The case for intelligent AI platforms
In order for businesses to prevail in our new AI-driven world, they will need to fundamentally change how they think about and use technology to improve how people and machines work together.
What’s needed for success is a scalable AI platform that enables companies to unlock the value of their data. An intelligent AI platform automates the complex task of preparing, cleansing and distilling the data that really matters. It separates signal from noise in the mountain of data businesses are sitting on, empowering teams across your organization to take action and make better-informed decisions that impact the bottom line.
Successfully creating a data-driven culture
The road to a comprehensive AI platform can be tricky, especially without the right experience to guide you. As your business defines its path to digital transformation, you need to be realistic about the potential challenges you'll face along the way. In our work, we’ve seen first-hand the roadblocks companies face that can make or break their digital plans. Organizations that are successful in embracing the digital revolution have alignment from key stakeholders on the following elements:
- The business use cases for an AI platform
- How quickly they want the platform to be up and running
- The amount of time and work it will take to build and/or implement new digital tools
- A rollout plan and change-management process in place
- The right team members to build digital solutions and/or an understanding of the talent gaps they would need to fill
- The ability to build or implement solutions that are flexible enough to adapt quickly to the ever-changing technology landscape
- Only after every decision-maker in your organization is aligned on the above issues can you then begin to explore your options.
Weighing your AI platform options
There are three main options for implementing an AI platform: build your own from scratch, assemble and integrate disparate tools, or deploy a solution. Depending on your organization's business goals and talent mix, one of these options will work better than the rest:
- A DIY, build-your-own solution is great for companies that have a team of in-house data scientists, engineers and developers to create a custom platform from the ground up. While significantly more difficult to achieve, this approach works for businesses that have access to the right talent and wish to have more control over the experience their customers have with the platform.
- If you have a reduced budget or a smaller team of engineers and data scientists, you can opt to piece together disparate tools to form the infrastructure your team can build on top of. While this approach is faster than your own, it can still be difficult to implement successfully since you’re at the will of the capabilities of the tools you purchase.
- If you don’t have the right talent — or the time to wait to build your own, or to integrate disparate tools — you can opt to purchase and deploy a Platform-as-a-Service (PaaS) solution. This option offers the fastest speed-to-path and will automate the most tedious parts of data wrangling for you. Technology providers take care of hosting the infrastructure and storing your data for you. You also gain the benefit of continual improvements as the technology provider advances its product roadmap with new features and functionality that are made available to you.
We’re living in an AI-first world — a world where industry is being disrupted with data, AI and machine learning. The businesses that act fast and make the right choices now will enable a new wealth of opportunities and position themselves as leaders of a new generation of industrial growth.
Interested in learning more about successfully creating a data-driven business culture? In our white paper, Choosing an AI & IoT Platform Strategy for Your Business, we take a deeper dive and analyze the risks and tradeoffs associated with the three options for implementing an AI platform.