Human-AI Interaction: How should I plan for interactions between my employees and our evolving AI-led solutions?

• Artificial Intelligence

As a business leader, working with Fortune 1000 organizations and their leaders, shaping new solutions for their future competitive advantage, I recognize the pivotal role that artificial intelligence (AI) plays in shaping the workplace. In this article, we delve into some dynamics of employee-AI interaction, exploring how we can strategically plan for positive collaboration between our employees and AI-led systems. In short, help navigate the rapidly advancing future of AI.

The Coexistence of Humans and AI

That idea of Replacement – The fear that AI will entirely supplant human workforces is an early one. To be clear, AI has been in our day-to-day realm for over 10 years.  Many of us care not to admit that many of the models behind Alexa and Cortana and other digital assistants are AI. It has augmented our capabilities for years. Understanding this fundamental truth is important. AI operates within predefined parameters, and its success hinges on human inputs. Yes, some mundane roles may well change forever, while our role as professional managers is to harness AI’s potential and navigate the omnipresent unpredictability in human development.

When I was a young boy, and my parents used to drum into my head “adaptability is a critical skill”, I used to think small. As we grow older, we appreciate that ‘adaptability’ is fundamental in so many large life-changing scenarios. AI adoption is no different. The secret lies in integrating AI smoothly into our workflows. By doing so, we enhance our existing productivity and stay relevant. Some describe AI as a ‘booster’ for people’s professional journeys—a way to amplify what they already excel at, while at the same time inculcating adjacent disciplines a little quicker.

Managers must appreciate the need for developing a new set of skills here. We have witnessed many large corporates rolling out GenAI Open days, AI-beginner and AI-awareness training across the majority of their workforces. Good for them! The human-AI journey starts with good and coherent awareness and strong mentorship. Beyond domain expertise, digital literacy and proficiency in collaborating with AI tools are paramount. Those interactions will vary, whether navigating new processes, understanding algorithms or leveraging AI-driven insights. It all starts with good awareness, which then leads to working harmoniously with intelligent solutions.

Lastly, we should also anticipate material business changes, spurred by strategic influences from these initiatives. Transformation is inevitable here. It may be in pockets to start with, but it is highly significant. As leaders, we must anticipate these changes and adapt our corporate Business Unit strategies accordingly. We should ideally lead the charge with the openness and desired optimization we are seeking. It will inevitably rest on personalized experiences – so let us embrace it.

Nurturing Digital Innovations

As we hurtle toward 2025, the workplace landscape is undergoing seismic shifts. AI is found in everyday common language – it is an integral part of our daily professional lives. The focus remains to harness the potential, thrive with the new capabilities and build exciting innovation, in harmony with our employees.

Within our client companies, currently rewiring themselves, we are often witnessing the heartbeat of progress in the shape of AI pods—small, agile teams dedicated to innovation. These pods (numbering in dozens and hundreds, depending on the ambition of democratizing AI-led innovation), release AI-led digital solutions regularly. With that, here are some thoughts on good working practices:

• Kit out the Technology Toolbox
  Centres of Excellence (COE) teams have been doing this for decades. Here with AI, an organization is looking to scale through a developer-platform of sorts with a self-service portal that grants access to standardized, company-approved tools, models and training data, to name a few. Whether it’s storage capacity or collaboration software, pods can now equip themselves swiftly, fostering autonomy and efficiency.
• APIs: The connecting tissue of Innovation
  APIs (Application Programming Interfaces) are the unsung heroes of AI integration. They provide access to data and existing app functions, methodically reducing dependencies. Imagine pods collaborating seamlessly, sharing insights, and building solutions without bottlenecks. Amazon, for example, is a big promoter of this, championing APIs –and transforming their agility and speed to value. It’s a mindset!
• Development Methodologies
  Three leading models have emerged: digital factory, product and platform, and enterprise-wide agile. Each of these models is built on 2 central themes. The first is that small, multidisciplinary agile teams, or pods, are the most effective and efficient way to develop software. The second is that pods work together most effectively when they specialize by category (e.g. pods focused on improving customer journey or user experience (GTM) vs. pods that are creating reusable services to accelerate the work of all pods (ecosystem).

Transparency and Accountability

As AI becomes ubiquitous, ethical considerations loom large. Transparency, fairness, and accountability must guide our AI decisions. We’re not just managing algorithms; we’re shaping our organizational ethos into the medium term, at least. Leaders must champion responsible AI use, ensuring that bias is minimized, privacy is respected, and decisions are explainable. The Executive Leadership team (ELT) should commit to ethical AI, thus setting the tone for the whole workforce. The AI-infused workplace will evolve quickly, and our choices today will shape tomorrow’s reality.

Google Assistant and Microsoft’s Cortana are virtual assistants that have been used in workplaces to read texts and e-mail aloud, offer reminders to follow up on e-mail, schedule meetings, and find time in your schedule for focusing on certain tasks. The new generation Copilots that are popping up in even more specialized places from Microsoft and other providers, taking ‘virtual AI boosting’ and related productivity to a whole new level. The accountability behind these tools in the workplace will be valuable. So, staying transparent with high collaboration between teams on those improved productivity gains is important—Not hiding behind these newfound capabilities, but instead encouraging shared experiences. For example, through ‘how are you using AI in your department’ lunch & learn sessions. Such steps are highly favourable for that harmony we talked about.

Leaders have to provide a vision that rallies everyone around a common goal. In particular, they need reassurance that AI will enhance rather than diminish or even eliminate their roles. Inspire and align the top team. Take the time to establish a common digital language, learn from other companies that are further along the journey, to develop a shared vision.

Some companies struggle from the start of their digital and AI transformation by getting the scope of the change wrong. Right-sizing those initial programs. Either spreading too thin + too far or just too small + too tactical. At any rate, what is to be avoided, is revisiting the same project and changing the scope repeatedly, ‘to make it stick’.  As many as 80% of successful interventions in struggling digital and AI transformations are based on re-anchoring the scope to spur a concerted effort of what could be presented as a success.

Product Management Mindset

An aspect of the deployment methodologies, of company culture and best practices that many folks are not talking about, is professionalised product management. This capability, in my opinion, makes a big difference in the implementation of a new AI deployment operating model. McKinsey recently surveyed business leaders and some 75% responded that product management best practices aren’t being adopted at their companies, and that product management is a nascent function within their organizations or that it doesn’t exist at all. That’s a problem. The shift to a new operating model is often the signature move of CEOs in rewiring the company. Only they with their team can catalyse such large-scale organizational change.

Software delivery automation – ever wondered how an app on your phone or in your car can be upgraded so frequently? That seamless functionality is made possible by software delivery automation. This is the method for systematically automating all steps, including quality checks, testing, packaging, and staged deployment of the solution to the user. Updates that took weeks or months, can be rolled out in days or minutes. That rigour and automation design requires a Product Management mindset, allowing pods to release incremental improvements – thereby supporting faster innovation cycles. This is key to achieve distributed digital and AI innovation, to be delivered in production environments quickly and easily.

The other part is the machine-learning (ML) models behind many AI-led programs. These models are like living organisms—they need to be constantly recalibrated as new data accumulates and is monitored in real-time for drift and biases. Solving for this has required a specialized type of discipline and automation called machine learning operations (MLOps). With that, I propose we close the loop here, on the required automation and product management mindset and the design teams and the production ecosystems to deliver those new performances.