AI Adoption in 2024 and Beyond: Progress and Challenges

June 26, 2024

Growth will be the talk of the town across industries in 2024. Gartner finds that 62% of Chief Executive Officers (CEOs) have chosen ‘Growth’ as their top business priority this year, which is at its highest since 2014. The newly identified focus on growth stems from Artificial intelligence (AI) being viewed as the primary driver of the next business shift after digital transformation.

Despite the buzz around AI, many organizations are still in the early stages of their digital journeys. 53% of CEOs report that their digital transformations are either just beginning or less than halfway complete, making technological change their second-highest priority.

These CEOs are clearly not deterred by any negative prospects around AI, as over a third of them agreed that the benefits of AI to their business outweigh the risks. It will be up to Chief Information Officers (CIOs), Chief Technology Officers (CTOs) and Chief Data Officers (CDOs) to help CEOs unlock the value of GenAI for the organization, as this is one of those rare trends which will not follow a top-down approach.

Therefore, it’s safe to say that regardless of their current AI adoption status, most organizations (92%) believe that AI will improve their businesses in 2024 and beyond.

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Artificial Intelligence processor unit. Powerful Quantum AI component on PCB motherboard with data transfers.

What are the Technological Advancements Driving This Accelerated AI Adoption?

AI Algorithms and Models

Continuous improvements in AI algorithms and models enhance their accuracy and efficiency. Advances in deep learning, reinforcement learning and transfer learning enable AI systems to perform complex tasks with better precision.

Computational Power and Cloud-based Services

The increase in computational power and the proliferation of cloud-based AI services make AI more accessible and scalable. Organizations can leverage cloud platforms to deploy AI solutions without significant upfront investments in hardware.

Natural Language Processing (NLP) and Computer Vision

NLP advancements allow AI to understand and generate human language more effectively, driving applications in customer service, content creation and more. Similarly, improvements in computer vision enable AI to interpret visual data, enhancing applications in surveillance, quality control and autonomous vehicles.

Big Data and Improved Analytics

The role of big data in AI cannot be overstated. Improved data analytics allow organizations to extract actionable insights from vast datasets, driving informed decision-making and strategic planning.

What are the Challenges and Barriers to AI-Adoption?

Although 80% of organizations believe their data is AI-ready, almost all organizations experience challenges during AI implementation, revealing a significant gap between perceived readiness and on ground reality.

Here are some areas of concern, to name a few.

Data Privacy and Security Concerns

Data privacy and security remain top concerns as AI systems handle increasing amounts of sensitive information. Ensuring robust data protection measures and compliance with regulations is critical to maintaining trust and integrity in AI applications.

In fact, 78% of organizations cite data security as a primary challenge in their AI initiatives, and 62% report that compliance with data protection regulations significantly slows down their AI deployment efforts.

Talent Gap

The complexity of AI models has increased the need for skilled personnel for development, maintenance and troubleshooting-related duties. However, 69% of organizations report a shortage of qualified AI professionals, further hampering successful AI implementation. This skills gap, combined with the need for continuous monitoring and updating of AI systems, adds another layer of difficulty for organizations striving to stay competitive.

Regulatory and Compliance Hurdles

Governments worldwide are promoting AI adoption through various policies and initiatives. However, ensuring compliance with the complex and constantly evolving regulations and standards related to AI is expected to add a layer of complexity due to the infancy stage of AI technology.

Data Volume

The high volume of data poses additional challenges to data management strategies. Notably, 64% of organizations manage at least 1 PB of data, and 41% manage at least 500 PB. This massive volume of data complicates the process of effectively leveraging AI technologies, often leading to difficulties in data integration, quality control and real-time processing.

Data Quality

The sheer volume of data opens a new can of worms about storing and managing it. Poor data quality not only burdens data storage but may also compromise the validity of AI-driven insights based on outdated and irrelevant information. Additionally, many organizational storage architectures may not be ideal for AI use. For instance, if your organizational data is spread across the cloud, self-hosted storage and physical documents that means you’re probably in a fragmented data ecosystem. Such fragmentation complicates accessibility for both people and AI, further exacerbating the challenges in AI implementation.

AI algorithms need access to all relevant information to build appropriate learning models, which can be challenging when data is stored in separate, disconnected locations. This separation makes it difficult to determine what information is available, how old the data is and its integrity, all of which are crucial for producing high-quality outputs.

Future Outlook

AI is undoubtedly capturing the attention of CEOs, with 34% identifying it as the top theme for the next business transformation, surpassing even operational efficiency, which stands at 9% (Gartner). In the next five years, the adoption of AI is expected to surge, with more industries integrating AI technologies into their workflows. This will likely result in smarter applications, improved efficiencies and innovative solutions across various sectors.

The relationship between humans and AI will continue to evolve, positioning AI as a partner rather than just a tool. Emphasizing AI trust, risk and security management (AI TRiSM) is crucial to fostering this relationship. Transparency, control and explainability of AI systems—understanding how AI models are trained, what data is processed and how solutions function—will be paramount in building trust and ensuring AI’s positive impact.

Sustainable practices will continue to be intertwined with digital maturity, strengthening organizational resilience. By 2026, 75% of CIOs are expected to be responsible for sustainable technology outcomes, and by 2025, one in four IT leaders will have financial compensation linked, at least in part, to achieving these sustainable outcomes.

Implementing AI in an augmented, connected workforce will notably impact the digital employee experience. Although the learning curve may be steep, AI will help streamline processes, offer precise predictions and expedite digital employee onboarding.

Organizations are expected to proceed with caution concerning AI integration. While there is strong belief in AI’s potential, its implementation comes with significant challenges, such as managing large volumes of data, ensuring data privacy and security and complying with regulations.


AI adoption is set for significant growth in 2024 and beyond, driven by technological advancements and an ever-increasing integration into various sectors. We have a lot of work ahead of us to successfully harness AI’s full potential. As AI technology improves and its use-case landscape continues to evolve, staying informed and prepared will be key to leveraging AI for transformative business outcomes.

If you plan to introduce AI integration within your organization, the easiest first step would be to get your data ready, regardless of your organization’s stage of digital maturity. Consider our Intelligent Information Management Solutions to ensure successful AI integration and to future-proof your business in the rapidly advancing AI landscape. For example, if your data is fragmented, you could start streamlining it with business process automation solutions that will bring your data one step closer to being AI-ready. Or, if you’re unsure if your existing internal processes are ready for AI implementation, you could start with process enhancement services that will improve your current processes, enhance content visibility and help integrate data between your business-critical systems.

By taking these steps, you’ll position your organization to adopt AI seamlessly and thrive in an AI-driven future. So, start now and witness how AI can revolutionize the way you do business, paving the way for unprecedented growth and success!

Aneri Rathod
MIT Marketing and Communications Manager, Brand and Content

Aneri is a Marketing and Communication Manager at Konica Minolta. In her role, Aneri strategizes, manages and implements branding, campaign and content development initiatives to support the MIT business segments across North America. With a decade of experience, including five years in the tech industry, she excels in building corporate relationships and creating impactful written, event, and campaign content. Aneri’s strategies directly address the target audience, reflecting her extensive market understanding.