Today, most task work is driven by logic, which typically requires working from our left-brain. Logic is all about numbers, critical thinking, black & white. Those of us who live in our left-brains have great difficulty perceiving right-brain work. But, the right brain is still there waiting for us! Right-brain work includes creativity, design, communications, influencing others, building relationships, engaging, consultative sales, strategy, among others, a lot of stuff our parents warned us not to do. In my new book, The Workplace Engagement Solution, I talk about how a global engagement figure of 13% isn’t just a business problem, disengagement is a tragedy infecting our lives, families, customer satisfaction and day-to-day living. The great disengagement of the modern worker is leading directly to the scourge of our modern economy: underemployment. How many task workers are now part-time task workers? “Consultants?”
Digital transformation should set you up for the long-term by giving you a scalable, streamlined approach to business growth. And as such, it starts on the inside, building the internal systems that support even the outward-facing initiatives. With that in mind, having a baseline of what's working and what's not in your organization will provide you a good jumping off point. Are processes clear, well documented and accessible to everyone? Are all those processes easily repeatable and scalable, and supported by the right technology? Is there a system in place for hiring the right people, documenting and sharing customer data across different departments, making suggestions for process improvement and acting on it, and getting leadership's approval of major initiatives in an efficient manner? If any of these elements are off, your path to transformation may lead to expensive dead-ends.
Adding even a simple orchestration tool to the primary container software facilitates container deployment on public and hybrid cloud, as well as across multiple data centers. It can also help organize multicomponent applications, particularly those that share components among applications. Large enterprises should look into this path to achieve container success. The second trail off of the main container highway is for businesses with stringent security and compliance requirements. There are many ways to separate containers within a given server, and not all of them offer the same level of security. The most basic container software is the most popular, but not the most secure. If you have applications that demand an exceptionally high level of isolation and control, such as financial applications and even cloud applications involved in storefront missions, you may want to use something designed for that purpose.
The function of data in a data story is to tell what happened, and figuring out what happened typically starts with a question. Typical business-oriented questions usually revolve around sales or other KPIs, but they could be about anything—the results of a customer satisfaction survey, the efficacy of a policy change, the user behavior on a website. Once the overall question is established (e.g., How are our sales doing this quarter compared to last quarter?), you can begin to partition the data into smaller, more manageable pieces. Maybe you can track sales by product or by a sales representative or by company branch. Maybe you can look for correlations between revenue and other variables. If you don’t have a question to answer or artificial intelligence to point you to an interesting trend, you’ll likely have to do some data discovery and exploration to find a story worth telling. This is the process Ben Wellington employs when researching his blog posts.
AI increasingly will require knowledge and skill sets that data scientists and AI specialists usually lack, according to the paper. Consider a team of computer scientists creating an AI application to support asset management systems. “The AI specialists probably aren’t experts on markets,” says PwC. “They’ll need economists, analysts, and traders working at their side to identify where AI can best support the human asset manager.” And, since the financial world is in constant flux, once the AI is up and running it will need continual customizing and tweaking. For that, too, functional specialists, not programmers, will have to lead the way. AI has already shown superiority over humans when it comes to hacking. For example, machine learning, often considered a subset of AI, can enable a malicious actor to follow a person’s behavior on social media, then customize phishing tweets or emails just for them, PwC says.
Governments lacking in-house expertise is problematic from a policy development perspective. As AI increasingly intersects with safety-critical areas of society, governments hold responsibilities to act in the interests of their citizens. But if they don’t have the ability to formulate measured policies in accordance with these interests, then unintended consequences could arise, placing their citizens at risk. Without belabouring scenarios of misguided policies, governments should prioritise building their own expertise. Whether they’re prepared or not, governments are key stakeholders. They hold Social Contracts with their citizens to act on their behalf. So, as AI is applied to safety-critical industries, like healthcare, energy, and transportation, understanding the opportunities and implications is essential. Ultimately, knowledge and expertise are central to effective policy decisions. And independence helps align policies to the public interest. While the spectrum of potential policy actions for safety-critical AI is broad, all with their own effects, inaction is also a policy position.
Personalization, 1-to-1 marketing, people-based marketing and other trending terms describe how marketers in every industry are working to stop aggressive, irrelevant advertising campaigns and create highly targeted interactions. Online gaming is no different. Through big data, gaming companies can create meaningful marketing messages. Especially as we talk about all of the data being collected, the thought of playing a mobile game may seem intrusive. But gaming companies are mining such metrics to better appeal to their users with content they’re likely to appreciate, not despise. “Segmentation isn’t enough anymore. 76 percent of digital nomads expect to see a personalized website screen on just about every brand site they visit — and your inability to give any kind of individual regard means they think a lot less of your brand, and makes them significantly more likely to bounce,” according to VentureBeat.
No one would dispute the fact that we’re in an age of considerable AI hype, but the progress of AI is littered by booms and busts in hype, growth spurts that alternate with AI winters. So the AI Index attempts to track the progress of algorithms against a series of tasks. How well does computer vision perform at the Large Scale Visual Recognition challenge? (Superhuman at annotating images since 2015, but they still can’t answer questions about images very well, combining natural language processing and image recognition). Speech recognition on phone calls is almost at parity. In other narrow fields, AIs are still catching up to humans. Translation might be good enough that you can usually get the gist of what’s being said, but still scores poorly on the BLEU metric for translation accuracy. Measuring the performance of state-of-the-art AI systems on narrow tasks is useful and fairly easy to do.
Using data is proven to work. Our client’s examples showcase the possibilities. We’ve built and implemented a dynamic pricing model that deals with over 2 million quarterly pricing decisions. Increased fraud detection from 50% to over 90% and more accurately predicted e-commerce sales a year in advance. Our portfolio includes AI and AA projects for a large range of industries, often times including industry leaders. If you discover more frauds than your competition – you get an advantage. Moreover, you build from there. You get new ideas every time you work with data. The power and value of using data are spreading within organizations as managers start noticing results. ... The very first thing is to understand where you stand today. It might be that you already gather a lot of data, but your organization is mostly driven by spreadsheets. This is how a vast number of organizations are managed.
The general sentiment of global media coverage of AI in 2017 frequently painted very much what is deemed to be a worst case scenario picture of what the future holds for the technology. Job losses, a lack of human control and even killer robots continued to dominate the headlines in the year just gone. This year however, we can all expect to see the technology applied more widely, and more practically than ever before. Digital transformation will then be experienced through the mainstream application of AI to business operations, coupled with greater adoption of cloud and growth in the scale and degree of AI implementations. Not only will the technology revolutionise areas like the supply chain, in-store operations and merchandise execution but also dependency on AI will become a more prominent means of pursuing new business avenues across the retail sector.
Quote for the day:
"Eventually relationships determine the size and the length of leadership." -- John C. Maxwell