AI is in every board room conversation, and everywhere the enterprise leaders are feeling pressure to correct it. But as the adoption increases, ask questions.
Which issues are providing real results? How do organizations balance speed with governance? Are most buildings from the beginning, buying shelf, or looking for a middle way? And most importantly, what is practically working for global businesses?
Kore.ai “AI leaders’ practical insight – 2025” reports the noise.
With the insights of more than 1000+ enterprise leaders in industries and regions, a real picture of it has been painted that in 2025, not only in the headlines, but also on the ground, AI’s experiences and adoption.
In this blog, how much will you look for in the global AI leaders – priorities, challenges, investment and capabilities strategies that create the next phase of Enterprise A.
Let’s dive into 👇
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In March 2025, a survey was conducted by Paradox and in collaboration with Core Doti, ‘AI leaders’ practical insights – 2025′ shows how enterprise leaders are adopting AI, tackling challenges, investing in budget, and gaining new shapes to make business.
The survey collected insights from more than 1000 senior business and technology leaders in 12 countries, including the United States, the United Kingdom, Germany, the United Arab Emirates, India, Singapore, the Philippines, Japan, Korea, Australia, and New Zealand.Jes
How deep does AI run in businesses?
Enterprises are experimenting with AI in several active areas, but often in cellus. What is lost is a compatible strategy to enhance the effects of AI throughout the business.
According to the Kore.Ai survey, 71 % of the enterprise leaders have said that their organizations are actively using AI in multiple departments, such as customer support, IT, HR, finance, operations and marketing. 
This increase in adoption is that by 2026, more than 80 % of businesses have deployed generative AI applications to production, which is less than 5 % in early 2023.
Survey shows that AI automation uses IT support, customer service, and marketing lead cases. Products, HR, Finance, Operations, and Engineering reflect strong epithet, while works like admin, purchase, legal and sale are in the early or experimental stages.
Regional, North America (79 %), Western Europe (70 %), and India (87 %) lead to the adoption of AI, which are driven by strong executive support. On the contrary, parts of the APAC, especially Japan (56 %), South Korea (64 %), and Southeast Asia (59 %), reflect a slow pace, reflecting more cautious leadership.
With the adoption of AI around the world, the next question is clear: which issues of use are driving leaders to double the AI?
What is the fuel to the AI’s agenda in the C suit?
Across the board rooms, the AI ​​conversation is moving from ‘why’. Research has highlighted that most leaders are focusing on use issues today that provide solid business costs.
1. 44 % are applying AI for process automation, which covers areas such as compliance, risk management, and workflow correction.
2. 31 % of the organizations are using AI to enhance the workplace production capacity, from automatically creating tasks and accelerating insights, to the creation of high -speed materials and abstract.
3. 24 % are deploying AI to enhance customer service and self -service experiences.
Technology (77 %) and financial services (72 %) are doubled on AI for insights and analytics, which are treating the data as a competitive edge. Retail (77 %), business services (75 %), and healthcare (69 %) are focused on AI -powered consumers. Meanwhile, use of search and information such as use of use, technology (64 %), finance (66 %), retail (71 %), and business services (62 %) are obtained.
The survey also found that AI’s deployment leads to meaningful effects from the pilot, usually taking time to be solid for 7 to 12 months. This makes Microsoft echoes that most AI projects take up to 12 months to develop business effects.
Enterprise AI Challenges: Why is scaling difficult?
The majority of businesses are already watching the initial win with the AI. In fact, 93 % of leaders have reported that their pilot projects met or exceeded expectations. However, a new set of obstacles has been introduced from successful pilots to the organization’s vast AI transformation.
Research shows that businesses face some challenges that are slowing down their pace. Some of these are challenges:
1. AI Talent Gap – This is the most important challenge of today’s enterprises. Ben & Company also pointed out that 44 % of executives feel that the lack of home skills is slow to adopt AI.
2. High LLM Cost – In reference to 42 % respondents, the ongoing token costs for LLM also emerged as an important challenge to scale AI in the study. This shows that costs based on the scale of organizations become more relevant.
3. 41 % of the decision makers in the data security and confidence-surveys have reported that they face the challenge of safety of the proprietary and first party data.
