| | AUGUST - 20239CIOReviewPoC's could be a good tool if you want to efficiently test how a "new technology" such as AI or distributed ledgers fit within your existing architectureBefore providing a methodology on how to innovate through experimentation, let us first explore the benefits. Firstly, testing new technologies and concepts through experiments is cheaper, faster, and easier than the sluggish, expensive, and risky alternative of implementing a solution based on intuition and marketing materials. Next, your organization can foster a culture of innovation and sense of ownership by creating a shared understanding of the hypothesis to prove in a PoC. Lastly, knowledge is a powerful raw material for any organization. Even if the experiment was deemed unsuccessful (i.e., new solution is not a viable fit for the organization's goals), there is an influx of insights around customers, markets, operational processes, and technology ecosystems.I have been fortunate enough to work for some fantastic organizations in the wealth management and capital markets industries, with a shared vision of prioritizing clients' financial goals while providing an effortless experience. Finding the right balance between experimentation and accelerated value delivery is critical; here are a few steps to aid anyone looking to kick-start the scientific mindset in your organization:Start by mapping out the capabilities within the function you support, so you have a good sense of what areas to prioritize. Once the vision and strategy are defined, it is up to your innovation and digital transformation practitioners to turn that dream into a reality. Intake: Managing innovation starts here, so rely on process and product owners in providing change opportunities as a starting point, albeit not necessarily the only approach.Discovery: As always, it is pivotal to ensure workstreams are clearly defined, expectations with key stakeholders are aligned, and commitment is gained from all partners, including oversight teams (e.g., Compliance, Risk, Legal, etc.). Rushing this step means you may be the only unbiased party in this experiment, so ensure there is an open-mindedness to explore new solutions.Design: The essence of any experiment ­ what are the hypotheses you are trying to prove/disprove? Ideally, I would encourage you to develop cross-functional and target-operating-model-agnostic success criteria. For example, if your current setup involves your IT department creating and maintaining all executive reports/dashboards, you might want to assess if that approach is possible but would also want to test if your business users can be empowered to create their own reports for a speedier process. This step is also important to start creating a high-level business case (e.g., Solution can reduce time-to-market by X%).Execution: Once the logistics are finalized, it is time to oversee the experiment. Try to keep the focus tightly knit and avoid scope-creep or delays, by reminding your users that your experiment is only proving a concept/value/capability, and so does not require a comprehensive view of every possible scenario.Prepare for Go-Live: Hopefully you have started to observe benefits with a future implementation of this solution and have kicked off discussions with Procurement (and other relevant teams) to onboard the solution. A good next step would be to plan a minimum viable product (MVP) to continue learning with each phase. Additionally, monitor any delivered benefits to your KPI's and pivot your roadmap, if needed.To recap, rapid experimentation of new technologies and vendors is critical to delivering quick wins (and losses), while setting up a foundation for an iterative development of digital transformation. You might still need to `preach' the benefits of your selected solution but can now offer much-added confidence to your leaders that an exhaustive assessment methodology was undertaken, and you are implementing the right tools to move your organization forward. When done right, a digital transformation program is akin to a caterpillar turning into a butterfly. Alternatively, not selecting the right tools and maintaining a biased thought process could turn your program into a faster caterpillar.
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