AI systems are machines. The central proposition that the power of artificial intelligence (AI) and its machine learning (ML) engines comes from software-driven computing devices, which are essentially machine brains driven by algorithmic logic, is debated. There’s no room for that.
But it’s the members of the AI team who really make the difference. The intelligence team includes data scientists, business logic experts, lower-level system administrators, higher-level system architects, and even commercial function representatives such as salespeople and project managers, and of course software application developers. If it is not made up of the right combination of things, it will not be a good team.
it’s about people
AI adoption in enterprises is currently moving at two speeds, with some organizations rushing to implement solutions to generate a quick return on investment (ROI). At the same time, some people take a long-term view and expect to earn future profits based on their current investments. Regardless of where your organization goes with its AI journey, it’s important not to overlook the most important foundation of your AI strategy: people.
“Today, we know that implementing AI faces many of the same challenges as previous technologies, and at this point, most organizations are “Not enough consideration has been given to the human element of the planned strategy.” . “Technology isn’t always the problem, it can ultimately be humans, and this is especially true when it comes to AI. Part of the talent problem is evident in the lack of AI skills; A shortage of qualified data scientists, data engineers, platform engineers, and other related professionals is hindering progress and increasing costs. Salaries for AI professionals are currently very high, accounting for up to 35% of project costs. There is a possibility.”
McMullan believes the problem gets worse because another part of the human problem is managing AI data in the right way so that experts can produce useful results and increase efficiency. I am. We know that AI models are only as good as the data that goes into them, and that comes down to talent. Also, keep in mind that biases can be introduced into algorithms through the use of inappropriate data.Why should we think about professional human resources first and then professional AI? is easy to understand.
“As we move into the next wave of widespread AI adoption, organizations need a detailed look at what it takes from a talent, compute, storage infrastructure, and data perspective to deliver scalable projects that generate real ROI. “This is an ideal time to analyze the future. To ensure success for organizations considering AI adoption, it is important to invest in the right resources and technology designed for the future.” says McMullan.
Perhaps best placed to comment on this issue is Workday, a company specializing in human resources (HR), finance, and planning software.
Tasks first, work second
“Building AI capabilities starts with understanding the tasks in your workplace and the individual use cases you are trying to accomplish,” said Jim Stratton, chief technology officer at Workday. . “We use a co-pilot written in-house in a proprietary software language. [Workday XpressO is essentially an object-oriented wrapper for SQL database queries combined with a page builder] The same goes for external technology in this area. The challenge is that while AI-centric co-pilots are good at coding, they are not as good at software engineering or understanding the human needs behind a particular implementation of AI. ”
For Stratton and his team, developing and implementing AI isn’t just about accelerating jobs; it’s about automation in a broader sense, helping software engineering deliver and scale applications that work in more parts of the world. It’s about being able to grow and collaborate better. It makes your data more secure (and eradicates AI bias), offers better integration capabilities, and of course more features.
But why can AI help developers improve integration? Stratton explains this point succinctly. Suppose one application needs to connect to another application. This functionality is often performed using application programming interfaces (APIs). This is a technology element that acts as a “glue” bond between applications and data services. Here, we can see that developers can use AI to take charge of the relevant data mapping (to understand the format, shape, schema structure, and syntax of the data) and perform the entire process more accurately and faster.
“When we deploy AI across our HR, finance, and planning solutions at Workday, integration with other workplace systems is often an issue, so bringing a human element to our approach to AI is fundamental to what we do. ,” Stratton said. “While we are currently working to elevate individuals into more strategic work tasks, we must always start AI projects with a human-centered mindset, from the developers who create the AI to the users who use it. Building highly distributed, highly available, high-performance systems (like ours) cannot be done automatically; software engineers in this field will soon be replaced by co-pilots. It is not.”
The point here is clear. When you think about implementing a finance and HR system, that system is connected to hundreds of other enterprise software systems covering things like revenue, employee benefits, and even sales and marketing. Stratton reminds us that many organizations are highly fragmented ecosystems with hundreds (or thousands) of connected applications. He is adamant that where we are today, keeping the human factor at the forefront of AI implementation is a hit or miss, first base or zero.
IFS’ Chief AI Officer (CAIO) is Bob De Caux. As a cloud enterprise software company with a leading platform focused on ERP and related areas across field service management and enterprise service management, IFS has a vested interest and skillset tailored to building and developing AI teams . De Caux agrees with many of the opinions and insights expressed here so far, and that from his perspective, a key part of building an AI solution is building a user experience around his AI. He states that human resources are important to properly achieve this.
“This is less of an issue when you are automating processes, but in many industry use cases, humans are still involved in the decision-making loop as AI provides decision support,” De Caux said. “Therefore, it is important to deliver the output of AI in a way that builds trust and confidence for users, who are often non-technical users. It means discovering and providing transparency into the data used in the system’ processes and converting highly quantitative results into business meaning. Building a framework of explainability and transparency to meet user needs is a very human adventure. ”
First people, then data, then AI
Even though we explain that people, processes, data and information sources, work systems, and task mining all come first, the discussion of data use cases remains central to this discussion. One of the biggest concerns surrounding AI and automation is that machines will replace the workforce in large numbers, and that eventually we will all be replaced by robots and a series of AI systems on assembly lines and customer inquiries. This is important because it will cost you your job. Remember that reality is more subtle and less scary.
That’s the opinion of Richard Jones, vice president of Northern Europe at data streaming platform company Confluent.
“By automating routine tasks, AI creates new opportunities and enhances existing roles. I see AI helping to make people more rounded and skilled employees. Boring Management By taking on the burden of employees, we free up employees to focus on more complex, creative and strategic tasks that drive value for the business,” Jones told Press&V analysts in London this spring. “Jobs are evolving over time to place more emphasis on informed decision-making and human interaction, and to allow employees to improve their skills as they grow into these evolving responsibilities. We get it. But AI isn’t just enhancing existing roles; we’re seeing entirely new roles being created, including prompt engineers, machine learning developers, and data scientists.”
Jones spoke about how intelligent use of data can take AI to a higher level, helping AI sift through extensive data sets to deliver highly personalized experiences and engage consumers. It reminds us that we can live up to rising expectations.
“By leveraging data streaming technology, we can also do this personalization in real time, resulting in customer service applications that respond instantly and accurately in a human way,” said Jones. . “AI, with human assistance, is expected to significantly improve the customer experience. AI-driven chatbots and virtual assistants can provide instant support around the clock and provide This immediacy greatly improves the customer experience while freeing people to address more complex and sensitive issues that require human intervention. There is a possibility that it will.”
I want human AI
We know that businesses are reporting improved customer satisfaction metrics as a result of integrating AI into their customer service workflows. This highlights technology’s ability to enhance rather than detract from the human touch, if an employee is willing to work collaboratively with his AI.
The AI message that people come first (and, in fact, data comes first) has resonated for a significant period of this decade as we work to embed AI services into enterprise operations layers, workflows, and lives. may continue. Your immediate question is probably when someone tells you that a new AI service is coming, is it great? How human is it?
follow me twitter Or LinkedIn.
