Are you AI-ready? You need to be. Get ready to level up your marketing strategy with AI and how to make sure your next infrastructure move helps you exploit the AI advantage — and more — when you join this VB Live event.
Artificial intelligence technology and applications for marketing have evolved to the point where most businesses can leverage them to gain insight, grab customers, and grow fast. The challenge isn’t finding a tool to dramatically improve your marketing strategy — it’s actually implementing that technology into your current infrastructure. It might be robust right now, but is it robust enough for terrabytes and terrabytes of deep learning, problem-solving, reasoning, and learning?
So what’s the next step? Do you need to start developing in-house, or should you be looking at VARs, systems integrators, or consultants? Deployment on the premises, or shoot for the cloud? Is your existing infrastructure powerful enough, or do you need to go the full new-servers route?
Here are some of the most important factors to consider as you look at scaling your organization’s capabilities.
The strength of your artificial intelligence strategy all comes down to your data — not just how good it is, but how much you’re able to capture. That means data storage needs to be top priority. That includes not just your actual capacity, but I/O, reliability, and latency. When calculating just how much and what you need, think about how big you’re going with your AI investment, and whether you need to be relying on real-time analysis, or using post-processing.
AI applications also generate massive amounts of data as they learn, and the database will just keep growing, so scaling your storage isn’t something to think about later, but needs to be planned for right from the start.
How good is your network?
Successful AI requires high efficiency networks that are high bandwidth, low latency, and again, easy to scale as your database grows in size and complexity. And to keep up with that complexity, you’ll also need to consider automating infrastructure management as well as implementing tools that anticipate network demands in real time.
How good is your computing power?
Multiple large datasets and complex algorithms, especially ones that keep expanding and learning, are going to outgrow a CPU-based compute environment fast. For uninterrupted service and efficient processing, it’s time to look at either upgrading to GPUs in your data center, harnessing the power of the cloud, or figuring out a cost-effective, easy to manage and run hybrid solution.
How good is your training?
As AI tools come flooding into the mainstream, they’re getting easier to use — you don’t need to be a data scientist any more to implement an AI strategy and use it effectively. But there are AI-specific best practices around machine learning, natural language processing, and deep learning. Plus some marketers still resist turning from the “trust your gut” model of marketing strategy and embracing the “look at the data” tactic. Implementing AI into your workflow means training, skills development, and a solid grasp of what you hope to achieve, and what you should expect to see.
AI isn’t quite plug and play yet — but it’s delivering the kind of real-world results that make the challenge of implementing AI tools and technology into your organization both worthwhile and surmountable. To learn more about what it means to be AI-ready, how to get to that point, and how to launch a successful AI strategy, don’t miss this VB Live event.
Don’t miss out!
Attend this webinar and learn:
- How to tell if your marketing and IT departments are AI-ready
- The fundamentals of an AI-driven infrastructure
- The role of clean, definable data goals in successful AI implementation
- How to scale the AI workload
- Rolf Schromgens, CEO, Trivago
- Sherif Mityas, Chief Experience Officer, TGI Fridays
- Michele Goetz, Principal Analyst, Forrester
- Andrew Malinow, PhD., Principal Data Scientist, Zylotech
- Rachael Brownell, Moderator, VentureBeat
Sponsored by Zylotech