Normally, GBS Heads and CIOs from various enterprises partner to accomplish organizational tech adoption goals. Yet, occasionally the urgency, the significance of form vs function, or even the two leaders’ vision may differ. Here’s the conversation summary between Ingo Elfering, CIO at Fresenius, and Robert Weltevreden, Former Head of Novartis Business Services.

1. IT Implementation should be planned according to business needs with utmost commitment – Every implementation approach should be planned, keeping an overview of business objectives. Sometimes, one needs to engage pilots to avoid initial risks and ambiguity. It’s small, it’s easy. Nothing happens to the business if we get it wrong. In the phased approach, one has the opportunity to innovate the minimum viable product incrementally. In some cases, one has to commit to a ‘Big Bang’ to make a big difference and eventually put all the resources of the company behind it.

2. If the impact of implementation is high, do it fast with clarity and establish clear lines of communication – High-profile implementations do impact people’s jobs and work processes, and it should always have a big-bang approach. It is important to make sure everyone in the organization is on the same page in terms of expectations.

3. The future must be one of collaboration and partnerships, where departments and ownership no longer matter – Generally, GBS is in charge of business processes, whereas IT is in charge of technology. But today, all have to come together with their expertise and contribute to the business collectively. Intense and in-depth discussion and collaboration is the need of the hour, as control and ownership blur with time. Hence, we see new product teams forming in organizations where individuals from IT, business, and operations are all coming together with their skill sets and expertise to solve a business problem.

4. The core focus should be on solving the underlying problem, not buying IT products – Organizations are not interested in flashy technology transformations. They are more interested in solving the underlying problem with technology. But many do approach it in the wrong way, thinking a breakthrough product from a particular vendor can solve a particular problem.

5. RPA is obsolete, and AI-ML is the future – Today, RPA is an outdated technology. AI and ML are going to be the real drivers for GBS transformation, and IT Team will hold the center stage in deciding the right technology and the vision of digital transformation for business. But even today, AI-ML is difficult to implement in a large-scale enterprise. Only a deep understanding of business data and well-defined business results one wants to achieve out of it will clearly define the success of AI-ML.

6. Technology and AI-ML are being introduced into workplaces faster than ever – With tech-savvy younger workforce joining organizations, tech adoptions are accelerated in workplaces like never before. There’s a proliferation of new technologies, and it’s very difficult to stay up to date on all the different opportunities that it brings.

7. Every organization should be well aware of the ethical aspect of AI-ML – Companies have a great opportunity to use AI-ML, but they must be ethical about how they use it. This is tricky and especially holds for the pharmaceutical and life science industry, where patients’ lives are at risk. Pharma companies must be mindful of how they use AI-ML in their business processes so that it doesn’t unduly discriminate against patient groups. Likewise, there can be multiple instances of machine bias and wrong predictions due to a lack of empathy and contextual understanding. One has to be smart enough to use AI-ML and not just hold onto it for dear life.