Legacy systems still form the backbone of many enterprises. Yet as the demand for efficiency, scale, reliability and agility grow larger, we’ve seen an evolution in these underlying technologies to meet those needs. Let’s explore some of these technologies, their history and their evolution to see why such a change was inevitable. Because in today’s digital economy, organizations need to drive at a very different speed than was previously acceptable and embrace change in their competitive landscape and products. Service-Oriented Architecture to the Rescue

Legacy systems, which are the mainstay of many enterprises, weren’t developed to support the implementation and adoption of new technologies and growing economies working at breakneck speed. Consequently, as the number of digital transformation initiatives increases and the speed of expected delivery intensifies, IT leaders become overwhelmed by the sheer number of requests across the systems. Moreover, existing legacy interfaces, developed in a world of daily batch calls, are not fit for purpose for today’s digital channels that require real-time data. 

Enter service-oriented architecture (SOA), with its promise of speeding up project delivery, increasing IT agility and scalability and reduce integration costs. Gartner analyst Roy Schulte defined service-oriented architecture in 1996 as follows:

“A service-oriented architecture is a style of multi-tier computing that helps organizations share logic and data among multiple applications and usage modes.”

The goal of SOA is to create independent services that represent a single business activity with a specified outcome, that is self-contained and can be consumed by others irrespective of its implementation details based on the exposed interface. However, as SOA was adopted by organizations across the world, SOA governance requirements, large scale ESB integrations, and a need for large service registries made the implementations heavy and monolithic.  

The original promise of SOA was to speed up project delivery, increase agility and reduce costs. However, SOA adopters found that it increased complexity and introduced bottlenecks. Although teams were able to create faster connections, they also needed to maintain a large ESB implementation which slowed down time to production and didn’t provide a reasonable return on investment.

Microservices are in fact, the next step in the evolution of service-oriented architectures. A microservice is:

• Functionally Scoped: Microservices design is based on services and applications that accomplish one narrowly defined business function. A microservice need not necessarily be small, its size depends upon the complexity of the business function it accomplishes. However, it will be smaller than an application that contains its functionality as well as other business functions.
• Autonomous: As essential element of a microservice is that it should be autonomous. That is, it should be able to function on its own and without the need for other services. Other services might be layered on top of it (such as a service that handles user authentication), but it performs its business function independently. It can be developed and tested independently, and it can be deployed independently. 

More than anything else, a microservices design forces us to rethink the way we plan projects and lead teams. It affects how we think of deliverables, application lifecycles and time to production. It is amenable to a DevSecOps based approach which is founded in amalgamated scrum teams with a focus on automation, speed and agility. In some ways it is akin to the change in thinking that came with assembly line production and Lean philosophies that revolutionized the manufacturing industry in the early 20^th century. Some of the benefits of using a microservice-based architecture are:

Speed — Since a microservice is an autonomous unit, independent scrum teams can develop, test and put it into production irrespective of other parts. Each new unit provides a critical and unique functionality but no one single unit prevents the whole from functioning. Hence services can be created and deployed to production in small sized scrum teams. 

Agility — An agile environment succeeds on small units which can be built in scrum teams of six to eight, tested and added to the release pipeline. Microservices not only just work, but thrive in an agile environment and promote quick, faster releases of independent units that can be promoted to production as autonomous units. 

Flexibility — The autonomy and lack of dependencies in microservices provides a number of advantages: teams are able to use the language and tools that best fit the problem, they can test, build and deploy functionality without being impeded by other teams and services, and the code base each team must manage is considerably smaller and simpler. They provide the flexibility to try out a new technology stack on an individual service as needed. There won’t be as many dependency concerns and rolling back changes becomes much easier. With less code in play, there is more flexibility.

And last but not least, Simplicity — Microservices provide us with the smallest productivity unit in a complex ecosystem of IT services within any organization — like the cells with the complex human body. It forces organizations to think of their simplest business function and smallest unit of work. In addition, rather than working on an element of a project that is centrally managed, each team working within the context of a microservices architecture is free to innovate within the context of a simple business function, promoting innovation and risk taking which does not affect the entire organization.

