Taking the AI road? Pause, reflect and ask the right questions

Business transformations aren’t a new phenomenon. For decades, companies have leaned on technology to evolve. What has changed is the nature of these transformations. Industrialization kicked off the first wave of transformations aimed at seeking unprecedented productivity and efficacy.

The rise of digitalization drove the second wave of business transformations that involved merging machines and consulting capabilities. The current wave of transformations is based on augmenting machines, people, and processes with computing and is driven largely by artificial intelligence. Beyond the apprehensions and scepticism, the AI revolution has kicked off a business metamorphosis.

Today, as AI emerges as the catalyst for digital transformations, many organizations lack the right resources and approach to begin such a journey.  A lot of organizations see the benefits of AI transformation but are uncertain about how to start. They need guidance on transitioning from observers to active participants in this change.

In this blog, Anurag Sahay, Head of AI and Data initiatives at Nagarro, explains how organizations can assess their readiness for an AI and data-driven transformation. It points out the key questions organizations must ask when embarking on an AI-driven transformation.  

Key takeaways

• AI transformation must be a conscious choice and not a default option.
• A successful AI transformation hinges on qualitative data.
• Creating a new type of intelligence demands a design thinking approach.
• A governance framework is critical to the success of an AI transformation project.

What entails an AI-led transformation?

AI-led transformation weaves intelligence into processes, products, and services and augments human beings. It goes beyond applying AI and data science to disparate projects across organizations and demands a mindset change from the top. 

The strategy dimension: Is AI transformation a strategic goal or a default choice?

The AI journey begins with the question of whether AI-driven transformation is a strategic goal and promises clear, tangible value.

The transformation must be a strategic choice aligned with the business objectives. This requires active participation from the C-suite leaders, who must shape the strategy and ensure its implementation. A top-down approach ensures cohesion and avoids scattered AI initiatives across the enterprise.

Here are the other critical questions organizations must answer at this stage of the AI journey: 

• Who will lead the strategy and implementation of AI initiatives?
• Have the use cases been identified?
• What kind of intelligence is required to create this transformation?
• Have the cost factors and ROI been considered?

Once these questions have been tackled, the leadership must initiate and drive a mindset change starting from the top. This involves establishing and communicating the value of AI and data-driven transformation and addressing the fears and concerns. 

The data dimension: Is there enough quality data to support the transformation?

Intelligence cannot be created in a void; it comes from data. While most enterprises have sufficient data, they must assess its quality and nature. Additionally, creating newer types of intelligence in products and services requires new data sets, so enterprises must ask if they have the relevant data to create the intelligence they seek.

A few important questions organizations must ask themselves in this context:

• Are they using transactional data in their enterprise ecosystem, or do they have a bigger vision that transcends transactional data?
• Are they investing in infrastructure to capture newer data forms?
• Do they have the processes for extracting the required data?
• Lastly, does the quality of the available data support the sought intelligence and transformation? 

This data journey is cyclical: The more data organizations use, the more data they generate. Businesses must begin with the data available, and as they build and operate AI models, they will generate more data and feedback, which again improves the models. For instance, Tesla recently signed a deal with the Chinese government, allowing the tech giant access to data from its autonomous vehicles on the road. Tesla will then use this data to improve its AI models further.

The second part of the data puzzle involves collecting data from multiple sources and integrating it to deliver tangible value. If we consider the end-to-end car manufacturing process, OEMs can extract a lot of data to improve their processes further. 

Currently many OEMs don’t collect data at all points; they have disparate data, such as sensor data from the manufacturing plant. Combining this data with data from processes like assembly and sales, it becomes unique and unlocks the sought-after intelligence. Combining disparate data and insights helps build a new customer view, leading to higher customer satisfaction levels.

Organizations like Tesla, Amazon and Netflix have successfully deployed this data approach to deliver unparalleled customer satisfaction and competitive advantage.

The third dimension: Have you planned for data democratization and people readiness? 

As AI evolves and makes inroads across departments and functions within business organizations, a human-in-the-loop approach is critical to ensure people are excited and not sceptical about this transformation. When looking at the people side of AI transformations, there are three critical components to consider: data and AI democratization, mindset change, and skilling.

