30 Sep 2022| ONPASSIVE
Role Of AI And Automation In Workplace
Artificial intelligence (AI) is the most important new technology today. It has apparent applications, and the value it has generated thus far is undeniable – think of your phone’s digital assistant, self-driving cars, and even Gmail. However, it is no longer only the responsibility of large IT firms. As AI becomes more mainstream, more companies gain access to it and launch artificial intelligence initiatives. After all, business is like an arms race, and having the latest “weapon” to help you stay ahead of the competition is an alluring proposition. What is the prognosis? Shortly, there will be a significant wave of new AI deployments… and with it, a lot of misery.
The reason for this is that, while well-known AI initiatives are making headlines, the technology is still difficult to implement. Both technological know-how and resources are in short supply. As a result, the concise answer to the question is no. A more nuanced view is that some businesses, particularly those with a track record of implementing advanced IT projects and relatively deep treasuries, are better positioned to use new technology.
We thought we’d offer our opinions on making the most of artificial intelligence in light of the tsunami of unprepared, overly-enthusiastic businesses rising on the horizon, all of which are getting ready to battle with the technology. Then they’ll very certainly fail, squandering time, money, and resources (maybe some image capital, too). What is the secret to success? We’re sure it’s a case of corporate architecture.
The first thing businesses should know about AI is that it is a highly specialized technology that requires extensive contextualization. It will take a lot of tweaking to figure out how it fits your business model. “AI technology in isolation is not useful,” stated Andrew Ng, one of the world’s best AI scientists.
To do so, you’ll need a broad awareness of your firm as well as a solid grasp of AI. To fully realize AI’s potential, you’ll need a team that knows the business context and has cross-functional expertise on topics like how to integrate AI into your hospital or how to apply AI in your logistics network. It’s impossible to design AI to generate specific business goals without cross-functional expertise of how your company operates.”
That’s coming from someone who oversaw the artificial intelligence programmes at Google and Baidu. He’s talking about first and foremost comprehending the business concept, having clarity over the business context and bigger organizational picture; he emphasizes the importance of cross-functional teams and collaboration, and even mentions aiming for particular business results(!). If you had any questions when we suggested business architecture was critical to a successful artificial intelligence deployment a minute ago, those worries should have vanished by now.
You cannot manage a complex, expensive effort in an existing complex environment of dependencies without being able to capture the organization’s ‘truth,’ i.e. having a thorough understanding of the business logic, strategy, stakeholders, processes, and technology and data landscapes. Well, good news: business architecture is your ally in all of this.
What better way to assess and choose the most appropriate artificial intelligence use case for your company, plan and allocate resources to support the project, and adequately protect against unintended consequences across the enterprise? When you use architecture to plan and execute difficult change over such a large and diverse region as a modern organization, you get the structure and transparency you need.
Consider the following scenario. Let’s say a telecommunications company came in last place in an industry-wide customer satisfaction study this year. The corporation decides to enhance its customer satisfaction level in light of the dismal result and, perhaps more importantly, the traditionally low degree of client loyalty in this area. As seen through the lens of enterprise architecture, here’s one method to go about it.
The company’s initial step is to construct a customer journey map and analyze which elements/capabilities contribute to a positive customer experience. Using a customer journey map already puts the customer in the spotlight, which is the whole point of the exercise. This also allows them to consider any distinct negative aspects that they may have overlooked previously.
Once this is done, and the capabilities have been defined, the project team may focus on the cross-architectural parts that make up these capabilities. Three abilities are recognized: a Customer Forum, a Cinema Chain Partnership, and a Telephone Customer Support Service. According to internal surveys, the first two are performing well, and consumers who interact with them are generally satisfied with the level of service.
On the other hand, customers appear to be continuously slamming the third one, citing excessive wait times and poor service. The customer experience would be considerably improved if this functionality could be improved. Suddenly, the possibility of using a virtual chatbot appears.
As a result, the case for implementing AI technology in the company can now be presented. But what about the present capability? On the other hand, EA allows you to pinpoint the programmes that help customer service representatives do their jobs and determine whether retiring them to make a place for a digital assistant is a better option. In this situation, combining technology and application designs with practical impact and dependency studies will ensure that decisions are made based on facts at the end of the day.
The telecoms corporation has decided to boost client satisfaction up to this point. They discovered a weak spot in their customer journey responsible for the great majority of their customers’ complaints — their telephone customer care service. This is an area where, coincidentally, AI has recently had a lot of success. An analysis is conducted to see if the benefits of providing cutting-edge customer service exceed the risks, costs, and general requirements of implementing such a solution. The organization decides to pursue this concept.
Finally, unwanted supporting applications and technologies are safely and efficiently discarded as the project progresses. As expected, the AI solution goes live, accompanied by a cast of supporting capabilities. Should any unanticipated developments in technology or the regulatory framework arise, EA would be the best chance to come up with a solution and ensure the project’s delivery?
After it goes online, the company should monitor client feedback and make adjustments. This learning and feedback loop is critical for optimizing the investment over time. Nothing is ever flawless, so keep an eye on the solution to see if there are any new ways to make it work even better — that kind of gradual progress adds up over time.
Not only that, but by periodically evaluating its performance and testing various scenarios, the telecoms business may be able to discover new applications for the solution, widening its reach and boosting the return on their investment. For example, they may find that integrating an AI-powered assistant might considerably increase their internal knowledge base after some time.
In this case, EA was in charge of the entire process of increasing customer satisfaction levels. The primary line is that, regardless of whether a problem requires immediate attention or not, enterprise/business architects will likely play a critical part in testing future AI scenarios. Their multi-functional understanding of enterprise value streams, processes, technologies, and data potent transformation enablers. As we approach closer to when artificial intelligence technologies are widely adopted, EA’s transparency and accountability make it a critical tool for identifying excellent use cases and successful deployments.
Are you ready to use artificial intelligence in business? Contact the ONPASSIVE team.
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Tags: Technology Artificial Intelligence