AI systems are essentially machines. There’s no disputing that Artificial Intelligence (AI) and its Machine Learning (ML) capabilities derive from software-powered computing devices, which essentially function as machine brains guided by algorithmic logic.
The real game-changer lies within the people who form an AI team. Without the right blend of expertise, including data scientists, business logic specialists, lower-level systems administrators, higher-tier systems architects, as well as representatives from commercial functions like salespeople and project managers, alongside software application developers, a team is unlikely to succeed.
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It’s All About the People
Currently, there’s a noticeable divergence in the enterprise adoption of AI, characterized by a two-speed approach. Some organizations are sprinting ahead, swiftly implementing solutions to yield immediate Return on Investment (ROI). Conversely, others are adopting a more patient stance, envisioning future benefits stemming from present investments. Irrespective of an organization’s position on its AI journey, it’s crucial not to overlook the most fundamental aspect of any AI strategy – people.
“Today, we’re aware that AI implementations encounter many of the same challenges as previous technological advancements. Surprisingly, most organizations aren’t giving sufficient consideration to the human aspect of their planned strategies,” According to CasinoSpotDE. “It’s not always a technological issue; oftentimes, it boils down to people – and this is particularly evident in the realm of AI. A significant part of the people problem lies in the shortage of AI skills, where the scarcity of proficient data scientists, data engineers, platform engineers, and other relevant professionals is impeding progress and escalating costs. Salaries for AI professionals are currently at a premium, sometimes accounting for up to 35% of project expenses.”
Another facet of the people problem arises from effectively managing AI data professionals to ensure they can deliver valuable outcomes and enhance efficiencies, according to McMullan. This challenge is further compounded. We understand that the quality of an AI model is intricately linked to the quality of the data it receives, highlighting yet another aspect of the people problem. Moreover, considering that biases can infiltrate algorithms due to the utilization of flawed data, it becomes evident why prioritizing skilled professionals is paramount. In essence, we need to prioritize professional people first, and then professional AI.
As we enter the next phase of AI adoption, it’s a great time for organizations to really look into what they need in terms of people, computing power, storage, and data to make sure their projects can grow and bring in real benefits,” advised McMullan from Pure. “It’s super important for any organization thinking about using AI to make sure they invest in the right stuff and technologies that will work well in the long run to guarantee success.
A company well-suited to comment on this matter is Mr PachoC, a specialist in human resources (HR), financials, and planning software.
Prioritize Tasks Over Jobs
Start by understanding the tasks and specific use cases you want to achieve when building any AI function,” explained Jim Stratton, Chief Technology Officer (CTO) at Workday. “We use internal copilots written in our own proprietary software language, Workday XpressO, which is essentially an object-oriented wrapper for SQL database queries combined with a page builder. We also utilize external technologies in this area. The challenge lies in the fact that AI-centric copilots excel at coding but struggle with software engineering and understanding the human aspect behind AI implementations.”
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For Stratton and his team, AI development and implementation are not solely about speeding up job functions; it’s about broader automation to empower software engineering in delivering applications that function seamlessly across various global regions, scale effectively, ensure enhanced data security (and eliminate AI bias), facilitate better integration capabilities, and provide additional functionalities.
But how does AI assist developers in achieving better integration? Stratton clarifies this point succinctly. Consider a scenario where one application needs to connect to another, often facilitated through an Application Programming Interface (API), which acts as a link between apps and data services. Here, developers can leverage AI to handle the data mapping process (understanding the format, structure, and syntax of the data), making the entire process more accurate and efficient.
At Workday, our approach to implementing AI across HR, financials, and planning solutions involves collaborating with other workplace systems. Integrating the human element into our AI approach is essential to our philosophy,” emphasized Stratton. “As we aim to empower individuals with more strategic work tasks, we always prioritize a people-centric mindset in our AI projects, from the developers creating it to the users utilizing it. Highly distributed, highly available, high-performance systems like ours cannot be built automatically. No software engineer in this field is going to be replaced by a copilot anytime soon.”
The point is clear: when implementing a finance and HR system, it connects to numerous other enterprise software systems covering revenue, employee benefits, sales, marketing, and more. In many organizations, this forms a fragmented ecosystem with hundreds or even thousands of applications to integrate. Stratton emphasizes that keeping the human factor at the forefront of any AI implementation is non-negotiable.
Bob De Caux, Chief AI Officer (CAIO) at IFS, a cloud enterprise software company focusing on ERP and related disciplines across Field Service Management & Enterprise Service Management, shares similar sentiments. De Caux emphasizes the importance of building the user experience around AI, with people being crucial for achieving success.
While automating processes may not pose significant challenges, for many of our industry use cases, a human is still involved in decision-making, with AI providing decision support,” explained De Caux. “Thus, it’s crucial that we present AI outputs in a way that fosters trust and confidence, especially for non-technical users. This involves explaining why AI models make decisions, providing transparency into the data used, and translating complex quantitative results into understandable business insights. Developing an explainability and transparency framework to meet user needs is inherently human.”
Putting People First, Data Second, and AI Third
If we prioritize people, processes, data and information sources, work systems, and task analysis, it doesn’t mean we overlook the importance of data in this discussion. The fear surrounding AI and automation often revolves around the idea that machines will replace humans in large numbers, leading to job losses and widespread unemployment. However, the reality is more complex and less alarming.
This perspective is shared by Richard Jones, VP for Northern Europe at Confluent, a data streaming platform company.
“In automating routine tasks, AI creates new opportunities and enhances existing roles. It helps employees become more versatile and skilled, freeing them from tedious administrative tasks to focus on complex, creative, and strategic activities that add value to the business,” Jones explained during a press conference in London. “Job roles are gradually evolving to emphasize informed decision-making and human interaction, allowing employees to develop new skills as their responsibilities evolve. Moreover, AI doesn’t just enhance existing roles; it also generates entirely new ones, such as prompt engineers, machine learning developers, and data scientists.”
Jones highlights how smart utilization of data can take AI to new heights, enabling it to sift through vast datasets to deliver highly personalized experiences, meeting the increasing expectations of consumers.
“By leveraging data streaming technologies, personalization can even happen in real-time, leading to customer service applications that respond accurately and promptly in a human-like manner,” Jones added. “AI, with human assistance, has the potential to significantly enhance customer experiences. AI-driven chatbots and virtual assistants offer immediate support around the clock, efficiently handling common queries and issues without human intervention. This instant support has the potential to greatly improve the customer experience while allowing humans to address more complex and sensitive matters that require human interaction.
We want human AI
We’re aware that businesses have observed enhanced customer satisfaction metrics after incorporating AI into their customer service processes, highlighting the technology’s capacity to complement rather than replace the human touch – assuming employees are open to collaborating with AI.
The notion of prioritizing people (and indeed data) over AI may remain relevant throughout much of this decade as we strive to integrate AI services into our enterprise operations, workflows, and daily routines. Perhaps the future question will be – when someone mentions a new AI service emerging – fantastic, but how much does it prioritize the human element?
