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Generative AI adoption: from boardroom to execution

insight
January 12, 2024
9 min read

    Author

Nagarro Turntable_Speaker_Thomas Steirer

 

Thomas Steirer is Chief Technology Officer (CTO) at Nagarro. His focus is on developing scalable and sustainable solutions that are primarily designed to deliver valuable information.  

CIOs and CEOs are talking about Generative AI and see promising 7-9% growth within three years. Are you still hesitant about turning this intent into action? This blog gives you a 6-step roadmap to turn hype into reality and move Generative AI from the boardroom to execution. Learn how to build governance, amplify human capabilities, unleash data accuracy, and supercharge workflows— to outpace your competition by succeeding with Generative AI adoption. 

According to a McKinsey report, 96% of companies state that Generative AI is already on their boardroom agenda. Their outlook is quite optimistic: executives predict 7–9% improvements resulting from the adoption of Generative AI in just the next three years, and about one-fifth of global executives surveyed believe Generative AI will significantly disrupt their industry.

So, what about you? Are you still struggling with the risks involved, or have you already jumped on the bandwagon?

Let's imagine an enterprise that increases its success through the use of AI, where technology and human ingenuity converge in harmony. At Nagarro, we call this a Fluidic Enterprise. It puts people first. It combines human qualities like intent, creativity, and empathy with the power of AI. It emphasizes the responsible and ethical use of AI. The goal is for technology to work for people, not vice versa. How could this be you? 

In this article, we'll take an in-depth look at how you can implement Generative AI by taking the right measures to save yourself from the threats of inaccuracy, data insecurity, and inadequacy and make incremental progress towards becoming a fluidic enterprise.

Here are the top five caveats we frequently hear: 

  1. It's hard to pick a promising use case. 
  2. I don't have all the answers. 
  3. It's difficult to foresee the risks we are taking. 
  4. I can't accurately calculate the costs involved. 
  5. I don't have access to experts.  

While all of these concerns are valid, now is the time to start using Generative AI because the risk of missing out is even greater. Early adoption will help you lead, succeed, and adapt quickly to innovations. The choice: is between quickly becoming uncompetitive or leading today and being ready for the future.

Generative AI adoption— one step at a time  

How can your Generative AI adoption agenda move from the boardroom to execution and become a reality? The answer might be much simpler than you think.  

 

Step 1: Establish Generative AI governance  

Generative AI can be fallible and exhibit unpredictable behavior because it does not fully understand the context and nuances of human existence, sometimes makes limited sense, and it can not yet keep up with humans in terms of things like creativity and empathy. This technology is an innovation in progress yet to reach its apex.

Before we get into the potential benefits, the ideas and the basis for our exploratory mindset, let's talk about something really important first. The risks. The caveats. The concerns and doubts.

The biggest risks of adopting Generative AI are inaccuracy and ethical concerns. Imagine your supposedly smart AI solution making things up as it interacts with your potential customers. These unintended outcomes can have far-reaching consequences. Generative AI can be fallible and shows unpredictable behavior, as it does not understand the full context and nuances of human existence, makes limited sense at times, and is still not on par with humans in terms of things like creativity and empathy. This technology is an innovation in progress yet to reach its apex.  

Showcasing examples of Generative AI Fails Source: The New York Times Magazine

So, how do you deal with these concerns? The answer is governance and oversight.

Generative AI Governance ensures a responsible and safe adoption of GenAI by laying rules for ethical, transparent, and accountable practices. It incorporates accountability, fairness, transparency, and data security into its metrics and guarantees that the AI system is created and deployed in a way that evades risks for the business and benefits society.

According to McKinsey, AI will contribute up to 13 trillion dollars to the global economy by 2030. This can only happen if we set privacy standards and monitor AI development under the oversight of multiple stakeholders, from researchers, developers and executives. Each of them has a role in creating the right standard.

Why is Generative AI governance and oversight so important? According to ai-infrastructure.org, "One of the most shocking revelations is that most companies saw big losses with existing AI deployments when they failed to govern them correctly. Worst of all, the size of those losses is staggering. 20% saw losses of 50M to 100M. 24% saw losses of 100M to 200M, and 10% saw losses of 200M or more from failures to govern the right models and applications."

