Kearney, the global management consulting firm, recently released its Global Artificial Intelligence Assessment 2024 (AIA), highlighting the growing gap between leading artificial intelligence (AI) companies and those that are still lagging behind.
Kearney’s studies reveal that more than three-quarters of organizations (77%) anticipate that Generative AI will improve customer experience and believe it will boost future revenues by making processes more efficient (76%) and aiding in better decision-making.
With these benefits in mind, global companies have increased their AI budgets by 22% over the past year. However, one-third of companies are still in the process of implementing these technologies, and more than half (51%) lack mature AI and analytics capabilities.
“These organizations may have developed strategies and identified use cases for analytics and AI, but they struggle to build, scale, and sustain the necessary capabilities,” the consulting firm reports.
This study, based on surveys of more than 1,000 executives across 12 industries and 25 countries, reveals key insights into how AI and data analytics are becoming differentiators for competitive success in organizations.
How much are companies investing in artificial intelligence?
One of the key trends observed is the growing investment in artificial intelligence, especially in Generative AI (GenAI). Currently, companies allocate 26% of their data and analytics budgets to GenAI, and this figure is expected to increase by 22% over the next three years.
Leading companies in AI and analytics, categorized as “Leaders,” allocate an average of 6% of their annual revenue to these technologies and will increase their investment by 22% annually by 2027, while “Laggards” invest significantly less in comparison.
Which sectors stand out in artificial intelligence adoption?
Among the sectors excelling in AI adoption are telecommunications, media, and technology and consumer and retail, each with 7% and 5% of leading companies, respectively.
In contrast, sectors such as energy and process industries and financial services lag behind, with only 2% of companies considered leaders.
The retail industry is particularly characterized by its customer-centric approach, which has driven greater adoption of GenAI. This sector allocates 32% of its total AI budget to GenAI initiatives for the FY24-27 period, compared to the 27% average observed in other industries.
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In retail, the implementation of AI in areas such as offer personalization and demand forecasting has enabled companies to enhance operational efficiency and respond more quickly and accurately to consumer needs.
What are the main barriers to artificial intelligence adoption?
Despite advances in AI, many companies still face significant barriers to implementation.
According to the study, 45% of organizations cite a lack of technical expertise as one of the main obstacles to adopting GenAI.
Additionally, 51% of the companies participating in the research consider themselves to have immature AI and analytics capabilities. While they may have developed strategies and use cases, they struggle to build and scale the necessary capabilities for effective execution.
This group includes 3% of companies classified as “Laggards,” which have not yet managed to capitalize on AI’s potential to transform their business.
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What do leading companies in artificial intelligence do?
Leading companies in artificial intelligence, representing only 4% of the total sample, have achieved advanced integration of this technology into their operations. These companies have implemented several best practices, including:
- Governance and C-suite support: 72% of AI initiatives in these companies are backed by the C-suite, which is crucial for successful implementation and adoption of this technology at an organizational level.
- Centralized or hybrid operating models: These models enable better collaboration among teams and optimal use of AI resources, avoiding organizational silos.
- Investment in talent: AI leaders invest in training programs across all levels of the organization, fostering a culture of continuous learning that allows better adaptation to technological changes.
Kearney emphasizes that an effective AI strategy must align with business objectives. Companies should clearly define desired outcomes and establish performance metrics to ensure tangible returns on investment.
The implementation of artificial intelligence not only has the potential to automate and improve efficiency but can also drive innovation, growth, and creativity within an organization. AI should be understood not just as an automation tool but as an opportunity to redesign business models and transform entire industries.
