Today’s AI Imperatives: People, Urgency, and a New Work Paradigm

AI is changing how we work. But it’s also allowing the C-suite and boards to rethink what work is and how people can expect to work alongside AI.

As we mature at Automation Anywhere and work toward becoming an autonomous enterprise, we’re reimagining traditional frameworks, repurposing decades-old methodologies, and using AI to reframe people as the primary focus. Along the way, we’re developing a scorecard to benchmark and track our progress and measure ROI across automated and AI-enabled processes and business functions.

Here’s how we see AI redefining work.

A new paradigm of work in the era of AI

The oft-used framework of people, process, and technology has been around for more than 60 years. It’s time for an update.

Developed as the "Diamond Model" in the early 1960s, it originally had four elements: people, structure, tasks, and technology. The model had a resurgence in the 1990s when structure and tasks were combined to create the current people-process-technology iteration that’s such a staple of business meetings worldwide.

However, as AI agents become less like technology and systems and more like collaborators and autonomous coworkers (i.e. more like people), and the technical side focuses more on agentic orchestration, AI is permeating and changing this three-pronged framework. Moreover, as organizational growth, innovation, and success increasingly rely on AI, the concept of work itself must include AI maturity.

Reimagining the people-process-technology paradigm to consider AI isn’t a simple task. Just as you cannot deploy AI and become an autonomous enterprise, you cannot simply inject AI into workflows and reap widespread efficiency and productivity gains. Instead, increasing AI maturity requires a strategic realignment of your entire organization, and that begins with your people.

The urgency to invest in people

A recent article, "AI elephant in the boardroom: future workforce is here" by Dr. Kellie Nuttall, AI Institute Leader at Deloitte Australia, explores why boards and executives must look beyond AI’s compelling productivity and efficiency gains to better understand how AI will transform your entire organization.

For example, Dr. Nuttall points to the traditional ways in which workers gain experience and how AI is eliminating that “apprentice” track from vast swaths of work: “Our traditional model of career development — learning by doing, gradually gaining experience and judgment — is under threat. Junior roles are often the first to be automated.”

As AI agents take on entry-level tasks such as information gathering, analysis, summarization, and reporting, fewer younger workers are required. That prevents an entire generation from gaining the experience, skills, and expertise necessary to guide AI-enabled workflows. Furthermore, according to Dr. Nuttall, those junior workers will never gain the “judgment, adaptability, ethical reasoning, creativity and critical thinking...essential to critique, shape and responsibly integrate AI into our world.”

What’s crucial for boards and C-suites today is to invest in those cognitive skills people at all levels increasingly need to guide work and evaluate outcomes in a workplace dominated by AI-driven processes.

Boosting every worker’s AI skills and knowledge

In our Capability Maturity Model for Collaborative Intelligence (CI-CMM), we provide a framework for assessing, deploying, and scaling AI in pursuit of strategic business goals. Across the five stages of AI maturity, a key component is skills development. Stage 1 assumes people have a basic understanding of AI tools and capabilities, while Stage 5 expects autonomous organizations to have people with expertise in AI governance, self-learning system management, autonomous decision-making processes, and more.

Again, simply deploying AI isn’t enough; people are equally crucial to your AI success.

People need opportunities for additional training and reskilling, input into and ownership over automated processes and prioritizations, and visibility into traditional KPIs and new AI-driven KPIs, as well as how they impact each other. AI governance efforts can pull those elements together, track metrics and ROI, expand AI enablement, and manage AI agent orchestration across teams and systems. Governance can also guide people as they use AI, providing guardrails that reduce risk, increase control, and safeguard data, which lets people use AI more confidently.

With a concerted AI effort that considers and includes people in the decisions and processes, you can increase AI maturity faster.

Don’t just take our CI-CMM’s word for it, however. IDC’s MaturityScape: AI-Fueled Organization 1.0 states that, across people, strategy, and technology, “‘People’ is a critical element of AI maturity on which to focus” as organizations increase maturity from the beginning “AI Scramble” stage through to becoming an optimized “AI-Fueled Organization.”

IDC’s “AI Scramble” terminology highlights today’s buzz around AI and the pressure most organizations and leaders are under to deploy and realize value from AI very, very quickly. However, the combination of pressure and speed can create a Wild West atmosphere, pushing teams to deploy AI without coordination, orchestration, or preparing people for the impact.