This means that it is paramount that these immense risks are thoroughly identified and controlled. As someone with several decades of experience in quality assurance with risk-based approaches, I believe this cannot be overstated. As AI use cases become increasingly intertwined with businesses, the business processes, identity— the potentials and risks will only become more extensive from now on.

How to build governance and oversight that has a currency in the longer run.


Balancing the multitude of possibilities and ethics is the core of Generative AI adoption, something that governance enables. However, building governance policies and rules is often a gradual process that only happens when you start working on technology.  

These are some measures that make a Generative AI governance plan relevant in the long run. 

Mindfulness

Objectives

Be clear about the overarching objectives and values of your AI implementation, e.g. transparency, accountability, fairness, privacy and security.




Ethics review board

An ethics review board or committee is responsible for assessing the potential ethical impact of AI projects— to ensure alignment with your defined values.




Incident response plan

A plan for dealing with ethical or technical incidents arising from AI implementation — an overview of how to respond, mitigate damage and avoid similar incidents in the future.

Inclusion

Cross-functional team 

Form a multidisciplinary team that includes experts from different fields such as AI research, ethics, law, policy, cybersecurity, and domain-specific expertise. This team will provide diverse perspectives and insights.




Accountability allocation 

Assign responsibilities to the various stakeholders involved in the AI implementation, such as the development team, senior management, ethics board and other relevant parties.




Guidelines and policies

Define guidelines and policies for AI implementation related to: Data collection, model training, deployment, user consent, bias mitigation, and accountability mechanisms.

Adjustment

Audit 

Audits and evaluates AI systems to ensure compliance with guidelines and policies. Identify deviations or risks that need to be addressed. 




Deep risk assessment 

Conduct a thorough assessment to identify potential risks and harms associated with your Generative AI projects. This includes analyzing the social, economic and legal impact of technology. 




Bias detection & mitigation

Implement measures to detect and mitigate bias in AI systems. Evaluate the system for any unintended biases from the training data. 




Continual learning & training  

Ensure your team stays up to date with AI ethics and technological developments through continuous learning and training. 

Measures that ensure Generative AI governance

Measures that ensure Generative AI governance

 

Step 2: Ensure Generative AI adoption is human-led

Ok, so there are quite a few things to consider when talking about governance. But there is also another aspect to consider: how do we, as humans, want to fundamentally approach our now guaranteed co-existence with this new type of technology?

By integrating Generative AI with human expertise, we can move towards the best possible outcomes. Generative AI becomes an asset that amplifies (not replaces!) human capabilities when used to supplement humans, resulting in an efficient and collaborative work environment. Human involvement brings intuition and empathy to GenAI's execution, which can ensure ethics and accountability.  

"Generative AI needs human supervision. Sometimes, you do not know what the response is based on or what parts of the training data are influencing the model. There is a possibility of bias and factual errors." Anurag Sahay. 

Don't replace humans; augment them with smart Generative AI Adoption. 

 

Step 3: Escalate productivity  

As a Test Automation person, automation and its potential have occupied large portions of my brain for many years. I see many of the same promises being made, albeit on a very different scale. I also see that while many of them are achievable, some of them are (as of now) a bit overblown – both in a positive sense (as in enthusiastic exploration) and in a negative sense (as in possibly deliberate inflation).

So let's take a look at what's what, at the end of the day:

Although programming involves a lot of monotonous and routine work, Generative AI can automate much of this work. You could easily automate tasks like gluing the components, reworking existing code, optimizing environments, orchestrating pipelines, etc. While a lot of work is ripe for automation and AI assistance, you need to identify where you employ these tools, auditing their impact and effectiveness.

Engineering leaders must get involved in building a structural strategy and avoiding security vulnerabilities. Human intelligence can help through performance bottlenecks, omissions, bad decisions, or mistakes that GenAI can cause.  

Task completion time using GenAI

Source: McKinsey & Company

This logic extends to content building, design and marketing strategies. There is much truth to the fact that Generative AI can be a powerful tool for marketers to quickly create autonomous and high-quality marketing content and develop and iterate fresh images from scratch. For instance, with the right prompts, you could generate a slide deck, talking points or meeting transcripts. But perfecting and personalizing it takes human intervention and skill.

 


 Will Generative AI spell the end of software testers? Read here.


