Part 2 in the “Machine Intelligence Ascending” series
No single technology can magically solve the challenges that come with digital business disruption. Getting value from machine intelligence will require you to roll up your sleeves and work through some big questions. Here are some key steps for moving forward:
Get imaginative. Machine intelligence enables business models and applications that weren’t previously possible. It’s your responsibility to create them and to understand how they can work within a modern software landscape. Machine intelligence could help a utility company identify and act on customer fraud or service theft, for example to save humans the work of poring over reports to find patterns and anomalies. The technology also could streamline customer service by analyzing text in customer messages and directing requests and complaints to the right employee. A media company could leverage machine intelligence to gauge individuals’ sentiments about a movie, for example, and then automatically generate hyper-relevant targeted marketing campaigns.
University educators could analyze text essays to accelerate the grading process or to flag plagiarism. Medical specialists could employ machine intelligence to speed up diagnostic work as they search for patterns among growing volumes of medical imagery and data. Fleet mechanics could simply take a picture of a broken part in the field and have a cognitive backend system automatically identify the broken part and order a new one – without the need for the mechanic to key in a part number or browse through a catalog.
Get smart. Understand what solutions are available. Many major technology companies today have some sort of intellectual property in the cognitive space. Smaller players have plenty to offer, too. Learn how the various technologies can work within your software environment to unlock value.
Get aligned. Work to get the workforce and leadership focused on the power of machine intelligence. Educate employees on its value to free up their capacity, so they can channel it to more subjective and strategic tasks. Clearly identify bottom-line benefits and build a business case that can show your top leaders the ROI potential and win their buy-in. Involve all the right practices and manage expectations.
Get organized. Document your processes. For cognitive technologies to be effective, you have to know what processes you can automate, what processes you need to automate, and what processes you can improve. Know also where your data resides and strive to develop a common repository for supporting consistent results.
Get going. Choose a sensible market, business model, or app to start with – one that can serve as proof-of-concept, providing lessons and inspiration for other projects. Understand that the project methodology for machine intelligence projects won’t follow the typical “input to output” path. You will need to embrace a more dynamic methodology – one that can adapt to the evolving outcomes of the project.
Get strong. Build a talent pool focused on bringing new cognitive capabilities to your organization. Know your plan for bolstering those capabilities – whether you decide to build them on your own, “buy” them from vendors, or rely on software-as-a-service solutions.
Get connected. Start building relationships with your suppliers and other partners, as they will be critical players in providing data – possibly new types of data – that can drive machine intelligence and help generate new value. Think also about business and customer privacy issues that can emerge as you connect with partners and collaborate with a systems integrator, because these third parties might end up with access to sensitive information.
Keep your sights set on the potential benefits
Why deploy more machine intelligence tools such as robotics process automation (RPA), machine learning, and AI? It’s all about the potential bottom-line benefits that your organization can see – not simply because machine intelligence sounds futuristic. Here’s what new cognitive tools can deliver:
- Increased efficiency, including greater speed of work, processes, transactions, and customer service
- Cost savings
- Savings on employee training
- Improved customer satisfaction
- Greater service-level availability
- Business simplicity, thanks to features such as natural-language search
- Higher employee productivity and fulfillment through the removal of menial tasks
- Improved information accuracy and process outcomes
Where do we go from here?
Machine intelligence is just one trend that will likely shape the future of your enterprise. New cognitive technologies will significantly influence what it means to create the next-generation kinetic enterprise. How will your organization move forward with cognitive tools? How will you unlock the potential they bring? What’s your plan?
Understanding how machine intelligence integrates with your existing solutions and how you can proactively respond to ongoing disruption requires more than vision. It requires a business-focused implementation strategy, extensive experience, and a rich set of tools and accelerators that can help you move fast along the digital transformation path.