. You have to cultivate an AI-fluent workforce capable of driving and adapting to continuous evolution in tech.

The imperative of AI adoption

AI adoption is no longer optional – it’s essential. But our survey shows it’s only as effective as the people who use it. So prioritize AI literacy among your workforce and focus on what brings your business the most value. Use AI features that solve real problems. Tackle data quality right from the beginning and integrate AI strategically into your operations. Implement reliable security measures and be transparent about AI usage to build trust among employees, customers, and stakeholders. 

Remember, the goal isn’t just adopting AI but making it work for you.

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