Thinking about and designing our applications in terms of small independent units is the first step towards building a modern infrastructure that is nimble, agile and scalable. While there will still be technical debt as teams balance timelines and design, it easier to pay down that debt. In fact, as organizations evolve and modify their requirements, IT departments can work alongside them in replacing services rather than maintaining them.

About the Author

Geetika Tandon is a senior director at Booz Allen Hamilton, a management and technology consulting firm. She was born in Delhi, India, holds a Bachelors in architecture from Delhi University, a Masters in architecture from the University of Southern California and a Masters in computer science from the University of California Santa Barbara.

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Organizations deploying artificial intelligence (AI) in the enterprise should start with a small use case that solves a specific business problem and ties back to the organization’s core values, according to a Google AI executive.

Tracy Frey, director of strategy for Google Cloud AI, shared these thoughts with the crowd at the MIT Technology Review’s EmTech conference last week at the Massachusetts Institute of Technology (MIT) in Cambridge, Mass. 

“What I tell companies, and what I think is really important about this space, is that the most important thing is to start with a business problem,” Frey said. “Identify what the problem is that you’re trying to solve.”

You’re Google, You Tell Us 

Frey gave attendees an inside look on how the search giant is living by its promise to be an AI-first company. But she also discussed problems she sees with organizations that want to leverage AI in the enterprise. Namely, it’s at the starting gate; too often, they start astray.

“There’s an extraordinary amount of hype about AI in enterprises around the world,” Frey said. “And a lot of the experience that we have in Google Cloud AI is that companies come to us and they say, ‘We really, really, really want AI. And we say, ‘Great, we would love to help you. Tell us what problem you’re trying to solve, so that we know what products we can help you deploy.’ And usually the next thing that companies say is, ‘I don’t know. You’re Google. You tell us what we should be doing.’”

Do You Know Your Organization’s Core Values?

Naturally, pinning an entire project on a vendor is not healthy. AI projects should begin with knowing your organization’s core values and “cultural pillars,” according to Frey. Ensure you spend time identifying those, and understand how you want your company to operate. 

“Because if you don’t start there, then if you start deploying things like AI and new technologies, you run that risk of everything being called into question,” Frey said. “Build your own principles, or whatever it is the process that speaks to you that feels like the right thing for your organization, and then identify one or a set of business problems. And start working with how AI can solve those business problems.”

Talent, Change Management

Frey likely recognizes she’s blessed to work in a company loaded with data scientists across the world and one that has its own AI Residency Program. She also recognizes that deploying AI in the enterprise is not only about technology and strategy but also having talent and change management practices. Data scientists are out there, but it’s not exactly easy — nor cheap — to get good ones into your front door. IBM predicted an increased demand for 700,000 more data scientists by 2020 in the US, but talented data scientists “remain hard to find and expensive,” according to a report from IDG. 

“AI has been around for a long time, but for the most part, enterprises that have been able to adopt AI are doing so because they have the ability to hire in top talent,” Frey said. “They are going to be likely only working on things that are really unique and customized to them and built in house and completely proprietary.”

That’s partly why it’s a “giant leap of faith” to invest in AI. It’s also “easy to underestimate the amount of change management that organizations should invest in when they are undertaking any AI project.” With the large volume of the unknown in the space, organizations without a change-management program will have a range of feelings across their organization with having AI part of their day-to-day work life. 

AI Needs to Be Built on Trust

No discussion of AI comes without ethics. Google has its own AI Principles manifest “because we fundamentally believe that you cannot have successful AI without being responsible and careful,” Frey said.

According to a Capgemini report, executives in nine out of 10 organizations believe that ethical issues have resulted from the use of AI systems over the last two to three years. Examples include: collection of personal patient data without consent in healthcare; over-reliance on machine-led decisions without disclosure in banking and insurance.

Trust needs to be the foundation of any new type of technology, Frey said. Without it, there’s a “great risk of stopping progress and making this incredibly beneficial technology available.”

