21 Oct 2019
AI Strategy and Implementation


AI Strategy and Implementation

Artificial intelligence (AI) is now playing an important role in how businesses operate and has significantly transformed the way many businesses work. From automated products and services recommendation to optimized processes, AI is no longer just a possibility, but real development with a focus on solving businesses challenges by leveraging proven algorithms. And from Retails to Manufacturing, AI provides businesses across various industries the capacity to enhance their operations and boost productivity as well as support for better business decision making.


In the new digital economy with an increasingly competitive marketplace, businesses constantly need to innovate and transform themselves by adopting Data-driven solutions as well as AI solutions development for a competitive edge over their competitions. However, not all businesses looking to adopt AI have a clear AI strategy as well as an implementation framework that are needed and critical to successfully implement their initiatives.

An optimal AI strategy will guide business on the right track to leverage and maximize the benefits that AI technologies provide, accomplishing their objectives of solving specific business problems, and also to identify their unique AI resources that allow business to have a competitive edges over their competitors.

Align AI strategy with the right people and overall business objectives

An AI development project would require buy-in from multiple parties and stakeholders from leadership to IT and end-users who are in Sales and/or Marketing etc.

Thus, to make AI work, businesses need to ensure that their AI initiative is in congruence with the overall business strategy and ensure that any party involved clearly understand what is required of their involvement and input for the project.

Otherwise, if your AI development endeavor won’t fit in with the corporate strategy, the whole program might be fragmented and or even at odds for the most parts. Besides, it’s important to align AI strategy with quantifiable goals and objectives to guide the AI deployment. An ideal AI strategy should be easily applied top down into divisional or local-level strategy.

Data Strategy

Any AI project would require data, and actually lots of data that is of quality and in the right format.

Read more about: Data strategy for AI project – Key Factors for a good Data strategy

Identify the correct problems to be solved using AI

Once it’s clear that the AI program initiatives is aligned with overall business strategy, the next step is to find out the exact problems that need to be solved using AI. This is extremely important and will ultimately have a considerable impact on business success, because there are many business problems that are not AI related or do not exactly require AI technologies for solutions.

Investing AI – which is an expensive technology – in the wrong place might lead to waste of resources. And businesses should, instead, identify the real problem which need AI and have the AI solution to be developed and delivered in incremental iteration – preferably through MVP (minimum viable product) or Prototype that act as a model for development of their entire capabilities.

MVP development projects usually do not cost a lot like a full-scale AI solution or take long to be completed and deliver results quickly for businesses to assess the effectiveness of their AI program. All in all, business will still need to evaluate the overall MVP project scope, size and its timeline as well as the overall impact on the specific business process as a whole.

In house vs. Offshore Outsourcing AI development

For businesses that are not in the IT industry, establish an in-house AI infrastructure can be a real challenge and the shortage of skilled data scientists and machine learning engineers make it even more difficult.

As a result, companies should review their existing in-house capacity in terms of AI and Data Science to find out where the skill gaps are and how such gaps should be addressed.

In particular, those skills gaps and expertise can be filled by hiring new IT talents and train in-house. If starting from scratch, companies also need to consider AI buy-in from leaderships and at other levels in the business. And should hiring local IT talents become too costly, then outsourcing to an external AI solutions development provider can also be considered.


Furthermore, since AI project would require and consists of a range of different technologies to make it work, thus, when forming an in-house AI development team, businesses should try to have a multi-disciplinary teams in their AI department, for example, AI & Machine Learning engineers, Data Scientist, web developers, etc. having a diverse team will increase the chance of success for the whole AI development endeavor.

Again, businesses might consider outsource a part of their AI development to an external software development outsourcing firm if it’s not possible to acquire the local technology expertise that is required as a part of the whole AI program.

Yet, it could be very unlikely that your in-house team get it right at the first instance, so having an open mind for experimentation to gradually locating the right AI asset is crucial to build the team up and get things running.  

Have a customer-centric AI strategy

Generally speaking, when it comes to AI product development, many businesses adopt a product-centric rather than customer centric, where products were built first and then customers were found.

When create AI strategy, it’s vital that businesses take a more customer-centric approach rather than product-centric.

AI solutions should be developed to help business interact and serve their customers better and more efficiently, eliminating frictions and make the whole engagement more personalized and frictionless, which in turn bringing in more revenue and profitability.

A customer-centric AI strategy will measure the results in corresponding back to those customer-centric KPIs, thus finding out the ways that business can serve their customers better in various ways.

Adoption – Scalability, Upgrade, and Maintenance

AI and data science is all about continuously learning through repeated development and measured results through feedback.

If an AI program is not well received and adopted by end-users – which could be due to numerous reasons such as lack of training and program high-complexity etc., there will be hardly any feedback and/or no longer it is machine learning but more like a one-off thing. Hence, to ensure adoption, businesses can take advantage of the immediate results and feedbacks provided through the MVP or prototype project as well as employ training services and review session for continuous optimization.

Any particular business problems that solved by AI effectively, no matter how small, is an opportunity to demonstrate the benefits of AI and gain buy-in for bigger AI project that is scalable across business.  

AI is a continuous endeavor

Last but not least, businesses should treat AI as a continuous endeavor rather than a one-time thing.

This means that no matter how successful MVP or lighthouse project has proven to work, businesses still need to constantly invest in the resources and effort into their AI program, in order improve and make their AI strategy as unique and difficult for competitors to replicate, making the competitive advantage becomes even stronger with time.

Additionally, once those MVP or prototype development projects proven to work, the in-house AI capacity will be need be scaled up and expanded further horizontally by acquiring new skills and technologies for continuous optimization of the AI solution. Therefore, businesses will also need to take into consideration of fostering a culture where continuously learning and improvement take place with people at the center.


An AI strategy will act as a continuously evolving guideline to ensure the chosen AI programs are developed and work toward business goals, driving innovation across all business functions.

In order to transition into a data-driven or at a higher level of AI-driven solutions, businesses will have to adopt a culture of experiment willingness and continuous improvement by starting small and implementing through short and incremental cycles, driving true AI transformation over time.

And most importantly, it should be the businesses requirements that lead AI transformation, instead of the other way around where businesses applying AI just for the sake of applying it.


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