Digital transformation is no longer a choice but a necessity for companies today. The ability to harness digital technologies and leverage data-driven insights to improve operational efficiency, and foster innovation through the creation of novel products and services, is crucial. One core driver of digital transformation is artificial intelligence (AI), including generative AI, which can create content that closely replicates human output.
It not only accelerates content creation, but can enhance marketing efforts, and enable more personalised customer experiences. With its ability to analyse vast amounts of data, automate processes and deliver valuable insights in a conversational format, generative AI is set to change the way organisations embark on their digital transformation journeys.
But while everyone is talking about generative AI, the applications of AI for digital transformation reach far beyond the generation of text and images. In this article, we are going back to the basics to look at how to harness the benefits of AI in all its forms. However, along with these opportunities come inherent risks. AI systems of any kind are susceptible to biases. Moreover, the ethical implications of AI, such as the impact on transparency and accountability, require careful consideration.
To help organisations navigate these pitfalls and unlock the value of AI in their digital transformations, we spoke with Maryia Dvaretskaya, a Managing Consultant at Capgemini Invent, and a 2017 Master of European Business (now MBA in International Management) graduate of ESCP.
Do not try and implement an AI solution for the sake of doing so, but rather start by analysing the actual business needs.
Maryia Dvaretskaya
Realise the advantages of AI
There are several key benefits that companies can expect from integrating AI into their digital transformation roadmaps, our expert tells us — starting with optimisation.
“The classic example is improving the efficiency and effectiveness of a supply-chain network, to reduce costs while maintaining a high level of service,” she says. AI algorithms can analyse historical data, market trends and customer behaviour to generate more accurate demand forecasts or identify the pitfalls in existing processes. This helps optimise inventory levels, production planning and distribution — ultimately achieving a better balance between supply and demand.
AI can also generate entirely new insights that would otherwise have remained hidden in the data, our expert adds.
Uncover latent insights
Consider hyper-customisation, or the ability to create products, services or experiences that are tailored to the needs and wants of individual consumers. AI systems can pour over customer data — including preferences, purchase and browsing history, plus social media activity — to serve them up the most relevant options from a menu of customisations. “We can identify the best way to satisfy their needs,” Dvaretskaya says.
In a similar vein, she reminds us of an example in life sciences, where AI has accelerated the drug discovery process — by identifying therapeutic targets and drug candidates, as well as predicting the efficacy and safety of the latter. AI algorithms can scan through chemical, biological, and clinical data in order to estimate how a patient may respond to a certain drug candidate. “Thus, AI helps optimise clinical development and thus, bring treatments to patients faster,” our expert says.
But there are challenges and barriers that companies commonly face when implementing AI in their digital transformation strategies.
Amass diverse, high-quality data
Firstly, to build robust and accurate AI models, Dvaretskaya points out that a substantial amount of diverse and high-quality data is required. This data serves as the foundation on which AI algorithms are trained to spot patterns, understand relationships and make predictions or even decisions. Without high-quality data to feed these machines, they cannot deliver valuable insights. Garbage in, garbage out is the common expression.
“When building the AI roadmap, it’s important to ensure the data is accessible, that we have the right quantity and quality of data,” she says. “Quite often in an organisation, the data is scarce, siloed or incomplete. And it’s complicated to get the full picture because the data is spread between different functions or difficult to complete with external sources. So it is vital to get the data foundations right and make data compatible.”
The second major challenge is access to data scientists with the right skills and expertise to extract valuable insights from massive, complex datasets — especially when that is combined with other in-demand competencies, such as software engineering. “Those profiles are crucial to be able to not only create a proof of concept but actually bring to life new processes, products and services using AI,” says Dvaretskaya.
When building the AI roadmap, it’s important to ensure the data is accessible, that we have the right quantity and quality of data.
Maryia Dvaretskaya
Outline precise business goals
Prior to addressing the data and talent obstacles, however, it is vital for organisations to define the specific business objective behind their digital transformation efforts, according to her.
“Do not try and implement an AI solution for the sake of doing so, but rather start by analysing the actual business needs,” she explains. “This way you will naturally prioritise the solutions that have the biggest impact, and they are more likely to be accepted by everyone in the company.”
She adds that by setting clear objectives and a step-by-step process to achieving them, you can improve the overall efficiency of the project.
Minimise bias in algorithms, boost transparency
Additionally, our expert identifies two primary ethical considerations linked to the use of AI in digital transformation. One concern is the potential for human bias to become embedded in the data used to train AI algorithms, and subsequently replicated by the AI system. “There are some very well-known examples of biased data sets leading to discriminatory results,” Dvaretskaya says, including some companies’ “sexist” hiring algorithms that showed bias against women.
The second concern is that the algorithms and decision-making processes used by the AI system are not easily explainable or interpretable by the humans using them. “Some of the AI systems are black boxes,” she explains, adding the absence of transparency can result in individuals misinterpreting the results.
Additionally, Dvaretskaya warns against “concept drift”, when the patterns in the data being modelled by an AI system evolve over time. This can mean the assumptions made by the system no longer hold, leading to inaccurate or unreliable predictions and decisions. “If the situation changes drastically, the results become false,” she says, underlining the importance of continuously monitoring data for changes and updating models using new data.
Through addressing these ethical concerns and overcoming challenges around data access and quality, access to talent and skills, as well as taking systematic approaches to AI, companies can ultimately unlock the potential for this technology to accelerate digital transformation — a strategic imperative.
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