5 strategies that separate artificial intelligence leaders from 92 % are still stuck in the experimental mode

With artificial intelligence from experimentation to the real world’s publishing processes, companies define best practices for what is already working on a large scale.

Multiple studies of various sellers have shown the basic challenges. according to Modern report From Vellum, only 25 % of artificial intelligence institutions have been published with fewer known effects. A Report from Deloitette Similar challenges were found with organizations struggling with expansion and risk management issues.
A new study of ToneOutside this week, it provides an analysis based on data for how to successfully implement the leading companies from artificial intelligence through their institutions. the “The first place guide for expanding the scope of artificial intelligenceThe report relies on a survey that included 2000 C-SUITE managers and data science from nearly 2000 international companies with revenues exceeding one billion dollars. The results reveal a large gap between the aspirations of artificial intelligence and implementation.

The results draw a realistic picture: only 8 % of companies qualify as the real “front managers” who have succeeded in limiting multiple initiatives of artificial intelligence, while 92 % struggle to progress beyond experimental applications.

For information technology leaders in institutions who move in the implementation of artificial intelligence, the report provides important visions on what separates the scaling of a successful artificial intelligence from suspended initiatives, highlighting the importance of strategic bets and the development of talent and data infrastructure.

Here are five main meals for the leaders of the Information Technology Corporation from Accenture Research.

1. Talent maturity exceeds investment as the main scaling factor

While many institutions focus mainly on technological investment, Acceenure’s research reveals that the development of talent is in the most important reality in implementing successful artificial intelligence.

“We have found that the highest achievement factor was not an investment but rather the maturity of talents,” said Senthil Ramani, Data and AI in Accenture, for Venturebeat. “The two centers in the forefront had four times larger talents compared to other groups, which leads to the implementation of talent strategies more effectively and directing talent spending to the highest value of uses.”

The report shows that the first positions distinguish themselves through strategies that focus on people. They focus four times on cultural adaptation more than other companies, focusing on the alignment of talent three times and implementing organized training programs at a rate of twice the rate of competitors.

IT Commander Work componentDeveloping a comprehensive strategy for talents that deal with both technical skills and cultural adaptation. The establishment of the Amnesty International Central Excellence Center-the report shows that 57 % of the front centers use this model compared to only 16 % of the fast followers.

2. The data infrastructure makes or breaking efforts to limit artificial intelligence

Perhaps the most important barrier to implementing artificial intelligence at the institution level is insufficient for data. According to the report, 70 % of companies included in the poll acknowledged the need for strong data when trying to expand the scope of artificial intelligence.

Ramani said: “The biggest challenge for most companies that try to expand the scope of artificial intelligence is to develop the infrastructure of the correct data,” Ramani said. “97 % of the first place has developed three or more data from the possibilities of artificial intelligence of Gen AI, compared to only 5 % of companies that try artificial intelligence.”

These basic capabilities include advanced data management techniques such as the generation of nutrition (RAG) (used by 17 % of the first place compared to 1 % of fast followers) and knowledge fees (26 % compared to 3 %), in addition to using various data through zero parties, second end, third party.

IT Commander Work component: A comprehensive evaluation of the data preparation that explicitly focuses on the requirements for implementing artificial intelligence. Giving priority to construction capabilities to deal with unsheather data as well as organized data and develop a strategy to integrate implicit organizational knowledge.

3. Strategic bets provide superior returns for broad implementation

While many organizations are trying to implement artificial intelligence through multiple functions at one time, Accessure research shows that concentrated strategic stakes give much better results.

Ramani said: “The leaders of C-SUITE first need the agreement-then the expression is clearly-what the value of their company means, as well as how they hope to achieve this.” “In the report, we referred to” strategic bets “or long -term investments in Gen AI with a focus on the essence of the company’s value chain and a very large reward. This strategic focus is necessary to increase the capabilities of artificial intelligence and ensure that investments provide sustainable business value.”

This concentrated approach pays profits. Companies that have expanded at least one strategic bet, almost three times more likely to get their own investment return than GEN AI compared to those that did not.

IT Commander Work component: Determine 3-4 investments of private artificial intelligence in the industry, which directly affects the basic value chain instead of following up on wide implementation.

4. The official AI creates a value that exceeds risk mitigate

Most of the AI ​​responsible institutions are primarily compliance, but Acceneture research reveals that mature artificial intelligence practices are directly contributing to business performance.

“Companies need to transform their mentality from looking at the responsible artificial intelligence, such as commitment to compliance with recognition as a strategic empowerment factor for the value of work,” Ramani explained. “The return on investment can be measured in terms of short -term competencies, such as improvements in the workflow, but they must already be measured in exchange for the long -term business.”

The report emphasizes that responsible artificial intelligence includes risk alleviation, but also enhances customer confidence, improves product quality and promotes talent acquisition – which directly contributes to financial performance.

IT Commander Work component: Developing a comprehensive AI governance that exceeds compliance selection boxes. Implement pre -emptive monitoring systems that constantly evaluate the risks and effects of artificial intelligence. Think about building AI’s principles directly responsible for your development instead of applying them retroactively.

5. The first place is in the intention of AI Agency

The report highlights the transformative trend between the first positions: the publication of “agent architecture”-networks of artificial intelligence agents who organize the entire workflow independently.

Front managers explain much larger maturity in the deployment of independent artificial intelligence agents designed to meet industry needs. The report shows that 65 % of the first place excels in this possibility compared to 50 % of the fast followers, with a third of companies that have already been included in the survey using artificial intelligence agents to enhance innovation.

These smart agent networks are a fundamental transformation of traditional artificial intelligence applications. It enables the advanced cooperation between artificial intelligence systems that significantly improve quality, productivity and cost efficiency.

IT Commander Work component: Start exploring how artificial intelligence agent can transform basic commercial operations by determining the workflow tasks that may benefit from independent synchronization. Establishing experimental projects focusing on multi -agent systems in high -value use cases in your field of work.

The concrete rewards for the maturity of the artificial intelligence of institutions

Successful artificial intelligence implementation rewards are still convincing to organizations in all stages of maturity. Accenture Search determines the expected benefits in specific phrases.

Ramani said: “Regardless of whether the company is considered the front centers, a fast follower, a company that scores progress, or a company that still experiences artificial intelligence, all the companies that we included in their survey expect great things from the use of artificial intelligence to push re -enrollment,” Ramani said. “On average, these institutions expect a 13 % productivity, an increase of 12 % in revenue growth, 11 % improvement in customer experience, and a 11 % decrease in costs within 18 months of deployment and expansion of AI through their institutions.”

By adopting the practices of the first place, more organizations can bridge the gap between the experiment of artificial intelligence and the transformation at the level of the institution.