Everyone has heard about the big potential for using artificial intelligence (AI) to expand your business, but many of the small businesses I mentor are still wary of embracing it, because they don’t understand how it works, and fear losing control and unintended consequences. My advice is that AI is here, so it behooves all of us to learn how to use it properly and move forward. For example, it is a no-brainer to first take advantage of the wave of new capabilities for data collection and smarter analysis to improve productivity and marketing. What is not so obvious is how to create and roll out solutions that can directly impact customer trust or financial well-being. There have been too many recent glitches, such as evidence of devices invading our privacy. To put this all in perspective, I was happy to see the guidance and recommendations on how to deal with artificial intelligence correctly in a new book, “The Big Nine,” by Amy Webb. As a recognized futurist and thought leader in this space, she outlines how the big nine tech titans, including Google, Microsoft, and Alibaba, should be working to solve key long-term issues.
Researchers at Microsoft and the late Microsoft founder Paul Allen's school of computing science at the University of Washington has built a system of liquids, tubes, syringes, and electronics around a benchtop to deliver the world's first automated DNA storage device. Using the proof-of-concept DNA storage device, the researchers demonstrated its write and read capabilities by encoding the word 'hello' in snippets of DNA and converting it back to data. The bench-top unit cost around $10,000 but the researchers believe it could be built in low-volumes for a third of the cost by cutting out sensors and actuators. The unit, described in Nature, consists of computers with encoding and decoding software that translate digital ones and zeros into DNA's four bases: A, C, T, G. There's also a DNA synthesis module and a DNA preparation and sequencing module, between which sits a vessel where DNA is stored. Microsoft principal researcher Karin Strauss says the group wanted to prove there is a practical way of automating DNA data storage.
“Begin at the beginning, and go on till you come to the end: then stop. That is not same thing, however, as saying that a digital business transformation process should begin without a clear idea of where it is going. Indeed, this is vital. Unfortunately, the Celonis study also found that most organisations are struggling with transformation initiatives because they are diving into execution before understanding what actually needs changing. The research found that 39% of analysts are not basing their work on internal processes when executing the transformation strategy given to them by senior personnel. Celonis suggested that this highlights “that business leaders are investing in transformation initiatives because they think they should and not because they have identified a specific problem.” Businesses are also skipping square one, suggests the report, and are “still jumping straight into tactics.” It gave examples, AI, machine learning and automation. The survey found that 73% of C-suite say that these are areas that they want to maintain or increase investment in. In contrast, a fraction under a third of senior leaders state that they plan to invest more in getting better visibility of their processes.
"Interestingly, there have been some really significant advancements in brain science, cognitive science," Leaman said. This research looks at how the brain remembers information. "What we know now is that people do what they remember. If they don't remember they will guess. So how do you get people to remember and not guess or just simply not do?" This perspective has shifted learning to the form of micro content, focusing on key learning points that are accessible via a mobile device, just when an employee needs it. Typical use cases include restaurant employees accessing recipe cards, manuals or operational reference material to learn about new promotions, Carr said. Or medical device sales representatives can access and learn about new product information, product launches and new drugs. A fitness club employee can look up the day's workout each morning before teaching it to the class, and a field service tech can look up quick tips on a potential problem before going into a customer's home.
Digital transformation, Mike Graham, CEO of Epilogue Systems argues, involves a lot of people over a long period of time. Preparing for staff, budget and time exhaustion — before it happens — is critical to your team and digital transformation. External staff, such as systems integrators and software companies will vanish after the go-live date and internal staff may take themselves out of the project, or completely leave the organization. “Users must be able to effectively adopt the technology themselves in order for digital transformation to reach its full potential,” he said. Think about digital adoption beyond the critical first months. While adoption in the first three to five months after things go-live is critical, it’s a process that’s never complete. Think of all the changes that an application experiences over time: upgrades, shifts in an organization’s application landscape, integrations and APIs, and an increasingly complex digital workplace. Beyond that, there’s also the challenges of the workforce to account for: hiring, turnover, retirement, role changes and business model evolution.
