This Is The Future Of Education Thanks To AI And More

This Is The Future Of Education Thanks To AI And More

Now You KnOW features quick insightful takes on the issues of the day that matter most to you.

Weekend must read

The rise of artificial intelligence (AI), virtual reality (VR), and data-driven learning platforms has opened unprecedented opportunities for personalized learning, catering to each student’s unique needs and strengths. Smart education doesn’t merely incorporate technology into classrooms — it redefines how students learn and how educators teach.

Did you know?

14 major banks have partnered with private credit firms to distribute their loans in the past year.

This is up from two the year before, according to our estimates. Banks worldwide are under pressure from new regulations, such as increased capital requirements, driving another wave of disintermediation. The private credit sector is set for significant growth, especially in asset-based lending, as it adapts to shifting investment allocations. We explore the impact this will have on banks and asset managers.

This week's briefing

Accelerating insurance performance with motorsport insights

Behind the success of Scuderia Ferrari and Shell-V lies a story of successful technology implementation, teamwork, and performance optimization. The insurance industry can learn best practices from motorsport’s innovation and use of technology for delivering optimal performance, enhancing customer experience, and managing high-functioning, diverse teams. Behavioral science plays a role in how technology is adopted in both fields.

Bringing order to airline business

Imagine a system that simplifies airline reservations, delivery, and accounting systems, and enables new ways to conduct business. ONE order will soon transform airline retail operations across major airlines worldwide, bringing significant gains in voluntary servicing, revenue accounting, and partnerships with other carriers. We share three key learnings for airlines making the shift to ONE order.

Overcoming the promo challenges with AI

While negative return on investment (ROI) on promotions may not always be immediately apparent, there are a various symptom of poor promotional strategies. AI models provide the opportunity to review past promotional performances in more detail and simulate multiple scenarios. Companies can predict the promotion mechanism, consumer segment, discount level, and frequency that will generate maximum value given market conditions, consumer behavior, historical performance, seasonality, channel dynamics, and competitive landscape. We outline five strategies to effectively use AI in consumer goods promotions.

Oliver Wyman in the news

The?Wall Street Journal: Supply Chain Woes Carry High Risks, Big Rewards for Some Companies

CityAM: Keeping a check on AI Oliver Wyman’s CEO highlights the pitfalls of regulating AI

Financial Times: 'Bespoke’ private banking: the ultimate in customer service??

Bloomberg: Private Credit’s Banking Romance May Turn Sour

CNBC: We’re still in a ‘favourable environment’ for banks, analyst says

Reuters: China's private tutoring firms emerge from the shadows after crackdown?

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Michael Azulay

Product & Operations Executive | Marketplace Leader | ex-Uber

3 周

While using your historical promotional data for promotions should be a given, that only gets to the surface of where AI can be leveraged for that medium. Thinking more broadly, you should be taking that historical data and then mixing in public data - so market trends (is a product popular right now), weather forecasts (its hard to sell a winter jacket if the forecasts don't show cold weather in the near future), buying habits of complimentary products, etc. By only leveraging the proprietary data with your AI model, you're limiting the true impact and potential greater ROI that can be reached. cc: Contextual.io

Gracie Murphy

BA (Hons) Business Management, University of Leeds | Currently seeking Spring Week and Summer Internship opportunities in consulting for 2025.

3 周

Using AI for retrospective analsysis is smart - lots of repeatable data patterns to use for predictive forecasting.

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