As Digital workplace Consultants, It is important for us to understand the difference between AI and GenAI.

AI is the science of leveraging machine learning and Deep learning techniques to make machines think like humans, process large amount of data, recognize patterns to model their decision making. Whereas, GenAI is a subset of AI that creates new solution based on dataset from previous resolutions. GenAI needs to be trained and integrated with pre-built data. Any GenAI use case cannot function without LLM/SLMs.

LLMs are a subset of AI, primarily designed for natural language processing tasks such as text generation, translation, and summarization. Hence, The premise of GenAI is limited to understanding human language in any format, interpret and process them using transformer architectures to perform a wide range of natural language processing tasks. But, The premise of AI is unlimited since it can function without relying on LLMs. The only problem with AI or GenAI would be that they lack ethical responsibility and cannot abstract real life and unpredictable nature of infrastructure, users or the business.

While LLMs are powerful tools within the AI landscape, they are not essential for all AI applications. AI’s versatility allows it to function effectively across various domains using a range of models and techniques tailored to specific tasks.

While analysing an organization technology footprint as part of the Design Thinking Empathize session, Do we ask ourselves the important question that do we need to position AI or GenAI and define the use cases accordingly? It is very important find the answer as the technology investments and future Tech debt depends on this. As consultants, It is also our responsibility that while we ask our customers to invest on a technology, we also give them a clear roadmap on how we are planning to provide the right ROI for the same.

This goes for AI embedded hardwares such as AI PCs also. As we know, The largest cost driver and TCO consumer in Digital Workplace is the end user hardware. As per industry trend, By 2027, 95% of the desktops, laptops or tablets would be enabled with AI tools such as copilot and for the AI PCs to function effectively, The hardware requires dedicated Neural Processing Unit (NPU) integrated with the CPU/GPU circuit. This would eventually increase the cost of the device atleast by 20%.

So, While AI is good for user productivity and efficiency, it also comes with a cost. The hardware + Technology/Tools + AIOps will be atleast 20-30% costlier than today. The AI or GenAI capability will come from tools or platforms such as ServiceNow, MoveWorks or Yellow.ai. Our job as consultants is more towards understanding the capabilities, defining its versatility in applications and training the AI tools with use cases beyond OOB use cases thus ensuring that the customer gets the right ROI on the investments along with delivering operational efficiencies and making users happy. Please remember the operating model that you define has to be compatible and agile to adopt futuristic ways of working. You will be defining a model that must deliver business values and not just solve incidents or requests.

Along with the services such as Zero SD, L2 Remote Services, Engineering services and onsite services, The Service reliability layer will also become essential that will consist of business analysts, data scientists and Automation/AI engineers. In a vertically integrated IT operating model, They will act as the cross functional teams who will drive digitization into workplace as a daily BAU. The L2 team will work more in agile mode who will be focusing more on innovation rather than issue resolution which will be handled by users, devices and L1 desks.

Some use cases to deliberate – User segmentation and profiling, User behaviour nudging & gamification, Endpoint management, architectural possibilities of the new workplace deployment and bolster security by enforcing permissions and restrictions.

As Digital Workplace Consultants, our responsibility is to have a vision for the customer and simulate the journey for them atleast for the next 3 years, explain to them on how you plan to provide the ROI on the investments and get their commitment to invest and engage with us in this AI journey. It can be towards Zero Ops or AI Ops, It will not happen overnight, It is a journey and needs persistence and commitment from customer + Service Provider more than investment.