Playing AI Defense

No matter how organizations feel about how AI can advance enterprises, such machine learning deployments can leave organizations vulnerable without playing sound AI defense, according to Tim Grance, senior computer scientist at the National Institute of Standards and Technology (NIST). 

NIST, a division of the US Department of Commerce, has its own take on AI technology development. Last month it released a plan for prioritizing federal agency engagement in the development of standards for AI. The plan recommends that the federal government “commit to deeper, consistent, long-term engagement” in activities to help the US “speed the pace of reliable, robust and trustworthy AI technology development.” 

Organizations must be aware of potential vulnerabilities AI exposes in their enterprise and deploy an “attack and defend mentality.” Recognize, though, that once you fix something, people are going to try to do something else, according to Grance. “If you’re betting the enterprise on some particular solution especially around AI, you want to address those questions of can people attack the data on which the system is training?” Grance said. “Can they attack our assumptions? Does it give us a real business advantage that we can maintain?”

Grance recognizes high-quality data, having the right people in place, knowing your business problem and executive buy-in as some key pillars in building a sound machine learning strategy.

“Everybody thinks about bias and can you protect the system so there are not some unintended side effects that would cause problems,” Grance said. “AI is just another cold-hearted, hard business decision you have to make. Is putting in this much worth it?”

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This mixed view of AI is not surprising because the technology does more than automate tasks. The person whose role no longer includes a certain repetitive task automated by AI may not necessarily lose their job. “Rather, they may now have new responsibilities that more broadly focus on human capabilities that AI cannot deliver,” Rachel Russell, executive director of corporate strategy at Allegis, said.

Increasingly, as more research is carried out into the impact of artificial intelligence in the workplace, it is becoming clearer that AI’s impact on employment will have some jobs disappearing. However, in this (near) future the role of humans will be to direct AI rather than for humans to be replaced by it. “Of course some jobs will certainly be lost as AI takes on skills formerly attributed to humans, but new jobs will also emerge…In addition, there will likely be new needs beyond technical development, such as AI ethicists to manage the risks and liabilities associated with AI,” Russell added.

As AI takes on more of the work we do, continuous learning and a willingness to develop new skills will likely be a requirement for nearly every worker to maintain their job. AI, it seems will be instrumental in changing and optimizing the digital workplace.

1. AI’s Impact On Data Analysis
Maksym Podsolonko is CEO and founder of Slovenia-based eazyplan, which brings automation to event creation and management. He says that given the amount of data used in any business transaction now, AI is the only way to analyze everything that is available to decision makers. “AI is the only way to effectively analyze digital experiences on scale. Only Machine Learning (ML) is capable of sifting through all usage data and finding the patterns that human eye simply misses. At this stage majority of organizations are not using this opportunity, leaving analysis of data coming from different tools mainly manual or slightly automated with SQL,” he said.

He argues that current offerings are still too complicated and expensive for a wide use. As a result, within the coming years we will see a significant surge of easily-accessible tools automating data analytics with ML.

2. Improving Intelligent Marketing
As companies undertake significant digital transformation initiatives, it is also becoming clear that artificial intelligence (AI) is no longer just an interesting idea, according to Katrin Ribant, Chief Solutions Officer and co-founder of Datorama, a marketing intelligence company based in New York City. “AI-enabled solutions are already changing how work gets done in marketing, customer experience and customer service departments. These departments are already aware of how AI can meet their various needs and streamline certain processes,” she said.

According to Ribant, the future decision makers will look to the vendors that AI-driven specializations. “Moving forward, professionals in these fields will look to vendors who can provide specific AI-driven features on top of their core offerings”.

She also points to the fact that enterprises are using AI to build digital experiences as AI-powered tools become more adept at workplace tasks. Some chatbots used by customer experience and customer service departments are able to pass the Turing test, making them indistinguishable from a human.

Marketers are freeing up to 80 percent of their day spent number-crunching with AI-enabled marketing technology platforms. A recent study showed that 53 percent of marketers plan to adopt AI within two years, the highest percentage of all technologies they are evaluating. These changes signal the widespread adoption of AI in the enterprise that is on the horizon and will fully optimize digital experiences.

