News

4 steps for a successful adoption of AI technologies within Customs environment

Discover the four steps and high-level framework that specifies the key dimensions for effective artificial intelligence technologies adoption governance.

With the rapid digital transformation impacting numerous industries globally, sophisticated data-driven technologies are now becoming more accessible to business organisations. Such accessibility has created new opportunities for customs administrations to support their reforms and modernisation programs. However, the need to leverage those technologies also creates new questions such as:

  • What are the existing customs business processes that can benefit the most from AI-driven technologies?
  • What are the right data solution technologies customs administrations should invest in to achieve growth and success in their business?
  • How can we integrate identified technologies into the existing processes and systems assets?
  • Do customs administrations have the required expertise to obtain the full benefits of the considered technologies? Should they hire/consult experts or train existing resources?
  • How should we roll out an effective change management process to maximise the likelihood of delivering the intended results and outcomes?

This article proposes a simple and logical framework prototype that specifies the key dimensions to consider for an effective AI technologies adoption governance.

The particularity of the proposed canvas is its customer-centric approach which can be implemented following a 4-step process.

Step 1: Audience definition

The model starts from the customer segment profile (jobs, pain points and delights) to identify fit-for-purpose innovative solutions. It answers the following primary questions:

  • Who are the targeted audience(s)?
  • What are their characteristics?
  • Who are the most important beneficiaries?
  • What value (products and services) can be created for each beneficiary segment?
  • What beneficiary problems are being addressed?

Step 2: Internal Capacity Assessment

It reviews the internal capacity (human, skills, equipment, facilities and data) versus the affordability to achieve the value proposition internally and the cost structure. It also focuses on leveraging the business ecosystem to build smart partnerships to ensure successful value creation and maximise the return on investment. It answers the following guiding questions:

  • What resources (profiles and essential skills) are important to achieve the value proposition?
  • What data do we have access to?
  • In what volume and quality?
  • Can the current data format be used as it is?
  • What processes/projects are important to realise the value proposition?
  • What key activities need to be performed?
  • Who are the strategic suppliers?
  • Who are the key partners?

Step 3: Value Delivery Channels and Roadmap

The framework recommends adopting an agile methodology (using the Minimum Viable Product approach) as a way to accelerate time to value delivery for the targeted audience(s), and to facilitate continuous communication, easy adoption and feedback. The following guiding questions can be used at this step:

  • How is the value (products and services) provided?
  • Which channels engage more and are most cost effective?
  • Who are the champions (main influencers)?
  • What needs to be considered in terms of usability and user-friendliness of the technology to engage more?
  • Whose role will be affected by the new technology?

Step 4: Value appropriation measures definition

Finally, it connects the delivered solutions with the value appropriated by the beneficiaries, enabling the fulfilment of their expectations, values, and interests. Questions to be considered are:

  • Which needs, expectations, values, demands, and interests are being met?
  • How does the beneficiary make money or capture other forms of value?
  • What kind of public value is delivered?

It is recognised that AI represents huge opportunities for organisations like Customs to automate business processes and make their operations smarter. However, many Customs administrations are failing to adopt AI because they are still uncertain about how to approach it. For more information about AI within Customs environment, read our article on the 6 key considerations to unlock the benefit of AI.

Built using academic research and fraud insights ascertained from customs-processed data, CRMS® our risk management solution, combines advanced machine learning features with both selective and random selection mechanics to automatically evaluate risk and recommend levels of controls.

For more information about our CRMS® and our Customs solutions or to request a demo please contact us at governments@cotecna.com

BACK TO NEWS