An enterprise that invests some time in advance to create the framework and set goals for success can more easily improve its efficiency, achieve savings and transformation through artificial intelligence.
Like any new transformative technology, it shines beautifully for business leaders that promises to streamline business. For artificial intelligence (AI), this was especially true in 2020. According to a recent survey, 43 percent of businesses worldwide have accelerated their AI initiatives in response to the pandemic. However, many businesses have rushed to integrate artificial intelligence and have not stopped to ask the questions: what, how, and why?
AI may seem magical, but it is not magic, writes Inc. magazine. Bad algorithms give bad results. Although investment and experimentation are extremely important, the biggest and most common strategic mistake companies make when discovering AI is not to identify use cases and desired outcomes of the technology supported by clear, quantifiable metrics.
A people-centered approach to artificial intelligence begins with who will use the technology, how they will use it, and why AI is needed at all. It is therefore necessary to critically rethink the problems a company faces, to formulate these challenges in such a way that AI has the potential to be used, and then to identify and refine business-critical uses
. The following approach is appropriate for the introduction of AI:
1. Defining Intent
Many companies do not have a really clear idea of what they expect from AI beyond the vague notion of “efficiency”. Therefore, it is important to clarify the intent so that you have some time to explore the targeted AI business opportunities that exist within your existing business strategy. The goal could be, for example, to keep employees safe or to increase customer satisfaction. It is definitely worth starting with a clear intent based on some basic business purpose.
2. Identification
Once the overall goal for the deployment of artificial intelligence has been defined, the uses and types of artificial intelligence solutions required by users and eventually integrated into their infrastructure can be identified. AI is evolving rapidly in many areas, from computer vision to the artificial intelligence of language processing in chatbots and virtual assistants. The question is, how can these applications advance the intentions outlined?
3. Evaluation
The evaluation phase involves figuring out what data is needed to make the identified uses effective. Different types of teams focus on different priorities and different sets of numbers, which means that most industry data is boxed to some degree. In order for successful use cases to be generated with AI, it must be ensured that artificial intelligence is fed with accurate, clear data from the whole organization
4. Planning
The final step in the design thinking approach is to focus on defining concrete actions using letters of intent to guide technical implementation. The goal is to help customers make AI operational through the business process and link all solutions to the defined AI strategy.
It is critical that the implementation strategy takes into account user trust. That is, how will customers or consumers react to the organization using the data in this way? How can consumers and the public know that the implementation of artificial intelligence can be explained and trusted?
Designing a successful AI strategy is also about who has a say. It is important for businesses to involve different voices and relevant stakeholders at each stage of the process.
Strategy meetings can be attended by senior business leaders who define intent, define information types, business hypotheses form, identify use cases, and incorporate corporate ethics into strategy. Technical scientists, designers, and developers are invited to meet for technical meetings to translate the intentions identified at the strategy meeting into a detailed strategy, identify use cases, evaluate data, and plan implementation. During each exercise, visual storytelling, images, and graphics can help ensure that, although they come from different areas, all participants have the opportunity to speak the same language.
The most common lesson, however, is that businesses too often think , they already have all the data to run any artificial intelligence model. But in reality, this rarely or never happens. However, despite these difficulties, AI will transform its business practices, for which we can already find many examples and experiences.
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