{"id":10420,"date":"2023-07-18T10:47:12","date_gmt":"2023-07-18T08:47:12","guid":{"rendered":"https:\/\/escp.eu\/thechoice\/?p=10420"},"modified":"2024-11-25T15:46:51","modified_gmt":"2024-11-25T13:46:51","slug":"harnessing-ai-to-accelerate-digital-transformation","status":"publish","type":"post","link":"https:\/\/escp.eu\/thechoice\/choose-to-lead\/harnessing-ai-to-accelerate-digital-transformation\/","title":{"rendered":"Harnessing AI to accelerate digital transformation"},"content":{"rendered":"\n

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 A<\/a>I, which can create content that closely replicates human output.\u00a0<\/p>\n\n\n\n

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. <\/p>\n\n\n\n

But while everyone is talking about generative AI, the applications of AI for digital transformation reach far beyond the generation of text and images<\/a>. 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.\u00a0<\/p>\n\n\n\n

To help organisations navigate these pitfalls and unlock the value of AI in their digital transformations, we spoke with Maryia Dvaretskaya<\/a>, a Managing Consultant at Capgemini Invent, and a 2017 Master of European Business (now MBA in International Management)\u00a0 graduate of ESCP.\u00a0<\/p>\n\n\n\n

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Do not try and implement an AI solution for the sake of doing so, but rather start by analysing the actual business needs.<\/p>\nMaryia Dvaretskaya<\/cite><\/blockquote>\n\n\n\n

Realise the advantages of AI\u00a0<\/h2>\n\n\n\n

There are several key benefits that companies can expect from integrating AI into their digital transformation roadmaps, our expert tells us \u2014 starting with optimisation<\/strong>.\u00a0<\/p>\n\n\n\n

\u201cThe classic example is improving the efficiency and effectiveness of a supply-chain network, to reduce costs while maintaining a high level of service,\u201d 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 \u2014 ultimately achieving a better balance between supply and demand. <\/p>\n\n\n\n

AI can also generate entirely new insights that would otherwise have remained hidden in the data, our expert adds. <\/p>\n\n\n\n

Uncover latent insights<\/h2>\n\n\n\n

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<\/strong> \u2014 including preferences, purchase and browsing history, plus social media activity \u2014 to serve them up the most relevant options from a menu of customisations. \u201cWe can identify the best way to satisfy their needs,\u201d Dvaretskaya says.\u00a0<\/p>\n\n\n\n

In a similar vein, she reminds us of an example in life sciences, where AI has accelerated the drug discovery process \u2014 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. \u201cThus, AI helps optimise clinical development and thus, bring treatments to patients faster,\u201d our expert says. <\/p>\n\n\n\n

But there are challenges and barriers that companies commonly face when implementing AI in their digital transformation strategies. <\/p>\n\n\n\n

Amass diverse, high-quality data\u00a0<\/h2>\n\n\n\n

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<\/strong>. Garbage in, garbage out is the common expression.\u00a0<\/p>\n\n\n\n

\u201cWhen building the AI roadmap, it\u2019s important to ensure the data is accessible, that we have the right quantity and quality of data,\u201d she says. \u201cQuite often in an organisation, the data is scarce, siloed or incomplete. And it\u2019s 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.\u201d  <\/p>\n\n\n\n

The second major challenge is access to data scientists with the right skills and expertise to extract valuable insights<\/strong> from massive, complex datasets \u2014\u00a0especially when that is combined with other in-demand competencies, such as software engineering. \u201cThose 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,\u201d says Dvaretskaya.<\/p>\n\n\n\n

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When building the AI roadmap, it\u2019s important to ensure the data is accessible, that we have the right quantity and quality of data.<\/p>\nMaryia Dvaretskaya<\/cite><\/blockquote>\n\n\n\n

Outline precise business goals\u00a0<\/h2>\n\n\n\n

Prior to addressing the data and talent obstacles, however, it is vital for organisations to define the specific business objectiv<\/a>e behind their digital transformation efforts, according to her. <\/p>\n\n\n\n

\u201cDo not try and implement an AI solution for the sake of doing so, but rather start by analysing the actual business needs,\u201d she explains. \u201cThis 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.\u201d\u00a0<\/p>\n\n\n\n

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. <\/p>\n\n\n\n

Minimise bias in algorithms, boost transparency<\/h2>\n\n\n\n

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<\/strong> to become embedded in the data used to train AI algorithms, and subsequently replicated by the AI system. \u201cThere are some very well-known examples of biased data sets leading to discriminatory results,\u201d Dvaretskaya says, including some companies\u2019 \u201csexist\u201d hiring algorithms that showed bias against women. \u00a0<\/p>\n\n\n\n

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. \u201cSome of the AI systems are black boxes,\u201d she explains, adding the absence of transparency can result in individuals misinterpreting the results<\/strong>.\u00a0<\/p>\n\n\n\n

Additionally, Dvaretskaya warns against \u201cconcept drift<\/strong>\u201d, 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. \u201cIf the situation changes drastically, the results become false,\u201d she says, underlining the importance of continuously monitoring data for changes and updating models using new data.\u00a0<\/p>\n\n\n\n

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 \u2014 a strategic imperative.<\/h5>\n","protected":false},"excerpt":{"rendered":"

By 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.<\/p>\n","protected":false},"author":1,"featured_media":10421,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[13],"tags":[126,62,16],"class_list":["post-10420","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-choose-to-lead","tag-artificial-intelligence","tag-innovation","tag-strategy","category-13","description-off"],"acf":[],"yoast_head":"\nHarnessing AI to accelerate digital transformation - The Choice by ESCP<\/title>\n<meta name=\"description\" content=\"By addressing data quality, access to talent and more, companies can ultimately unlock the potential for AI to accelerate digital 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