17 March 2022

Challenges and opportunities towards AI solutions adoption

Artificial intelligence solutions have been revolutionizing the industry continuously in the last decades. The benefits delivered by these technologies are numerous and diverse; among others you can find: capacity to improve work efficiency, capacity to analyze big datasets, automate infrastructure for easy escalation, enhance customer experience, etc. Nowadays companies are challenging themselves to obtain benefits from these technologies, even enabling whole organizational transformations, boosting the capacity of the companies from its core.

The mere existence of these opportunities implies risk. A company's competitors can and will eventually exploit the capabilities of AI solutions, gaining significant competitive advantages. This fact brings pressure to implement AI strategies with architectural deficiencies based on general misconceptions among business people and AI specialists

This blog will explore two important problems to consider when integrating AI solutions with business strategies. Looking to provide solutions for future effective adoptions.

1. Implementing AI-Based solutions without clarifying the problem, neither business objectives: AI works as a wide collection of technologies which correct usage in the right situation can effectively help an organization to achieve strategic goals. One of the most common problems is the adoption of AI tools to "have as many AI applications across our enterprice solution", and then sell ourselves as "the AI-powered solution".

This integration of AI technologies cannot be developed in random order. It is proved that there exists an important correlation between the value gained with AI solutions and how integrated these initiatives are with the general digital strategy [1]. This pattern justifies that the impact of AI must be planned regarding the entire business model, not in single narrow scoped projects. In this way, executives are in a better position to appreciate and plan new paths to transform their business model using AI methods.

2. Lack of knowledge in work units:  This problem has two sides. From one part, there is a well known lack of highly qualified AI professionals. Usually, companies try to mitigate this problem using the outsourcing of these professionals. Nevertheless, this practice only works targeting particular projects with specific objectives, and the adoption among business areas of the final solution is often unclear. On the other hand, the second problem lies in the consumption side of the technology. It is crucial that business managers develop a genuine interest, and learn to cherish the disruptive capabilities that AI solutions can bring to business problems. In general, companies will never be able to exploit benefits from AI if medium management does not understand how technical solutions can improve the quality, efficiency, response time and resilience of the service their organizational area offers.

From the last points we can state that for meaningful AI technologies adoption, a reasonable way to proceed is the creation of AI teams, each of them tightly aligned with a business area with particular problems to be solved. In this scenario, the communication between AI specialists and Management will be projected on the quality of the final solution; given the importance of the capacity to exchange domain goals from business, and requirements to co-develop, calibrate and tailor AI solutions from AI specialists.

In an effort to make this communication easy, Databloom is preparing a web system called Blossom Studio that supports the generation of ML pipelines from data extraction to its final consumption. The interface provides a simplified overview of the operators and plans the organization has built; then, in an intuitive way the interface allows them to work with these assets and generate new transformation operators. Using the editor, the user is able to concatenate all these resources into new AI solutions that are parameterizable without the need of developing skills, just general AI knowledge.

All objects in the editor are customizable, and can be annotated providing all the relevant information for business users to be able to tailor these solutions regarding new needs. The work among different work units can be easily integrated and the preparation of multi-department assets can be split among business areas as separate operators that can be merged into a single one.

As a company we expect that these features will facilitate the elaboration of intricate execution plans. Making collaborations for the implementation of AI solutions more natural for today's work environments.


References

[1] https://sloanreview.mit.edu/projects/winning-with-ai/

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