Day 1 - 24 November 2020
Chair’s Opening Remarks: Enterprise AI Day 1
AI Coalition, Dutch Government
09:45AM - Day 1
Presentation: AI in the Dutch Government
• Introduction about National AI Coalition
• A guideline for the use of AI within the Public Sector
• Examples of AI projects within the Dutch Government
• Lessons learned so far
Presentation: Data Science and Machine Learning to make better business decisions
- Understanding the business need or problem that needs to be solved.
- Defining your KPI
- Data quality and governance, how to avoid ‘garbage in, garbage out’
- Testing and implementing the right algorithm
- Translating your insights
Panel: A sprinkle of AI fairy dust makes the Enterprise better
- Demystifying AI – what does it actually mean?
- Distinguishing the hype from the reality
- What can and can’t it do?
- What are the opportunities and risks?
Presentation: Creating an AI Powered organisation
- Achieving organisational change, changing mind-sets and attitudes
- Hiring and maintaining talent
- Developing employee skill sets and capability development
Presentation: The Cognitive Enterprise
- Extracting and gaining value from vast amounts of data
- AI as a digital assistant to enhance employee ability
- What business functions are being disrupted?
- Timescales for adoption, investment, scaling automation projects
- Real world examples and best practices
Panel: Clash of the Titans! Investing in a platform model – Build, buy or partner?
- Assessing the leading players; Amazon Web Services, Microsoft, Google, IBM
- How to assess what your business needs versus what each vendor offers
- How do you know which is best for your organisation? Do you need to budget for consultants?
- Should you use the same AI platform partner as your cloud partner?
- Benefits and lessons learned from cloud migrations when looking at AI cloud platform choices
Presentation: Humans and AI working together
- What will the impact of an AI based workforce be on the future of the human workforce?
- Cultivating a strong employee experience
- Constructing effective human-machine teams
- Making sure your organisation is customer led, insights driven and connected
Presentation: AI platform decisions – What to do with your legacy platforms and applications
- How to create an iterative process of legacy system modernisation
- Are there some workloads which will not budge – and if so what to do with them?
- How to get boardroom buy-in, from the CTO to the CEO – allaying fears around how long-term the project will be, potential disruption to business processes
- Lessons learned from cloud migrations and other digital transformation initiatives
Customer Engineering Manager
03:15PM - Day 1
12:40PM - Day 2
Presentation: Evocative applications of AI/ML within the enterprise
- Cutting through the hype and myths of AI
- A look at real-world industry applications
- Benefits of AI & ML technologies can bring to the Enterprise
Chief Data Officer
04:15PM - Day 1
Presentation: DataOps, the secret weapon for delivering AI, data science, and business intelligence value at speed
- According to recent research, just 7.3% of organisations say the state of their data and analytics is excellent, and only 22% of companies are currently seeing a significant return from data science expenditure.
- Poor returns on data & analytics investment are often the result of applying 20th-century thinking to 21st-century challenges and opportunities.
- Modern data science and analytics require secure, efficient processes to turn raw data from multiple sources and in numerous formats into useful inputs to a data product.
- Developing, orchestrating and iterating modern data pipelines is an extremely complex process requiring multiple technologies and skills.
- Other domains have to successfully overcome the challenge of delivering high-quality products at speed in complex environments. DataOps applies proven agile principles, lean thinking and DevOps practices to the development of data products.
A DataOps approach aligns data producers, analytical data consumers, processes and technology with the rest of the organisation and its goals
Panel: The challenge of operationalising AI
- Prioritising projects
- How do you get from lab to business process?
- Making your infrastructure AI ready
- Embedding AI into core business systems and processes
- Access to quality data.
Chair’s Closing Remarks: Enterprise AI Day 1
End of Session
Day 2 - 25 November 2020
Enterprise AI Day 2: Chair’s Opening Remarks
Presentation: From convenient to smart shopping with ML
- Behind the scenes look at Picnic’s deep-learning based behavioural analytics and prediction engine
- A walk through the ups-and-downs of product, category and promotional recommendations of FMCGs
- Learning from the failures along the way.
- Predicting customer buying patterns with >95% likelihood the top 12 articles of the next order of each of our customers.
Presentation: Building a CX strategy
- AI solutions to better understand your customers in real-time
- Analyzing customer feedback and sentiment at scale using NLP and NLU
- Mapping the customer journey
- Tracking and measuring success
Panel: AI for customer experience
- How to leverage AI technologies such as NLP and Image recognition to get more value to customers with less expenditure?
- How to gain customer’s trust
- The next generation of conversational AI
- Predictive Intelligence for predicting customer behaviour
Chief Data Scientist
11:45AM - Day 2
Presentation: Data, AI, and connecting with customers
- How can the growth of rapid data sources, combined with advances in data science techniques help business build better customer experiences?
- Commercial applications of technology versus work in the academic setting
- Examples from Aviva’s digital transformation journey
Presentation: Case study – An intelligent Automation Journey
- Evaluating which tasks to automate first. How to achieve time-to-value
- How much will you need to invest?
- How do you get organisation support and by in?
- Scaling Intelligent Automation across the Enterprise
- Lessons learnt along the way
Panel: Building Ethical AI Frameworks
- The importance of transparent and accountable AI – how did the algorithm reach the outputted decision?
- Avoiding bias in decision making systems and computer vision. How to avoid entrenched existing bias.
- Data Protection and privacy rights. Can you apply AI learnings to different data sets?
- Who is responsible when things go wrong? Who bears the risk?
- Developing a set of policies and values to ensure safe AI that can benefit your business
Jeroen Van Genuchten
Global Product Owner Robotic Process Automation
02:15PM - Day 2
Presentation: Becoming an Intelligent Enterprise
Using technologies such as RPA, Conversational AI and Machine learning to:
- Deliver exceptional customer experience
- Empower employees
- Drive new revenue streams
- Make better products
Presentation: Customer insights with NLP and ML
- How can you leverage NLP and ML techniques to maximise your customer’s lifetime value
- Refining high-volume data to gain deeper insights into attitudes and behaviours
- Scaling personalisation with real-time behavioural signals.
Dr. Krishnan Ganapathy
Vice President, Engineering of the US R&D Centre
03:15PM - Day 2
Case study: Leveraging Multi-language conversational commerce to reach over 100m Consumers
- How is Flipkart using cutting edge AI/ ML techniques to assist users in the discovery, research and purchase of products.
- Strategies for implementation
- Improving response rates and growing customer loyalty
- What were the successes and failures?
Panel: The Future of Enterprise AI
- What business functions are being disrupted by AI and which will be next?
- What challenges and opportunities do technologies such as predictive analytics, anomaly detection bring?
- The increasing role of AI for cybersecurity
- Using AI to improve productivity and enhance human creativity
- What do leaders need to think about?