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MORE BUSINESS with
COMPETITIVE AI & ECOSYSTEM PARTNERS
Ladies & Gentlemen
Start Your Engines!
IF AI IS THE ANSWER, WHAT WAS THE QUESTION?
Align your AI strategies with your business strategies
Big opportunities and manageable risks
The major decision for the futures, with a little help from data analytics
Dr. Johan Himberg, Chief Data Scientist, Reaktor
BUSINESS, AI, AND VALUE - CROSSING THE CHASMS
- What AI can’t do, but you can
- Optimise or disrupt - identifying value
- Why crossing the chasms between business, IT, and data is vital - and only a start
- Habits of value creating AI teams, experiences from real cases
HANDLING AND PRIORITISE THE ECOSYSTEMS WITH YOUR AI PARTNERS
· As data becomes more business-critical, its presence expands in the C-suite
· Laws and consumer rights that push companies toward transparent AI
· The needs to control AI
- Harnessing the power of AI and other intelligent automation technologies
- The goal of react in real time with model-based tools on an enriched and structured environment
- Using exponential technologies at scale to get value out of your data
FROM CONCEPT TO REALITY
Overcoming the challenges
- Rapid experimentation and “fast fail” the building blocks for long term success in AI
- A healthy dose of pragmatism and expectation management keys to successful implementation
- Imperative that organizations define their overall solution approach that includes AI & ML
- The business value realization organization wide absolutely crucial for the journey
- Accounting for unexpected value – you must often trade exactness for actionable results. Simulate the models, then set the goals
- When to be flexible with assumptions, when it’s appropriate to deviate from the textbook and the importance of empathizing with your stakeholders.
- Scout the area with pilots, test hypotheses and gain the knowledge
IS YOUR CURRENT BUSINESS MODEL FIT FOR THE PURPOSE?
- Can technology revolutionise your business model?
- Understand how AI and automation can unlock new revenue streams
- Data and insights are the foundation to better decisions - do you have the info needed to make the right changes?
OPERATIONALISATION WILL BE THE NAME OF THE GAME
- 3 different size success stories
- Demystifying AI implementation
- Overcoming project mistakes and clarifying lessons learnt
HOW TO ENSURE THE ROI OF AI DEVELOPMENT
Case: China the Leader of AI in 2025?
- Ability to keep pace with all leading AI technology
- Competitive advantages
AI x POLICY
Looking ahead at how external stakeholders and governmental regulations will influence your data strategy
- transferring and accessing data across borders, given different jurisdictions/laws/regulations
- EU/US/China – what’s next in tech policy? How will it change tech investments and innovation? What does it mean for private companies?
- Ethical Intelligence: What are Your Technical Morals?
AI AND AUTOMATION IS CLAIMED TO CREATE MORE NEW JOBS THAN THEY DESTROY
- Is AI the new employee? Managing the evolving workforce landscape
- Driving AI adoption in the workplace
- Normalcy of the Hybrid-Workforce — Human and AI Co-operation
INVESTING IN EMERGING TECH
MAKING THE BUSINESS CASE TO BACK UNPROVEN TECHNOLOGY
- How can you evaluate technologies that are not yet proven?
- Being a pioneer holds risk – how can you mitigate it?
Q & A
Welcome & Registration
EXCLUSIVE AI STRATEGY TABLES
First Come, First Served - Super-charge your strategy
- prepare for the future, build in culture for change into the strategy
- can one create your own CoE on a shoe string budget?
- don’t miss opportunities and chase the right projects
Demystifying AI implementation
- the secrets to successfully delivering complex projects with AI
- comparing the management of new AI initiatives in an online environment
- lesson learnt and hints on execution
How to build ethical, AI-augmented organizations people want to work for?
- What does good AI governance practice look like? Who is accountable?
- Legal, regularity and security frameworks. The matter of privacy.
- Considering data related risks.
- How can you create a culture of cross-functional, integrated ethics and compliance? Can a machine answer ethical questions?
Data Strategy: The fuel of your AI engine
- the need for clean data in your AI roadmap
- reducing the barriers to entry – making data accessible to your teams
- data migration strategies
- turning traditional players into an innovator
Key areas for AI deployment – a step-by-step approach
Structure & Momentum
Workforce & Buy-in
Trust & Data
Development & Convergence
Leadership structure – digital transformation starts from the top
- the evolving role of business leaders – what has changed for a CxO?
- creating the right mix in the leadership team - who do you need in the room?
- balancing the quick wins against long-term success
Architecting AI-powered organizations from the bottom up
- making AI explainable and trust-worthy to bring on board your stake holders
- installing trust and transparency to win over the hearts of your customers
- gain more business buy-in and drive better AI product uptake
Implementing an effective business-wide Data-Strategy
- making data a key corporate asset and analytics a humanized product
- see market dynamics more clearly, understand cause and effect
- transform the business while creating a better relationship with your customers
All Personalized Everything
- New Data Intelligence Drives CX-innovation
- AI can make the experience more personalised
- Enhance the customer experience online, on apps and websites
Freeing your workforce with intelligent automation
- identifying tasks you can automate and where to start
- streamline your processes and make decisions quicker
- A Robotic Coworker – Friend or Foe?
NLP | Chatbots, voice assistants and feedback machines
- what is new about robot assistants
- how are they increasing customer satisfaction in the future?
- are they a substitute or a support pillar?
- Backend or Conversational NLP. Which one better for a Business Boost?
SaaS, IaaS, PaaS, FaaS – Where do the XaaS acronyms fit in?
- what are the critical differences in “As a Service” - models
- can you create innovative revenue
- how does it work and how could it be effective or benefit you