AI Readiness Assessment
With the advancement of artificial intelligence (AI), its ability to disrupt industries continues to grow and becomes a game-changer if companies give it a purpose. Organizations are thus left with better options to explore new markets, improve existing processes or create better value propositions. However, success in utilizing AI does not come from just providing the necessary technology and tools, it requires a plan that shows the organization is, in fact, AI-ready. This is where the concept of an AI Readiness Assessment comes into play.
For the organizations that have gone through an AI Readiness Assessment, the benefits include an understanding of their current capabilities, identification of their gaps, and definable steps towards the successful deployment of AI. This handbook will define what it means to be AI ready, describe what aspects can be looked at in an AI readiness assessment and how the companies can use the insights to further their AI agenda.
What is AI Readiness?
It is the ability of an organization to apply AI technologies in such a manner that returns are generated while at the same time enabling the organization to grow sustainably. AI readiness has several dimensions namely: availability of technical capabilities, data, organizational environment, skilled personnel, as well as governance frameworks. A high readiness level means that the organization has the capability to embed AI into its business effectively and enjoy the returns from the deployment.
An AI readiness assessment is meant to find out how ready an organization is, on these dimensions, as well as what steps the organization can take to become more prepared for using AI technologies. AI is a powerful weapon, but its full use cannot be harnessed unless the organization is equipped with the required base.
Key Components of an AI Readiness Assessment
A readiness for an AI undertaking is defined by an AI Readiness Evaluation and below are the critical areas that require assessment:
1. Data Infrastructure and Strategy
The core of AI is and remains data and organizations without proper data infrastructure will be unable to create AI models that provide insights and automation of processes in a meaningful manner. When preparing for any AI readiness assessment, organizations must also examine their data infrastructure and data strategies which can include the assessment of:
- Data availability: Does the organization have access to sufficient volumes of relevant, high-quality data?
- Data management practices: Do experts or practice managers have systems to gather and organize data in an efficient manner?
- Data integration: Is there data that is spread over various departments or within silos?
- Management of data: Do policies exist to govern the aspects of data management and protection, privacy and compliance to set out rules?
For any AI to be operational, it is important to ensure that the data systems are sound and integrated. Further, organizations should have a detailed plan on how to use data to extract more value from AI.
2. Technical Infrastructure
AI technologies are generally demanding in terms of computing power, storage capacity, and software resources. Organizations have to review their existing technical infrastructure to see if it has the means to accommodate AI initiatives. Some of the issues that should be considered include:
- Cloud readiness: Owing to their scalable resources, cloud platforms are the most suitable infrastructures for AI. How does the organization use clouds for the processing requirements of the AI algorithms?
- High-performance computing (HPC): Certain AI tasks and applications, particularly deep learning, may require access to HPC. Does the organization have such capabilities?
- Integration with existing systems: Can the AI solutions easily fit into the organization’s core IT structure?
It is important to have the appropriate technical infrastructure in place to manage the resource intensive aspects of AI workloads, and to ensure deployment and scaling of AI systems as required.
3. Talent and Skills
The application of AI entails having a workforce consisting of data scientists, machine learning engineers, and AI strategists. The organizations wishing to implement AI technologies should assess the partly attainable strategies in regard to an appropriate mix of core competencies. The above-mentioned corporate initiatives cover the four aspects presented in the points below:
- AI capability: Does the organization’s business model envisage having any in-house professionals specializing in AI, or will it need to recruit or train people to fill the voids?
- Data analysis and understanding: Apart from AI, organizations require personnel skilled in data science, data engineering, and data analysis.
- Knowledge improvement: Does the organization promote learning and upskilling to enable advancement in AI technologies?
In most instances, organizations will need to provide learning resources or hire additional staff to create a competent AI team.
4. Leadership and Culture
The AI implementation is not merely a technological advancement; it calls for a change in an organization’s culture and way of thinking. Strong leaders are necessary for AI adoption as well as monitoring the linkage between AI initiatives and strategies of the organization. Some important issues to consider in this respect are:
- Leadership buy-in: Are the top management authorities on board with AI deployment, and do they make provisions for its deployment?
- AI strategic alignment: How clear is the organization’s AI strategy in relation to its core business objectives?
- Cultural preparedness: Will the organization be able to accept the AI changes, the innovations, and the decisions based on the data?
Additionally, organizations should motivate their employees to embrace new AI systems and incorporate them into their daily functions. Leaders must lead this change and communicate the benefits of AI to the relevant parties.
5. Integration of Processes and Workflows
In order for AI to be useful, it has to be properly embedded into the business processes and workflows of the organization. This includes assessing the existing processes for their suitability for AI based automation as well as determining the gaps that can benefited from AI. In the course of evaluation, organizations must take into account the following:
- Potential efficiency: Which business processes can be optimized or eliminated from the manual effort and replaced by AI?
- Coordination and integration: Are there some cross-functional teams who work on AI projects in the institution?
- Management of organizational change: Does the organization have the capability to adapt to the new workflows that will be brought about due to the process automation by AI?
Integrating AI into processes is successful when there is a plan to ensure that the persons that will work with the AI machines are capable and that there will be proper enforcement of the new processes.
6. Ethics and Governance
Incorporating AI into business processes and applying AI in practice both has its ethical implications. It is not enough to simply use available tools and target specific outcomes. Businesses will be required and prepared to put in place governance mechanisms that would not only help manage AI risks and ensure legal obligations but also eliminate biases within AI models. It is suggested that during the readiness assessment, organizations consider such particulars as:
- Ethical AI Frameworks: Does the organization have ethical AI policies for the use of AI, which policies must address aspects such as fairness, transparency, and accountability?
- AI risk management: Are there AI deployment related risks which exist, and if so, are there mechanisms to identify and cut down the impact of the risk?
- Compliance: Does the organization adhere to applicable laws and regulations while deploying AI related technologies?
Governance is critical to guide the manner and ethics out of which AI would be employed by the entities. It also articulates how such usage could be in line with the organizational ethos.
Moving Forward After an AI Readiness Assessment
After it has been established that an organization is ready and has the required resources to embed AI into its business processes, the organization should then work towards formulating an adequate plan which will detail how exactly the AI is to be integrated and adopted across the organization. This plan should also indicate what areas are particularly weak pointing to the gradual interaction with AI. The action points include:
- Establishing foundational competencies: Fill any gaps in data infrastructure, technology systems and human capital in order to develop firm’s building blocks for AI.
- Initiatives: Use the initial few AI projects focusing on a targeted area to achieve results within a defined scope to show the organization the usefulness of AI.
- Growth of the AI: With the increment in the number of departments that are at different stages of AI adoption, the organization can implement AI strategies at the center. The AI implementation strategies can be replicated to other departments considering the factors that made the pilot successful.
- Looking Forward: One thing that is clear with the development of AI is that the field will keep changing and improving and so will the technologies that are in use as well as the opportunities that come up.
Summary
AI Readiness Assessment is an important phase for any organization intending to use AI in their activities. It is also important in determining the active areas, for example, infrastructure, skills, management, and governance which will enable the organization to deploy AI optimally. With a realistic timeline in mind, organizations can proceed with assurance that they have what it takes to utilize AI and create sustained value in the process.