3. Securing The Digital Workplace
AI is also being used to secure the digital workplace. If there is a great deal of talkabout AI’s wide-ranging impact on digital experiences in every vertical and discipline, one of the most important contributions is in the way it is enabling a whole new generation of digital security experiences. “AI-enabled software and dedicated chipsets together with tiny and accurate 3D cameras, are now allowing for fast, secure and transparent recognition, authentication and access management capabilities,” George Brostoff, CEO of SensibleVision, said.

He explained that one of the reasons these new authentication solutions are so transformative is that AI allows for the rapid capture, analysis and manipulation of the tsunami of information captured in big data sets. Digital experiences tied to authentication and credential validation enabled by AI are much faster, more convenient and at the same time more secure.

AI-enabled digital experiences deliver this new authentication paradigm across broad set of enterprise applications and settings. “The newest generation of AI-enabled smartphones allows users to simply point a handheld device at their face to validate their identity. We are also seeing Enterprise-level applications in more public setting,” he said.

There are many more AI-enabled authentication applications coming into play, including the use in urban settings of AI-enabled 3D video cameras to monitor access to physical locations such as data centers, hospitals or transportation hubs. All of these innovative digital experiences tied to authentication are being made possible by the ongoing evolution and application of AI. And we are only at the very early stages of this transformation.

4. Enabling Digital Learning
Raphael Sweary is co-founder and president of San Francisco-based WalkMe, which brings predictive analytics to digital workplaces. Sweary says that using AI coupled with deep analytics, enterprises can predict user behavior to provide step-by-step guidance and engagement on how to use a system, allowing the individual to be an instant pro on how to use technologies they haven’t encountered before.

With global IT spending expected to reach upwards of $3.7 trillion this year, ensuring an optimal digital user experience, improved productivity and measurable return on investment is paramount. We are already seeing big business capitalize on AI — and we can expect that in the near future, AI will learn about the user so that we don’t need to learn how to use any software,” he said.

5. Recruiting Help From AI
For Jonathan Duarte, founder of one of the internet’s first job boards called for GoJobs.com, the recruiting process for most companies, from small businesses to global enterprises, is ripe for process automation. There are several manual steps in the process that are currently being automated using subsets of artificial intelligence; including natural language processing (NLP) and machine learning. The debate in the recruiting and HR circles is whether this is true AI or not, but it is coming and coming quickly.

Take, for example, setting up interviews — necessary but time consuming. Historically, these tasks have been handled by recruiting coordinators or recruiters, who juggle multiple phone calls and email exchanges in order to book rooms and schedules for multiple participants, and then get confirmations. “Process automation, using messaging, algorithms, and digital calendars are going to rapidly change the interview scheduling process, and therefore eliminate a significant amount of manual processes handled by recruiting coordinators. This technology has yet to be deployed to the mass market, but it is coming. So, predicting the loss of this labor requirement is clear,” he said.

6. Using AI to Prioritize Data
Agata Celmerowski, VP of Marketing at Klaviyo the email marketing automation platform for ecommerce said that the most important capability is the interaction between data and AI. She said that brands and other enterprises need to think about the data they’re collecting, how they’re storing it, and how they can use it to create a better experience for their customers. “Without that focus, they’ll be limited in what they can do with AI. It’s important to view data as a strategic business advantage, and use it to make personalized content,” she said.

She added that the marketing industry is at a tipping point. “There is simply too much noise. Using brute force to try and cut through the clutter will stop working altogether in 2018. The only path to success for an ecommerce businesses of any size will be in making every interaction with current and prospective customers incredibly relevant to the consumer,” she said.

2018 is the year where using accurate, rich, and meaningful data to enhance every customer touchpoint will stop being optional, she said. It will become the new status quo for every ecommerce brand. Merchants who can’t quickly and effectively use things like website behavior, purchase patterns, or engagement metrics to drive their marketing strategy will be left behind.

From there, the natural progression will be applying increasingly sophisticated techniques to analyze that data and determine the best ways to accelerate growth. There’s a lot of hype in martech space today around things like artificial intelligence, but so far it’s been a series of empty promises.

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