There is no doubt that AI (artificial intelligence) is the new electricity and everyone is trying to get benefits from the trend. Many companies are integrating AI solutions in their business operationsto reap the benefit of emerging machine learning (ML) technologies. The seamless introduction of AI, however, requires thoughtful adaptation of corporate strategy to requirements of this emerging technology.As a partner at a venture studio, I see companies try to get in the trenches of machine intelligence without the proper preparation. Here's what we recommend companies to advance their initiatives effectively.

To be efficient, data that is fed into ML algorithms should be properly labeled, cleaned up and structured. Companies produce huge amounts of unstructured data that adds no value unless necessary transformations are made. To succeed in improving their data, companies have the option of in-house data labeling and data cleansing or using third-party services of companieslike Scale that offer programmatic access to a growing community of people specializing in making data usable.

Similarly, to enhance their AI preparedness, companies need to integrate their dispersed data sources into the unified data warehousing framework. Data warehouses and data marts allow storing data generated by different business operations and departments in one place and in the uniform representation. This allows for centralized sourcing of data for ML algorithms. Even though it sounds like a simple exercise, most of the companies we work with have trouble organizing their data sources.

Prioritize and Grow Narrow Expertise

It is often tempting to use AI solutions in every business process that may benefit from automation and ML. However, such strategy leads to dispersion of organizational resources and decreases the cumulative effect of AI innovation.

Instead of creating a horizontal platform for AI innovations, companies should prioritize concrete AI solutions that have the biggest potential to increase financial value and customer satisfaction. Growing narrow expertise in one specific area will help concentrate organizational resources on one particular task, ultimately contributing to the development of a more general solution for your business.

Get Advantage of the Academia

Academia is the main breeding ground of expertise and skills in emerging technologies like AI. The depth of theoretical knowledge and expertise offered by AI researchers is hard to attain in the private sector.

Therefore, each company that we partner with is trying to find its own machine intelligence expertise to boost its strategy. We recommend these companies reach out to talent in academia. Academic AI experts can offer a long-term AI agenda for your company. breathing life in the most exciting and revolutionary ideas that would otherwise be lost in the lengthy articles published in academic journals.

In turn, companies should provide AI researchers with an opportunity to share their research with the public by encouraging them to publish scientific papers, participate in conferences, and maintain a connection with universities. AI researchers will join those companies that offer more freedom and necessary organizational resources to put their theoretical ideas into practice.

Create a Process Versus Chaotic Experimentations

AI experimentation is great. However, too many companies rush into new AI domains without putting structured approach in place. Treating AI innovation as a process starts from automating existing data analytics procedures to create a pipeline of fresh data. Without automation of existing operations, new AI solutions may reach the wrong conclusions simply because they work on out-of-date data.

The integrity of the AI process requires modernization of the entire IT infrastructure, ranging from in-house servers and databases to cloud-based services and networks. Sound AI innovation process may be also facilitated by the organizational change towards multi-disciplinary teams and training employees to new roles associated with the AI innovation. With all departments of your organization prepared for the technological disruption, AI integration will be closely aligned with the corporate strategy and organizational goals.

Global leaders of the AI innovation facilitate the fast adoption of AI technology in all industries by open-sourcing ML libraries and APIs (Application Programming Interface). Such ML libraries as Google TensorFlow offer companies access to out-of-the-box algorithms and neural networks optimized for fast deployment in the enterprise setting.

Businesses can also take advantage of cloud-based ML APIs that allow to easily bootstrap in-house AI software. One example of such APIs is Googles Cloud Vision API provided as part of Google Cloud offerings. The system encapsulates powerful machine learning models for image classification, object detection, image-to-text transformation provided as REST API. The system may be used by companies for building metadata of their image catalogs, moderating offensive content, and developing new marketing strategies based on image sentiment analysis.

Similar functionality is also available in the recently released TensorFlow Object Detection API. Apple has recently joined the party by unveiling its Core ML API that may be used to integrate fast ML algorithms on iPhones, iPads and Apple Watch. Companies using these solutions will have instant access to image and face recognition, and natural language processing in their applications.

Partner atColab, helping startups build tech products.

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5 Ways to Advance Your Machine Learning Initiatives - HuffPost