Post by account_disabled on Mar 4, 2024 23:39:36 GMT -5
The mold even more: artificial intelligence. But are we ready to implement ai in your business ? Technology is really something that I am passionate about, which is why I invest time researching and looking for ai material. Precisely in that search I came across an article by professor tsedal neeley, who is associate dean of teaching and research at harvard business school. In her writing, professor neeley offers some questions that company leaders should answer before implementing ai solutions. Below, we share 5 questions that, for us, are mandatory: how should I prepare to incorporate ai into the company? To start, it's important to recognize that the optimal way to work with ai is different from that used with other new technologies. In the past, most new tools simply allowed us to perform tasks more efficiently. People wrote with pens, then with typewriters (which were faster), then with computers (which were even faster). Each new tool allowed for more efficient writing, but the general processes (writing, revising, editing) remained largely the same. Ai is different. It has a more substantial influence on our work and processes, because it is able to find patterns that we cannot see and use them to provide ideas and analysis, predictions, suggestions and even complete drafts on its own. So instead of thinking of ai as the tools we use, we should think of it as a set of systems with which we can collaborate. To effectively collaborate with ai in the company, you must focus on three aspects: first, make sure everyone has a basic understanding of how digital systems work.
A digital mindset is a set of attitudes and behaviors that help you see new possibilities using data, technology, algorithms and ai second, make sure your company is prepared for continuous adaptation and change. Incorporating new ai requires employees to get used to processing new streams of data and content, analyzing it, and using its conclusions and results to develop new perspective. Likewise, to use data and technology most efficiently, organizations need an integrated organizational structure. Third, incorporate ai into the operating model. As my colleagues marco iansiti and karim r. Lakhani have shown, the Buy Bulk SMS Service structure of an organization reflects the architecture of the technological systems it contains, and vice versa. If technological systems are static, your organization will be static. But if they are flexible, the company will be flexible. This strategy worked successfully on amazon. According to iansiti and lakhani, the company was having trouble maintaining its growth and its software infrastructure was “cracking under pressure.” so jeff bezos wrote a memo to employees announcing that all computers had to route their data through “application programming interfaces” (apis), which allow various types of software to communicate and share data using established protocols. Anyone who didn't would be fired. This was an attempt to break the inertia within amazon's technology systems, and it worked, dismantling data silos, increasing collaboration, and helping to build the software and data-driven operating model we see today. While you may not want to resort to a similar ultimatum, you should think about how the introduction of ai can – and should – change your operations for the better.
What are the actions we can apply to guarantee transparency in decision-making using ai? Recognize that ai is invisible and inscrutable and adopt a transparent attitude when presenting and using ai systems. Managers must recognize that it is not always possible to know how ai systems make their decisions. Some of the same characteristics that allow ai to quickly process huge amounts of data and perform certain tasks more accurately or efficiently than humans can also make it a black box: we can't see how the result is produced. However, we can all contribute to increasing transparency and accountability in ai decision-making processes in two ways: callen anthony, beth a. Bechky, and anne-laure fayard identify invisibility and inscrutability as fundamental characteristics that differentiate ai from previous technologies. It is invisible because it often runs in the background of other technologies or platforms without users being aware of it; for every siri or alexa that people understand as ai, there are many technologies, like anti-lock brakes, that contain invisible ai systems. It's inscrutable because, even for ai developers, it's often impossible to understand how a model gets to a result, or even identify all the data points it's using to get there, good, bad, or otherwise. As ais rely on ever-larger data sets, this becomes increasingly true. Let's think about large language models (llm), such as openai's chatgpt or microsoft's bing. Openai's llm was trained on 175 billion parameters and was built to predict the probability of something happening (a character, a word or a string of words, or even an image or a tonal change in your voice) based on its context preceding or surrounding.