By Harry Kabadaian, CEO of Fancy Lab, a digital marketing agency Artificial intelligence (AI) isn’t merely “important” to innovation and basic processes at the organization of the future, it’s indispensable. To thrive in that future, businesses already are in early-stage explorations to transform into AI-driven workplaces. But despite the high interest level in leveraging AI in business, […] 
By Harry Kabadaian, CEO of Fancy Lab, a digital marketing agency
Artificial intelligence (AI) isn’t merely “important” to innovation and basic processes at the organization of the future, it’s indispensable.
To thrive in that future, businesses already are in early-stage explorations to transform into AI-driven workplaces. But despite the high interest level in leveraging AI in business, implementation remains quite low. According to Gartner’s 2018 CIO Agenda Survey, only four percent of Chief Information Officers (CIOs) have implemented AI. The survey report is careful to note we’re about to see more growth in “meaningful” deployments: 46 percent more CIOs had made plans for AI implementation by February, when the report was published.
t won’t happen instantly. First, you must understand your business in terms of goals, technology needs and the impact its adoption will have on employees and customers. Plenty can go wrong as you address any of those points. Here are a few tips to help achieve minimum resistance.
1. Treat AI as a business initiative, not a technical specialty.
Many organizations view AI’s implementation as a task for the IT department. That mistake alone could give rise to most of your future challenges.
AI is a business initiative in the sense that successful adoption calls for active participation throughout the process — not simply when it’s deployed. The same people currently responsible for running daily business processes must have real roles to help build and maintain the AI-driven model.
Here’s how it looks in real life:
- The organization requires collaboration and support from data scientists and the IT team.
- IT is responsible for deploying machine-learning models that are trained on historical information, demanding a prediction-data pipeline. (Creating that pipeline is a process unto itself, with specific requirements for each of the multiple tasks.)
The odds of finding success with AI implementation increase when the whole team is on board to acquire data, analyze it and develop complex systems to work with the information.
2. Teach staff to identify problems that AI can solve.
AI-driven enterprises often search out data scientists with deep knowledge of their business. A better approach would be teaching employees to identify problems that AI can solve and then guiding workers to create their own models. Your team members already understand how your business operates. In fact, they even know the factors that trigger specific responses from partners, customers and prospects.
IT can help businesses analyze and understand the context of each model. It also can plan its deployment using supported systems. Specifically, IT should be able to obtain answers on topics such as:
- The usage pattern required by a particular business process.
- The optimal latency period between a prediction request and its service.
- Models that need to be monitored for update, latency and accuracy.
- The tolerance of a business process to predictions delayed or not made.
Employees who tackle problems with an AI mindset can monitor business processes and learn to ask the right questions when it matters.
3. Allow business professionals to build machine-learning models.
A company trying to transform its complete scope of operations with AI might view the timeline as a bit slow. The current approach hinges on manually building machine-learning models. When asked, businesses managers ranked time to value among the biggest challenges. Respondents in the Gartner survey revealed their teams took an average of 52 days to build a predictive model and even longer to deploy it into production. Management teams often have little means to determine the model’s quality, even after months of development by data scientists.
An automated platform could transform AI’s economics, producing machine-learning models in hours or even minutes — not months. Such a platform also should allow business leaders to compare multiple models for accuracy, latency and analysis so they can select the most suitable model for any given task.
Equipping your staff with the right tools and skills empowers them to contribute to a system that’s optimized for your business. What’s more, automated platforms can help them create the models they need to transform processes.
Considering the many challenges businesses face when deploying AI, it’s understandable so many still lag behind. Organizations that have overcome these barriers can attest to AI’s power to revolutionalize business through process improvement and increased employee productivity.
End-use technologies require human participation as an input. Without human creators, technology can’t successfully morph into human roles.
Read the source article in Entrepreneur.