As you move from today’s scramble an autonomous enterprise, you want your people to be involved. Build their AI knowledge now so they better understand how AI impacts and changes their core responsibilities and what parts of their role can be shared with AI agents.

Reimagining work to unleash human potential

Why are people so critical of this workplace evolution? AI empowers people to reach their full potential as they work to accomplish corporate goals.

If you’re unfamiliar with the concept of Lean, it’s a process improvement methodology focused on reducing waste to improve efficiency and speed in delivering value to customers. The two pillars of Lean are continuous improvement and respect for people, including customers, workers, and teams.

Since Lean aims to reduce waste, the methodology categorizes "eight wastes" such as defects, waiting time, motion, and more, with the eighth being unused skills and talent. It’s obvious how defects and unnecessary transportation waste money, time, and resources. However, unused human potential wastes innovation, training, education, experience, ingenuity, and more.

Unleashing human potential separates people from work. Instead of merely following orders, people can take responsibility for processes, identify issues, and create solutions. Reimagining work to unleash human potential by increasing AI maturity creates shared objectives of improved value, innovation, and scalability for your workers, customers, and organization.

As the initial "AI is taking our jobs" scare subsides, more and more researchers are pushing leaders to see AI as acollaborator, curator of information, and coworker best used to augment human effort. A simple example is AI taking on mundane manual work so people can focus on higher-level cognitive thinking. However, the real value comes at the higher levels of AI maturity, where people and AI combine their best traits to tackle work collaboratively.

As you invest in AI to gain capabilities, invest in your people for the same reasons. As Dr. Nuttall writes, "Invest in creativity, emotional intelligence, complex decision-making, systems thinking – the skills machines can’t replicate."

Power to our people, quantified

At Automation Anywhere, we’re well on our way to becoming an autonomous enterprise. We’re not there yet, but we’re putting agentic AI to work across our organization to reimagine work and strategically realign the enterprise, increase scalability and efficiency, and empower our people. More AI-enabled processes will change the work experience for our teams and allow them to focus on creativity, strategy, and complex problem-solving.

By putting people at the center of these efforts, AI helps them elevate experiences for our customers, coworkers, and partners. AI also increases efficiency, enabling our teams to achieve more in less time and with fewer resources. And, AI can scale instantly across processes, data, and locations for elastic capacity that scales our people, too.

We’ve also updated the people-process-technology framework to a people-process-systems enterprise operating model, supported by performance management and controlled through governance. Here’s how the different elements adapt to today’s AI-enabled reality:

• People: Humans and their AI collaborators
• Process: Agentic orchestration to manage effort
• Systems: AI and AI-enabled technologies, applications, and systems
• Performance Management: Track progress and maturity in becoming an autonomous enterprise
• Governance: Control, access, security, responsible AI, and more

Introducing our Autonomous Enterprise Scorecard

Along with redoubling our focus on people, we’ve increased AI training and enablement, measured our current maturity baseline, and created new maturity targets and plans to achieve them. The updated baseline gives us a better view of impact as we move forward, which also helps us prioritize ongoing investments based on expected and delivered business value.

An outcome of these efforts is our new Autonomous Enterprise Scorecard, which enables us to score processes and business functions based on their level of autonomy. These levels range from fully manual processes with zero automation to AI-assisted processes where humans and AI collaborate to autonomous processes completed without human involvement.

Mapping processes across this spectrum provides a measure of our AI maturity and our progress toward becoming an autonomous enterprise, even identifying processes or areas where autonomy isn’t possible or feasible, such as unique, highly complex, or cognitive processes AI may never fully automate.

As we evaluate processes for automation, we take an “automate vs. eliminate” approach to identify areas where we can remove waste from underlying tasks. Our Autonomous Enterprise Scorecard quantifies and visualizes these breakdowns to show progress on moving processes from manual to autonomous and targeting others for elimination.

Here’s an example of a business function scorecard with subordinate functions and the breakdown across the spectrum of:

• Autonomous: Run entirely without human involvement
• Assisted: Where AI and automation tools augment human work
• Automatable: Manual effort ripe for automation
• Not Worth Automating: Complex or low ROI processes
• Eliminate: Wasteful tasks targeted for elimination through process re-engineering

Expect to hear more about the Autonomous Enterprise Scorecard very soon, including how you can score your organization’s progress and AI maturity.

Until then, our Collaborative Intelligence White Paper provides a detailed overview of the CI-CMM, increasing your organization’s AI maturity, and laying a course to becoming an autonomous enterprise.