The state of enterprise AI adoption in 2024

Thumbnail for blog post on enterprise artificial intelligence adoption

In the race to adopt artificial intelligence (AI) in enterprises, don’t get left behind. Two-thirds of respondents in a Box-sponsored IDC survey have already deployed generative AI in some areas (or broadly) at their organizations. With AI, you can tap into the value of your unstructured data to boost your team’s productivity, creativity, and decision-making at scale. 

Let’s review how the landscape of enterprise AI adoption continues to evolve in 2024, building upon the momentum gained last year. 

Where you’ll see increased adoption of AI in enterprises

Insider Intelligence forecasts the initial adoption curve of generative AI is climbing faster than the adoption rate of smartphones and tablets. Enterprise adoption of AI is driving these growth trends as organizations look for technologies to automate business processes, boost employee productivity, and reduce costs. As an IBM study points out, there’s an increasing amount of AI embedded into off-the-shelf business applications, making this accessibility a top driver in AI adoption, too.

Insider Intelligence infographic about the adoption of artificial intelligence in comparison to smartphones and tablets

In terms of technology, advancements in machine learning algorithms, increased computing power, and the availability of large datasets have propelled the use of AI applications in all industries. But you’ll see more cases of successful AI adoption in areas with a need for data processing, pattern recognition, automation, and decision-making based on complex and large datasets. 

Keep in mind that 90% of your data is unstructured. With AI, you’re able to analyze, organize, and extract value from unstructured data at scale. Plus, you can identify hidden patterns and generate content from scratch.

Top use cases of AI enterprise adoption

According to McKinsey, the most-reported use cases of generative AI come from three areas.

Top uses cases of generative AI adoption in enterprises

The rise of AI in marketing and sales

AI empowers marketers and sales professionals to speed up work by automating routine tasks such as content creation, campaign personalization, and lead prioritization.

Marketing and sales teams leverage AI capabilities specifically to:

  • Conduct research
  • Create social media posts
  • Write product descriptions
  • Fix spelling in sales decks 
  • Respond to emails 
  • Condense sales call transcripts
  • Refine messaging for campaigns 

Marketers can measure the impact of enterprise AI adoption by looking at how much time teams from Hubspot found that the average employee saves about 12.5 hours per week by getting AI help to complete tasks.

AI in product and service development 

From concept to market, AI in product and service engineering automates repetitive tasks, identifies trends in customer needs, and even assists in the creation of new product designs. Think of predictive modeling, demand forecasting, and personalized product recommendations. 

Plus, AI-powered simulations and virtual prototyping accelerate design iterations and minimize costly errors. PwC found that 14% of enterprises with advanced adoption of AI and machine learning in product development earn more than 30% of their revenues from fully digital products or services.

The growing role of AI in customer service operations 

Improving customer experience has been a key focus of enterprise AI investments. With AI, you enable more responsive and personalized customer service. In fact, nine of out 10 service professionals currently using generative AI confirm that it helps them serve their customers faster.

A classic example comes from AI-powered chatbots and virtual assistants, which provide real-time support, address queries, and resolve issues efficiently. These AI systems comprehend customer inquiries, adapt to various communication styles, and continuously improve their responses over time.

By sifting through vast datasets to uncover hidden insights, AI-enabled analytics allow customer service teams to:

  • Anticipate customer preferences and tailor their interactions accordingly 
  • Forecast service trends and anomalies to stay ahead of evolving customer expectations and industry shifts

AI adoption challenges for businesses

Even though enterprise AI adoption is growing at unprecedented levels, there’s still a long way to go. Here are the common obstacles.

Training and upskilling workers on AI technologies

For a successful AI adoption, you need a team with AI skills. It doesn’t necessarily mean you need to go out recruiting AI talent — a challenge on its own. You might just need to train and upskill your current workforce so they use this technology to its full potential. 

There are two common scenarios.

  • Your team is eager to use AI but they don’t know where to start. In this case, start with a comprehensive AI readiness assessment to identify specific business cases that AI addresses. Another key step is to foster a culture of continuous learning through workshops and training sessions for your team.
  • Your team is resistant to AI adoption. Automating and simplifying work with AI requires a culture change, and you’ll have to deal with employees who want to stick to what they know or are even worried about being replaced by AI tools. As an Ericsson study reveals, 87% of respondents faced more people/culture challenges than tech and organizational hurdles in their AI adoption journey. Consider these solutions:
    • Focus on transparent communication about the advantages of AI
    • Start small and scale your AI project gradually
    • Involve employees in the decision-making process
    • Provide reskilling opportunities for your team

Ethical, compliance, and security concerns with AI

At the enterprise level, ethical considerations about data privacy and bias in algorithms inform the decision-making about AI adoption, especially given the need to navigate a complex landscape of regulations and compliance standards.

IDC found that the top roadblock to AI enterprise adoption is the concern about releasing proprietary content in the large language models (LLMs) of generative AI providers. Another obstacle is the lack of clarity about intellectual property rights around the content used to train LLMs.

Challenges in AI adoption

Overcome those concerns by reviewing the AI principles of your technology provider to ensure they align with business needs. 

Build trust in AI by partnering with providers that give you:

  • Full control over your own data and processes
  • Ability to enable and disable the use of AI
  • A clear understanding of how the AI system works 
  • Comprehensive security controls, including encryption and permission policies

Lack of strategy and understanding

Many enterprises struggle with formulating a comprehensive AI strategy that aligns with their business objectives. Without a clear roadmap, you may invest in AI technologies without fully understanding how to integrate them into workflows or measure their ROI. This can lead to inefficiencies and underutilization of resources. 

Some questions to ask:

  • How will you integrate AI solutions with your current tech stack and existing workflows?
  • What do you want to achieve with AI adoption?
  • Which use cases will you prioritize?
  • When selecting a vendor, what criteria will guide your decision?
  • What AI skills will be needed and how will you get your team ready?
  • How will you measure the success of your AI adoption?

Read our guide on how to build a successful AI strategy.

Jumpstart your enterprise AI adoption with Box

Box accelerates the adoption of AI in enterprises with a new suite of capabilities that natively integrates advanced AI models into the Content Cloud

With Box AI, you tap into the wealth of knowledge within your enterprise content to:

  • Summarize information(such as lengthy reports or call transcripts) for improved comprehension
  • Retrieve information from your documents — for example, locating clauses within contracts
  • Generate content from scratch, whether it’s a personalized email or meeting agenda
  • Uncover insights, patterns, and trends that could open up new market opportunities 

You maintain full control over your own data and workflows, and we don’t train AI models on your content without your consent. Plus, security and compliance are built into our products from the ground up, because maintaining your trust is our priority.

Let’s connect and discuss how to power your enterprise AI adoption with Box.

Banner inviting readers to accelerate their enterprise artificial intelligence adoption with Box

While we maintain our steadfast commitment to offering products and services with best-in-class privacy, security, and compliance, the information provided in this blog post is not intended to constitute legal advice. We strongly encourage prospective and current customers to perform their own due diligence when assessing compliance with applicable laws.

Free 14-day trial.
No risk.

Box free trial includes native e‑signatures, let's you securely manage, share and access your content from anywhere.

Try for free