Artificial Intelligence (AI) deals with the development of intelligent machines or software. The software can learn from past activity, data gathered from such activities, self-correct, and reach a conclusion. AI technology revolutionized the working of all the industries across the globe.
Many enterprises tend to try a small use case and if they succeed, they’ll implement a few more use cases. They sometimes try implementing new features without proper planning and if it doesn’t yield results, they’ll try another project. All this leads to cost overruns, effort duplication, scaling issues, incompatible systems, insufficient data lakes, and even privacy and ethics problems. An Enterprise AI Strategy can solve these issues.
Enterprise AI Strategy is a route for choosing and implementing AI within the enterprise. It defines priorities, objectives, mission, and vision. It is usually developed at the top level of the enterprise, by the top management, or by the board of directors. It deals with problems that affect the enterprise as a whole. AI strategy is used to analyze the industry and figure out which parts are important and require implementation. This is long term planning that includes a financial structure.
- Begin with core business priorities
If the vision of the enterprise and execution of AI doesn’t match, it would result in non-cohesive and complicated AI solutions that would take years to yield results. Therefore, it is necessary to understand the challenges and opportunities for the enterprise and align the AI objectives with its corporate strategy.
- Multidisciplinary AI team
A robust AI team includes members from different departments. For example, engineering, web design, and R&D. When such a multidisciplinary team assesses the enterprise AI strategy, they can verify if that strategy meets the objectives of individual departments. If there are any faults or gaps, they can revise the strategy accordingly.
- Strategic AI priorities
- There are two types of AI use cases. In the first type, industry best practices, that is, previously tried and successful use cases are implemented. It takes a definite period, from about a week to a year, for implementation. However, it directly impacts the enterprise and yields quick results.
- In the second strategy, the state of the art method is used. These are hard to replicate by others and might give a competitive advantage in the long term. The downside is that they may not have a direct impact on the enterprise and takes a long time, maybe even years to implement.
- It is paramount to select the right use cases. An enterprise must prioritize the first type of AI use cases that result in high ROI.
- Data strategy
AI needs enormous amounts of data to work successfully. Therefore, the data strategy of an enterprise must support its AI strategy. It is necessary to check whether they have enough data and if that data is fulfilling the AI requirements. If not, they have to decide how they will collect new data and which collection methods can be employed, or whether they should use any third-party data. Enterprises need an up to date data strategy to get optimal results from AI implementation.
- Technology and Infrastructure
- Technology requirements and challenges for various AI use cases have to be analyzed. The technology used to collect, store and process data have to be decided. Obtained insights have to be communicated efficiently.
- Picking the right platforms and partners is crucial to the process. Cloud computing helps to scale the AI enterprise solutions efficiently.
- Roadblocks during implementing enterprise AI solutions have to be identified and the AI Strategy team must be ready to resolve those problems. The team must also be prepared to handle both common requirements and complex challenges.
- Change management & scaling AI community
- AI projects can impact human jobs, especially when various tasks and processes are automated. Employees can develop an inherent distrust towards AI. This can be avoided by good change management, communication, and upskill programs for affected employees.
- When there is a skill gap between AI and data, new employees have to be hired and training has to be conducted to upskill the staff. If the requirement is too high, then the enterprise can partner with an external AI provider.
- The current AI team must not only be innovative but should also upskill the future AI teams after a use case is successful. They must also add automation to data science to be able to iterate the results on a large scale.
- Customer-centric
Times have changed. Focus has shifted from designing strategies from product-centric businesses to customer-centric businesses. It is now essential to execute backward from the value chain while designing an AI enterprise Strategy. This customer-centric approach must also align with business objectives.
- Ethical and legal issues
When AI is implemented, ethical and legal considerations must be comprehended by the enterprise AI team. Privacy and consent are vital factors to be considered. AI must be free from any bias and discrimination. AI must be used for the betterment of customers, employees, and business, i.e. its usage has to be ethical.
Conclusion
Adopting AI technology in the enterprise is essential to have a competitive edge over others and for a business to become market leaders. Projects must be planned, tired, tested, improved, and then scaled.
This must happen through short cycles to obtain optimal enterprise AI transformation. Hence, an AI strategy will act as a roadmap to move towards business goals.