Here are the important questions to ask while evaluating the readiness of the workforce:

• How democratic are AI and data assets in an organization?
• Are the people ready to use the means and methods to become data and AI-driven?
• Is the workforce trained and capable of building the kind of intelligence required? 

Providing the workforce access to data and AI tools is a significant step towards fostering a culture of inclusion and skill development. However, to fully bridge the skill gap and address any fears or apprehensions about AI, it's essential to invest substantially in resources and partnerships.

Microsoft and Google have invested significantly in AI skilling and education programs. Microsoft has committed billions to AI programs impacting millions globally, focusing on building AI fluency, developing technical skills, supporting business transformation, and promoting responsible AI deployment. They also assist organizations in launching their own AI skilling initiatives. Similarly, Google has pledged €25 million ($26.9 million) to support AI training and skills development across Europe. 

Low code for AI and data democratization 

The fourth dimension: How do you reimagine your products/services to reflect this capability?

The next step is to balance the data, people, and processes part of the AI equation to deliver the organization's desired results. This starts with identifying and aligning the top business objectives with AI initiatives.

Aligning AI projects with organizational objectives requires a design thinking approach. This approach fosters user-centred innovation and improvement, understanding user needs, defining clear objectives, brainstorming creative solutions, and prototyping for quick feedback. It promotes cross-functional collaboration and scalability while emphasizing ethical AI deployment.

Applying AI across the board without tying it to the organization's strategic objectives will not yield the desired returns and investments. At the same time, scattered AI projects across functions aren't optimal either. Organizations need to find the right balance. After observing the results, they can begin with pilots and roll out the project organization-wide.

Amazon, Netflix and Tesla are some of the organizations that have successfully utilized AI to build unparalleled products and services by incorporating data-driven intelligence. 

The fifth dimension: Is your AI governance framework in place?

AI's sheer potential and nature, which makes it different from other technologies, also makes it difficult to govern. The promised benefits lure organizations, but there are also possible risks.

An AI governance framework can bring about better, more sensible, and compliant adoption.

From a governance perspective, organizations must ask themselves the following questions:

• Do they have an AI governance framework?
• Do they understand the policies, licences, and regulations about AI?
• Have they established the importance of AI and data governance within your organization? 

How to build an AI governance framework?

Creating an effective AI governance intertwines with broader corporate governance, including the active involvement of the Board of Directors and collaboration among various functions like IT, legal, and risk management.

Organizations looking to create a data and AI governance framework must begin with these five building blocks:

Policies and procedures: Policies and procedures that help implement and monitor the governance framework. They ensure ethical AI and data practices and monitoring through guidelines on data collection, storage, access, use data privacy, security, and quality.  
It further involves implementing post-deployment monitoring processes and developing human oversight and intervention protocols.

Roles and responsibilities: When defining responsibilities for the AI and data governance framework, the first step is to decide who drives the transformation. Secondly, you must clearly define the roles of all stakeholders, including executives, managers, data scientists, and ethical review boards, should have clearly defined roles.

Transparency and accountability: Establishing transparency and accountability starts with providing clear and accessible information to all stakeholders. This involves publishing regular reports on AI initiatives and ensuring compliance with relevant laws and regulations, such as GDPR and CCPA.

Risk management and compliance: Organizations must develop robust frameworks and strategies to address potential risks and establish contingency plans. This includes implementing data encryption, access controls, bias audits, and incident response procedures.

It's crucial to have processes for identifying, assessing, and mitigating AI-related risks, ensuring compliance with laws and regulations. For example, the GDPR mandates Data Protection Impact Assessments for AI systems handling personal data.

Stakeholder engagement: Engaging internal and external stakeholders ensures alignment with societal values. Initiatives like the IEEE's Global Initiative on Ethics of Autonomous and Intelligent Systems facilitate this.

From evaluation to implementation of an AI and data-driven transformation

Addressing these questions gives an in-depth understanding of whether an organization is ready to begin its AI journey and if the returns justify the investment. AI interventions done for the sake of applying AI will not only be cost-intensive, however the poor results they deliver will further impact the organization’s future AI and digital-journey.

Nagarro is working with leading global organizations and helping them on their transition to become an AI and data-driven transformation. With our AI workshops, organizations can leverage our consultative and innovative approach to s achieve an AI advantage. It all begins with a conversation; let’s talk!