Preparing for AI? if you’re not focused on the broader organization — chiefly people, process, and principles — you won’t just stunt your capacity for good AI, you risk sunk investment, lost employee trust, brand backlash, or worse. To learn how to go beyond data and secure the five fundamentals of AI readiness, don’t miss this VB Live event!
Step away from the data. Because while the real transformative impact of AI on your business relies on having enormous mountains of clean, well-sourced, relevant data and the best data scientists, it takes far more than perfecting your data and infrastructure to prepare your company to seize the advantages that AI offers, and deploy sustainable, effective machine and deep learning programs.
But even if you shift your focus from data as the key to an AI strategy, where do you actually begin? You’re not alone. Almost 85 percent of businesses are ready and willing to deploy an AI strategy, but just haven’t. In fact, less than 35 percent of those enterprises even have an AI strategy up and ready to be launched.
Research and advisory firm Kaleido found that the most common stumbling blocks to deployment do sometimes lie in data and technology, but talent plays a key role as well, with people at all levels of the company resistant to change and unwilling to participate in company-wide initiatives. So if you want to achieve the benefits of AI, you can’t just pour all your time and attention into creating the platform; you need to ensure that your company is prepared, first, from top to bottom. And that means broadening your scope to look carefully at five critical areas.
1. Strategy
AI-driven transformation begins with ground-up problem-solving, but must be supported by a foundation of governance and aligned with business objectives and enterprise data strategy. While approaches and metrics vary by organizational maturity, customer experience is always true north.
2. People
Preparing people for AI is as important as preparing data, and it is essential for businesses to prioritize human factors over technological capabilities. Instill the “AI Mindset” across myriad stakeholder groups; foster lockstep coordination between technical and product, and address AI’s limitations and cultural stigma head on.
3. Data
Data preparedness is not a linear destination. AI data readiness requires organizations to address their broader data strategy and orchestrate data pipelines and resources for ongoing enterprise learning and evolution.
4. Infrastructure
Decision-making around the technical architecture and integrations required to deploy AI must align with core product strategy, balance reliability with flexibility, and account for rapidly evolving AI software, hardware, and firmware.
5. Ethics
The mass automation of big data and AI call for a new business competency: a formalized approach to organizational resources, bias assessment, transparency, and ethical preparedness.
To learn more about these five key areas, and how to address each of them in order to establish a robust and powerful AI implementation, plus hear some real-world case studies and cutting edge research, don’t miss this VB Live event.
Don’t miss out!
Attend this webinar and learn:
- What you need to do to prepare for AI — beyond the data science team
- Real-world examples and research findings
- Top five best practices for strategic AI implementation
Speakers:
- Jessica Groopman, industry analyst and founding partner of Kaleido Insights
- Nathan Decker, Director of eCommerce, evo
- Ken Natori, President, Natori Company
- Rachael Brownell, Moderator, VentureBeat
