Key conclusions
- Most AI pilots fail: 95% of AI Enterprise pilots do not demonstrate the return of investments from the poor fitting of the work process, the lack of training in the AI ​​model and the weak integration of the work process.
- Workers prefer llms: Employees prefer Consumer LLM tools, such as ChatGPT, compared with AI Enterprise solutions from familiar interfaces and the best results.
- Success proceeds from adaptive AI systems: Companies that introduce AI into work processes provide feedback training for AI and are scaled from small, but highly valuable use options, achieve success in their AI pilots.
Despite the fact that billions of dollars invested in adoption of artificial intelligence, most companies can be shown little.
According to the latter MIT Nanda Research95% of organizations that started Genai pilots did not see the return of investment.
While the vast majority of artificial intelligence pilots are poorly failure, without any measuring profit and losses (P&L), only 5% of integrated AI pilots generate millions in business costs.
Why do Genea pilots fail?
Most pilots fail not from weak models, but because the tools do not correspond to real work processes.
Employees expect AI to adapt, study and improve, but most corporate systems do not correspond to these expectations.
So why do most of the companies fight, while only a few are successful? The study indicates three main causes.
1. Block blocks for the scaling of AI in setting up the enterprise
The MIT study showed that the greatest obstacle to the successful scaling of artificial intelligence is the unwillingness of employees to accept new tools.
Other reasons for the failure of the AI ​​pilots include fears about the quality of the output of the model, poor user experience, the lack of sponsorship of the executive branch and complex changes.
On the scale 1-10, this is where each checkpoint costs:
At first it seems strange that employees complain about the quality of the model. After all, 41% of workers Already used ChatGPT and similar tools of artificial intelligence.
The problem is that the same workers who like the use of ChatGPT or other artificial intelligence tools personally consider the AI ​​Enterprise version unreliable, which can cause resistance to Ait Enterprise tools.
2. The preference of the user for common artificial intelligence tools
Organizations are invested in expensive individual solutions for artificial intelligence enterprises, designed to meet their specific needs.
Nevertheless, employees prefer general tools of artificial intelligence, such as ChatGPT, because they are faster, simpler and adapted than Ait Enterprise solutions.
According to the study, employees prefer LLM consumer llm because they are:
- Trust them more than corporate decisions.
- Find interfaces more familiar.
- Get better results.
3. Problems with the basic integration of work processes
Another reason why pilots pilots are that artificial intelligence tools cannot study in feedback.
The study notes that the AI ​​tools, even consumer LLM, which employees prefer, fail in high -risk tasks. They often forget the context, cannot study and cannot develop.
When he was asked about the obstacles to the basic integration of work processes, the inability to study on users' reviews was invariably the most significant barrier.
Other obstacles include excessive requirements for the context, a poor landing of the work process and failures in cases of edges from a limited adaptability.
Together, these restrictions force corporate users to depend on people for important tasks.
In fact, 90% of enterprises prefer people for difficult tasks, such as customer management or projects that cover several weeks.
However, for such quick tasks as basic analysis, e -mail or resume, 70% of enterprises prefer AI.
These obstacles create a gloomy look. Nevertheless, studies show that success is still achievable if companies are realized by AI.
Success recipe
The MIT study states that companies that are successful in artificial intelligence pilots are developing adaptive built -in systems that study in feedback.
In fact, 66% of managers want AI who is studying in feedback, and 63% await systems that can maintain the context.
These companies are also not trying to do everything at once. They are focused on small but high cases of use, and then expand constant training.
More importantly, they integrate their tools directly into their work processes, adapting to the context and expand from a narrow but high starting point.
In short, the success of artificial intelligence comes to organizations that decide for training, memory and work process, while general tools and internal assemblies fail.
The truth behind the excitement
The MIT study also exposes several common Genai myths in enterprises such as:
- AI will soon replace most jobs: There were few dismissals from the Genai, and only in sectors, already strongly prone to strong exposure to AI.
- Genai transforms the business: The adoption of AI is widespread, but this rarely leads to an actual transformation. Seven out of nine sectors studied do not show structural changes.
- Enterprises are lagging behind when adopting new technologies: They with great enthusiasm for AI, with 90% actively studying the solutions of AI.
- Ai -kioski from the quality of the model and legal restrictions: Most of artificial intelligence tools cannot generate the profitability of investment because they cannot study or integrate smoothly into work processes.
- Leading companies create their own artificial intelligence tools: When firms develop internal AI, their projects fail about twice as much as those who work with external suppliers or partners.
The path to AI ROI
95% of the frequency of the Genai pilot does not mean that enterprises cannot benefit from AI.
Instead, he acts as an alarming call for companies, demonstrating that investment in AI is not guaranteed to increase P&L.
The MIT study clarifies one thing:
The success of AI does not come from bright AI or general tools, but from systems that can learn, maintain context and smoothly integrate into work processes.
At the same time, enterprises should also work to overcome the main obstacles to adoption of AI, such as:
- Lack of skills to support AI.
- Lack of vision among managers and leaders.
- The high cost of available products/services of AI.
The future of Genai at enterprises will go to those who pass by the hype, accepts the systems that promote training, and coordinates AI with real work processes.
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