6 min. read

ai.lcy – event recap

It’s no exaggeration to say that every problem today is an AI problem. But while AI is a critical component of our problem-solving approach, it’s no longer enough to overcome these challenges at scale. We now have a new recipe to solve complex challenges at scale & drive action –  AI combined with Engineering and Design.

Our ai.lcy event in London last week focussed on helping businesses drive better decisions when operating at scale. The event was attended by FTSE 250 attendees from a wide-range of industries including CPG, financial services, healthcare, insurance, telecom, retail and more. Speakers from companies including Mars, Visa, Google, Lloyds Banking Group and M&G Prudential provided their insights to 60+ attendees, as well as members of the media. They also experienced AI products and services in the exhibit showcase area. Here is a brief top line summary of each session:

  1. Keynote – How AI revolutionises business strategy – Kenneth Cukier (Senior Editor, The Economist)
    Businesses need to think of data as a new factor of production. The more data we collect, the more we can do with it and the more we can produce. Because we can now apply artificial intelligence (AI) to different problems, businesses are able to learn things they couldn’t before. They can maximise new opportunities and create new values (jobs, services, production, sales). But how an organisation ‘frames’ the problem it’s trying to solve through AI is becoming increasingly important. AI is at it’s very best when forecasting and making predictions. As a result, businesses need to stop considering the problem as “humans vs. machines” and instead make everything a predictions problem. That needs to be at the core of their AI strategy.
  2. Keynote AI is not enough – Srikanth Velamakanni (Co-founder and Group Chief Executive, Fractal Analytics)
    AI is becoming ubiquotus – all problems will be reframed as AI problems & at the core of everything that we are doing around the world of  AI is a behavioural problem. When we think of it as humans and machines as opposed to humans vs. machines, and balance between driving forces and restraining forces, we power decisions that make real progressAnd AI alone is not enough to solve problems at scale. Machines are scalable, but so are their errors. As simple automation isn’t always the solution, a deep understanding of human behaviour is needed. Therefore, a combination of AI, engineering and intelligent design is needed to solve problems at scale. To maximise the benefits and efficiencies of AI, businesses must combine their solutions with their staff. Simply having algorithms that outperform humans isn’t enough, you need to marry super intelligence to experience. Machines solve problems, humans make sense of things. They have the ability to adapt to a new environment, to a new set of circumstances very quickly. Machines alone don’t possess that adaptability
  3. How to use AI to hack tricky problems – Abhijit Akerkar (Head of Applied Sciences, Business Integration, Lloyds Banking Group), Priyank Patwa (Head of AI & ML, M&G Prudential), Rahul Desai (Client Partner, Fractal Analytics)
    To solve tricky problems, we need to understand human behaviour. To improve the overall experience – which should be the goal of any business – businesses need to consider the motivating factors that drive customer decision-making. And then we need to ask ourselves: can we predict the next likely event in the customer’s journey and power the “next best action” for the customer? To do this, we need to appreciate that short-, medium- and long-term actions have consequences on the customer journey. So, we need deep learning to extract customer patterns (and memory) from the journey so that we can craft a model that propels that person to the next best action (and deep learning-based models outperform traditional models across various scenarios). We need to change the decision-making journey by making sure that the right information and the right insights are available to the right person at the right time.
  4.  How to solve problems at internet scale – Linden Glen (Digital Transformation Director, Analytics & Data, Mars), Arpan Dasgupta (Client Partner, Fractal Analytics), Sameer Dhanrajani (Chief Strategy Officer, Fractal Analytics)
    Business should start with a user-centric approach: find out the problem of specific users, use design thinking to uncover the problem and why it needs to be solved. Once this is done, we must then consider how we should use analytics and AI to solve these problems. The final step is looking to find out how to scale a solution. But all starts with understanding what the initial problem is before thinking about what data and technology is needed.It is the business’ goal/aim – whatever it is that it’s trying to achieve – that will inform the type of AI solutions and computational architecture that it crafts and deploys. At the end of the day we’re talking about a sea-change in personal preferences and so now we have to make a change in business processes.
  5. How to make it work with design – Pranay Agrawal (Co-founder and Chief Executive Officer, Fractal Analytics)
    To fully unravel human behaviour, we need to go beyond data. We need to ask why people take the actions they do? And businesses need to go beyond what the data shows them. Behaviour is driven by a wide variety of emotions, desires, factors and influences, some conscious and some unconscious. The two key factors in any behavioural change situation/scenario are the driving force and the restraining force. These need to be examined and understood so that we can design solutions for non-conscious behavioural change. So, we need to not only get a better understanding of behaviour but also context. Because context alters human behaviour. To identify the problem, we need to analyse the behaviour. To solve it we need to understand it.
  6. The new recipe and it’s magic – Ben Neffendorf (Joint Data Science Lab Delivery Lead, Visa), Eleonora Kourtzi (Product Marketing Manager – Digital Growth Lead, Google), Martha Bennett (Principal Analyst, Forrester), Natwar Mall (CEO, Cuddle.ai)
    To build and implement AI solutions correctly, organisations should pay special attention to the people involved. It’s the people who gather the data, people who select which data goes into the model and people who design the algorithms. All the problems that businesses are trying to solve are problems of AI, design and engineering. Through the right combination of all three, we can help business leaders reimagine their business through new technologies. Improving the overall experience for the customer and generating high-quality results.

Through ai.lcy, we have challenged some of the assumptions around AI and big data practices, while delivering insightful and thought-provoking sessions on what businesses need to do to apply AI efficiently and scale it effectively. To be successful, organisations need to understand the driving forces behind customer behaviour, the context of that behaviour within the customer journey and the problems that need solving. For businesses to get the best results and deliver the best possible experience, they need a combination of AI, engineering and design.