Artificial Intelligence (AI) represents a business opportunity for companies that want to improve their processes and acquire new customers. However, not all companies that invest in AI succeed in achieving the desired results. To help them, Intellico has developed a framework based on the analysis of 5 input and 3 output dimensions.
Why AI in the company?
There are two main challenges in which Artificial Intelligence can play a key role in business: improving the activities that are carried out in a process and improving the way these are currently carried out.
It is important to emphasize that the benefits do not only derive from greater efficiency in processes, but also from the possibility for companies to acquire new customers by generating ideas, products and, in some cases, even sectors.
AI opportunities and challenges for Artificial Intelligence in Business
Reports in recent years show that Artificial Intelligence is a sought-after choice for many different companies. The trend is mainly due to the technological evolution we are experiencing in improved algorithms, availability of low-cost cloud resources and the huge amount of data available. However, some data show that 7 out of 10 companies have gained little or no contribution from its implementation and that less than 40 per cent of those that have invested in AI have achieved results in the previous three years (Source: MIT Sloan).
Leveraging our more than 40 years of combined experience as consultants, entrepreneurs, and Machine Learning experts, Intellico has developed a framework that is summarized below to provide a clear and concise approach to:
1. Assess whether your company has the foundations for AI success
2. Maximize the value of your AI investments
The key elements of successful AI projects
Intellico has highlighted 6 best practices to follow to structure the introduction of tools effectively:
- Have a clear vision of data and AI within the company by aligning achievable results with business objectives, identifying problems that can be solved with algorithms, and avoiding situations where they are not useful or ineffective.
- Start with simple use cases based on the data already available.
- Experiment with new approaches, remembering that the benefit derived is long term and that there is no need to focus on improving efficiency during experimentation.
- Scale up the investment, making forecasts on how to move from the pilot project to a large-scale one.
- Make sure you do not just develop solutions but create tangible change.
- Constantly measure the implemented solutions to pursue continuous improvement.
How to assess the company’s degree of maturity: the framework developed by Intellico
To assess the degree of maturity of a company with respect to Artificial Intelligence, Intellico proposes a framework based on three simple but significant characteristics:
- Accessibility ensured by timely questions and easy-to-complete multiple-choice questionnaires.
- Data flows to evaluate the passage of data between the various business units (data chain).
- Solid foundation to create value quickly and effectively right from the start.
- Strategy: it must be clear from the outset what role the AI tool can play in creating value for the company, remembering that it is a tool and not the scope of the investment.
- Skills and corporate culture: the success of innovation depends on the ability of employees to use the tool and the company’s data orientation.
- Process: the process structure must be as adaptable as possible to the innovation, avoiding forced updating.
- Technology: Existing systems and platforms must be able to allow change at the level of data accessibility.
- Governance: the governance structure is crucial for managing data quality.
Outputs of the framework
- Volume and speed of data collected: the optimal volume and speed varies, but in general data should be provided at a rate that allows informed data-driven decisions to be made.
- Fluency: the arrival and processing of data from the various functions allows benefits to be identified in detail by evaluating combinations of effects.
- Impact: the implementation is truly effective when the organization uses the data to make decisions.
Stages of the company’s AI Readiness
Based on the inputs and outputs, the company may be in one of the following 4 stages:
- Starting point (poor input/poor output): organizations at this stage should invest in skills and technology and start with pilot projects.
- Focus on strategy and cultural change (good input/poor output): organizations at this stage have already made the right investments in skills and technology but a cultural change is needed. For this to happen, it is important that corporate leadership conveys this need and then invests in pilot projects that can show the effectiveness of the changes.
- Skills and technology enhancement (poor input/ good output): organizations at this stage are ready to make decisions by means of AI due to the very good inter-functional communication flow. On the other hand, they need to invest heavily in skills and technology, especially in data platforms, to put an end to a local approach.
- AI Ready (good input/good output): organizations at this stage can fully enjoy the benefits of AI. Thanks to a long-term vision, it is possible to move from pilot projects to more complex ones. However, it is necessary to continue one’s own development and to associate change with well-defined areas and ideas, finally assessing those that are worth considering.
For more information and to receive our assessment, please do not hesitate to contact us.