 

Step 4: Gain real value from big, bigger, biggest data— with accuracy

Accuracy, "big/bigger/biggest data,” and value: these words crop up everywhere. And for good reason. Let’s explore these terms and look at what’s behind them, why they appear so frequently in the context of Generative AI – and what that means for its adoption.

Generative AI can radically change the way we analyze data to shape business realities. It overcomes human limitations, recognizes no bias (when trained on data) and has no fundamental limit to its speed. It can work tirelessly on unstructured data such as text, audio and images and deliver the result faster than ever before.

While AI can help, human domain knowledge is essential to create meaningful features that align with the specific problem you are trying to solve. GenAI’s data capabilities empower humans to get data classification, tagging and cleaning data, as well as generating new things. However, researchers and practical experience have clearly shown that (at least today) incidences of failure are not only possible but need to be expected and safeguarded against. GenAI’s efforts in the field of data analytics need human intervention in analyzing, testing and validating data for sensibility and accuracy.

This also means that you need to have a clear vision of what “an acceptable degree of accuracy” (I’m talking about the colloquial meaning here and not the clearly defined term in the context of machine learning algorithms) and “good quality” means to you.

GenAI-driving data often has ethical concerns, quality issues and computational complexity. It is also difficult to provide accurate estimates for the data they generate, which can be critical in certain applications such as decision making and risk analysis.

According to the MIT Technology Review, “chronicled a number of failures, most of which stem from errors in how the tools were trained or tested. The use of mislabeled data or data from unknown sources was a common culprit.”

Shaping and continuously improving an environment where humans and AI work together is crucial. Humans can guide AI, set priorities and make final decisions, while AI can assist in handling repetitive tasks and providing data-driven insights. To be successful, the roles of humans and technology must therefore be clearly defined.

Step 5: Supercharge your business user workflow

Optimistic tech leaders paint a rosy picture, even if it takes a lot of brainpower and courage to get there:

Generative AI implementation should relieve us of the repetitive, boring and inefficient aspects of operations, while enriching our ideas and shortening the path to their implementation. The key to simplifying internal processes and improving the customer experience is understanding how to manage a mix of human and machine-driven activities.

For instance, GenAI is great at: 

  • Improving customer experience by accurately analyzing and responding to a customer’s issues when integrated with a Customer Relationship Management tool (CRM). It can be incredibly helpful in aiding customers on their buying journey by helping them navigate through the options and reach where they want to. 
  • It can also quickly respond to emails or process legal documents or guides, helping us stay consistent and compliant, and increases accuracy and relevance – experts can now find data and relevant information much faster.  

However, Generative AI models might need more contextual understanding and can provide inaccurate information or unpredictable outputs. It needs human backing to deal with complex workflows that involve intricate problem-solving. GenAI may also need help to understand the tasks and providing insightful solutions. It needs human judgment and emotional intelligence to review and validate the generated content to ensure accuracy and appropriateness.

Step 6: Adopt now – learn & improve on the way

According to marketingtechnews.net, a recent survey has shown that most consumers are not interested in using AI as such. However, they do appreciate the increased convenience and other benefits that AI could bring. This underlines the need for user-friendly, useful and well-designed use cases that seamlessly integrate into the end user experience. 

Our experience shows that Generative AI is a technology and not a solution in itself - we first need to think about what we want and need and what we do not want and need. The approach to this question is key.

Even if you are unsure of the outcomes of Generative AI implementation, it's critical to start today with some thought-out use cases is crucial to build muscle with this new technology. Generative AI is a technology that you master by building it. You can only explore what AI can and cannot do for you when you start using it. The biggest practical questions for any business can only be answered when you start building. 

 

Start building today

With all of the above factors covered, you are ready to execute your Generative AI project, and the right time to start building is now. Harnessing the potential, enthusiasm and a quick fail/improve approach is key to making real progress in innovating in this space.

Depending on your strategy, being an early adopter could be a key advantage to staying ahead of the competition by significantly improving your operations, identifying new trends ahead of time, understanding customer behavior like never before, and closely analyzing the competition to develop competitive strategies that help companies gain market share.

This is a complex field: to accelerate the Generative AI adoption and orchestrate a scalable solution, it makes sense to turn to experts who can make it simple for you and help you build quickly and efficiently. Move faster with experts who can help you harness the transformative capabilities of GenAI to lead you to business growth and success. 

 


Read more about Fluidic Enterprise by Nagarro.


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