AgileCraft's value stream management technology provides joint visibility for Atlassian's own stack into Azure DevOps Server, Rally and various continuous delivery tools. "What makes AgileCraft interesting is the platform's focus on helping enterprises figure out how to replicate DevOps success by holistically looking at and correlating the business and financial side of things and DevOps process flows," said Torsten Volk, an analyst at Enterprise Management Associates, based in Boulder, Colo. Atlassian's addition of a DevOps analytics platform could replicate the success of one or two high-performing DevOps teams across the entire enterprise, Volk said. "Considering the lack of competition in this arena, I think the $166 million could prove to be money well spent," he said. A key question is whether AgileCraft customers will face pressure to move to the Atlassian stack. Most enterprises use a variety of different products at the teamwork level.
Quantum computing-based security technology is effective because it relies on two of the best-known properties of quantum physics – the idea that observing a particle changes its behavior, and that paired or “entangled” particles share the same set of properties as the other. What this means, in essence, is that both parties to a message can share an identical cipher key, thanks to quantum entanglement. In addition, should a third party attempt to eavesdrop on that sharing, it would break the symmetry of the entangled pairs, and it would be instantly apparent that something fishy was going on. “If everything is working perfectly, everything should be in sync. But if something goes wrong, it means you’ll see a discrepancy,” said Jackson. It’s like a soap bubble, according to Brian Lowy, vice president at ID Quantique SA, a Switzerland-based quantum computing vendor – mess with it and it pops. “At some point, you’re going to have to factor [quantum computing],” he said, noting that, even now, bad actors could download encrypted information now, planning to crack its defenses once quantum computing is equal to the task.
In a situation where the deprecated feature or function will be removed entirely and not replaced, developers should offer suggestions for software layers or tools that can provide a worthwhile alternative, as well as guidance and instruction to help users adopt them. For example, if a custom database is replaced by a third-party database, such as SQL, support staff should ideally help users connect the database to the software and migrate it to the third-party platform. Software deprecation is all about continuity. You must ensure that developers don't alienate the customer base and instead help them through impending product changes to minimalize disruptions for their businesses. Providing continued service requires training for the help desk and support team, as well as helpful documentation in the form of notices, guides and knowledge base entries. Customers use your software to help run their businesses, so you must communicate changes about your product -- especially when you deprecate software features -- well in advance.
The pros are that you can have a 7/24/365 monitoring and management program on the cheap. If you believe operational staff is expensive, try hiring them for shift work. AI-based monitoring and management systems never sleep, never take time off, and never ask for a raise. Once they are up and running, they cost almost nothing beyond their license fees and infrastructure costs. And they are self-learning at the same time; in other words, the more they run, the better that they get at the job. ... One con is that the cost of rolling out these systems is high, even in the cloud. Vendors that have married AI and operational tools are going to charge a premium to get them up and running and in production. While the prices are all over the place, count on paying 50 percent more than for traditional tools, including consulting services for the first year or so to get the tools learning correctly. Another con is that operations people don’t seem to like them no matter how well they perform. The number of passive-aggressive actions that I’ve seen over the years from people pushing back on AI-enabled operations tools has been huge.
New technologies are also threatening the long-term viability of credit-based insurance. Carriers are increasingly seeking out data and building predictive models that will prove more powerful and profitable, even during periods of economic volatility. For example, my company, ODN has shown it is possible to extend more policies at more affordable rates to people with poor credit, by pricing risk based on where people drive, rather than who they are. To remain competitive and profitable in the long-term, underwriting and actuarial teams need to pay attention to the dynamics of credit-based insurance today and plan for a future that simply may not include FICO. Carriers should be asking, what will happen to our pricing models if economic conditions or regulators make credit-based insurance irrelevant? What new technologies need to be in place to continue pricing risk and remain competitive in a world without FICO?
Quote for the day:
"Leaders dig into their business to learn painful realities rather than peaceful illusion." -- Orrin Woodward