We’re t-minus five weeks away from January 1. Which means t-minus five weeks away from New Year’s Day, when conversations and social media feeds will be inundated with talks of resolutions — losing weight, exercising more, reducing screen time, getting that promotion, putting more money aside for savings, starting a meditation practice — you get the idea.
But in order to be successful in those endeavors, the resolution needs to be SMART. It’s an acronym coined in the journal Management Review in 1981 for Specific, Measurable, Achievable, Realistic, and Time Bound. How can you apply the SMART framework to your goal of scaling AI in 2023, despite attempting to do so in the past and always hitting the same roadblocks? We’ll tell you:
1. Specific
To be absolutely clear, you can’t just say “My goal is to scale AI across the organization in a governed and responsible way.” You need to be explicit about what pain points keep rearing their heads (such as issues with data quality and access) and be as detailed as “My team struggles with siloed or duplicated data sources, which leads to a lack of trust in data products. My goal is to resolve that problem in order to, then, be able to scale analytics and AI on a broader scale.”
2. Measurable
How are you going to track value creation and wins as part of this massive goal? One recommendation is to assign an owner of value tracking to coach and evangelize on the value of analytics and AI for the organization, coordinating overall efforts. This person (or team) can share updates and reports to build momentum and show how well the organization is doing with the scaling effort.
3. Achievable
Just like aligning people, processes, and technology is a matter of time, so is the journey to reaching Everyday AI. In fact, scaling AI is just that — more of a journey than a destination. To help, though, all of those aforementioned elements has a critical role to play:
- People: There needs to be an institutional capacity to learn from data, collaborate internally, and strike the right balance between automation and maintaining a human in the loop.
- Processes: Teams need to be able to synthesize and consume information in a timely fashion to extract insights from overwhelming volumes of data.
- Technology: Technological capabilities need to deliver insights at enterprise scale as the foundation of automation and autonomy. The right platform will allow massive consumption of data, operationalized model deployment, and the ability to bring together experts and non-experts in a common place.
What can organizations expect heading into 2023? Check out insights in the short video above from Dataiku's Shaun McGirr, Jed Dougherty, Claire Gubian Sommain, and Conor Jensen.
4. Realistic
What results can actually be achieved given available resources? Despite any economic flux, organizations should absolutely still invest in AI and, to succeed, need to start addressing the issues that are the catalyst behind a lack of tangible business value and ROI. Time and time again, we hear organizations face blockers in the same five categories (listed below), so we recommend prioritizing whichever ones your organization faces most frequently:
- Data access and quality issues
- Lack of operationalization and business impact
- Lack of visibility and control
- Scarce and underused data experts
- Costly and complex infrastructure
5. Time-Bound
We believe 2023 is the year where organizations are going to double down on solving these ever-present challenges in order to successfully scale AI, so be sure to set up smaller, intermediate goals along the way. Mastering AI won’t happen without setting up AI capabilities in the right way and in the shortest amount of time (without, of course, it being a rushed effort where certain steps are compromised along the way). In addition to the right operating model that fits the organization’s composition, teams must be dedicated to lead the way and implement change management.
So, why are we qualified to equip organizations with best practices on integrating technology, people, and processes with AI at scale? At Dataiku, we believe that instead of focusing on one problem, from data prep to AutoML to cloud strategy, organizations should aspire to solve all of their challenges together. And to do this, they need a systemized approach to AI that requires:
- Empowering all people, including the business, in a central place
- Accelerating the time it takes to deliver AI projects from months to days
- Governing the lifecycles of all AI projects
Along with featured takeaways from our friends at Snowflake and Slalom, Top New Year’s Resolutions for Data, Analytics, and AI highlights practical how-tos for getting started solving the five core scaling AI challenges in order to increase productivity, improve decision making, innovate products and services, and respond to market changes faster than ever.