In view of these challenges, many organizations are considering their approach to adopting AI: Should they prepare a custom solution at home, or is it more effective? 👇 👇
Buy or build? Strategic Trade Office Creating Enterprise AI
Let’s dive the fascinating story revealed by Corey. 
The survey shows that businesses are clearly supportive of speed and complexity. Only 28 % of the organizations said they would prefer to prepare their AI solutions from the ground, while the remaining 72 % of the purchase -led strategies are choosing. This includes a solution for deployment (31 %), customized third -party offer (25 %), or the best solution (16 %).
This trend is in accordance with the Mac Cancity report, which states that the AI ​​strategy that connects vendor tools with internal capabilities enables businesses to accelerate AI 1.5X as compared to fully customized solution makers.
Co -selection of shopkeepers: price higher than
The choice of AI vendor is no longer a decision to purchase, but is a decision on the making or break. Where the right partner can accelerate the results and scale innovation, while the wrongdoing can introduce any friction, delay and technical loan.
According to the research, the decision makers prefer ease of integration with output quality and accuracy (45 %), AI solution performance and performance (34 %), domain -related skills (28 %), and existing systems (28 %). 
In particular, vendor pricing (24 %) is very low in the list. These priorities reflect a solid market where leaders are looking for long -term partners who can be manufactured with their needs, understand their industry, and provide the measurement price on a scale.
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What are the toughly earned lessons from past AI projects?
Since the enterprise goes beyond AI pilots, leaders are asking tough questions: What does it really matter make? Where are we ready? And what can we do better? This research highlights important areas that are repeatedly exposed as the backbone of successful AI’s deployment.
1. Data quality is required
More than 50 % of respondents presented data quality as an area of ​​serious improvement in future AI projects. However, the effect of AI is just as strong as it learns data.
Industries such as retail, manufacturing, and technology are doubled on first -party data, which recognizes its role in enabling discrimination, AI -powered experiences. Meanwhile, regular sectors such as healthcare, financial services, government, and business services are focusing more on securely handling data and third -party data.
2. Security and data privacy is non -dialogue
In enterprise operations, data security and privacy are higher than technical boxes, with the AI ​​system. They are accessories of confidence and compliance. About 40 % of leaders view security and data privacy as the first area to strengthen the next AI measures.
3. Tech infrastructure is a strategic viable
Many organizations, in the survey, admit that their current tech stacks are not designed to support the enterprise Grade AI. The burden of AI work demands important computing power, expansion pipelines and strong model governance.
4. AI talent is a makeup or brake for AI success
Core. Research shows that almost two -thirds of the two -thirds acknowledged that they need strong AI’s skills, but they are divided on whether new abilities are to be hired or existing teams are advanced. The number indicates a wider talent cranch that affects each scale.
“AI’s success partnership depends on the construction of data and business teams and data literate culture.” – Wangward Chief Data Officer.
2025 and where is the investment beyond?
When asked, “How do you guess in your AI budget in the next three years?” A notable 90 % of the leaders say their AI’s budget will increase, with 75 % plan to allocate more than half of their IT expenditures in AI measures. 
The above trend has been endorsed by IBM study, which states that by early 2025, AI expenditures increased from 52 % to 89 % in the last three years.
The report also highlights specific budget specimens related to the industry. For example, the financial services and technology sectors are leading the charge with more than 50 % of their tech budget towards AI technology. Business services and health care are closely followed with a allocated amount, while manufacturing (25 %) is more conservative in its AI costs.
Final views: Enterprise AI story is just starting
If one thing is clear from this research, it is that AI is becoming a fundamental part of organizations working, competing and increasing.
A number of storytelling has been told that the leaders are moving beyond the pilots, the budget is rapidly scaling, and the AI ​​is feeling its presence in the departments, from customer support to marketing from finance. Talent strategies are being developed, infrastructure is being modernized, and the data is eventually getting the attention they deserve.
But the journey is far from over.
Research has also highlighted that when excitement is high, pressing expectations and costs, protecting data and responsibility. Leaders now make decisions, such as what to make, what to buy, where to invest, and how to measure success, will create AI speed for years to come.
This blog just scratches the level. The full cover of AI leaders. The Practical Insight -2025 report has divered the depth of benchmarks, strategies and lessons that today’s decision-maker AI is using to convert AI’s capacity to business performance.
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