By Montoux's Geoff Keast, originally shared in Forbes
Decisions shouldn’t happen in a vacuum — or in a black box. But for modern decision makers, adjusting the decision making process to incorporate new information sources and technologies can be as complex as any of the business decisions they need to make.
Decision makers have increasing opportunities to incorporate a wide variety of data into the decision making process, often alongside new technologies powered by artificial intelligence and machine learning algorithms. Sometimes, they’re encouraged or tempted to completely turn over decision making to these systems, which comes with its own risks. But what is riskier — changing too much and risking black box decisions or changing nothing and losing the ability to ground your decisions in real-time data and insights?
Here are some realistic ways decision makers can approach modernizing the decision-making process — without giving too much ground to technology or failing to gain its advantage.
1. Treat decision making like a science — because it is one.
Resurrect the classic scientific process (bet you haven’t heard that one in a while). Decision making, when treated like a science, is a process that can be fine tuned over time when approached through the classic steps: start with a decision, do the research, create a hypothesis, test and implement, monitor results, adjust your hypothesis and repeat.
The technical term for this approach to decision making is Decision Science.
2. Be clear and specific about the decisions you’re addressing.
This includes identifying all parties the decision impacts and will impact, the different opportunities and threats associated with the decision, any trade-offs, as well as all the different sources and types of data that are relevant to the decision. Ask questions like:
• Who do we need in the room?
• What data and insights do we need to ensure we’re informed about our options?
• What are some associated risks we need to account for?
• What areas of the business will this decision impact?
3. Broaden your data scope.
Don’t just dredge up some historical data from similar decisions and call it a day. Data can do so much more than that, and large companies have no shortage of ways to access new forms and sources of data. Consider third-party data and recent customer, competitor and market data. Ask questions like:
• How will we gain the data?
• How do we combine it effectively to gain cohesive and holistic insights?
• Is what we have enough or do we need to outsource for additional data sources?
• Are the insights we can gain clear enough to illustrate our options? If not, who do we engage to gain clearer insights?
4. Leverage AI technologies thoughtfully, not through a black box.
Artificial intelligence has tremendous potential across the decision making process. It can help automate tedious data mining and number crunching, accelerate and refine insights and deliver a clearer, more holistic view of what your options are. AI can be tricky though, and avoiding the black box is crucial to making this process repeatable. Ask questions like:
• Does my team understand where the data is coming from and how it’s being used?
• If we’re working with a third party — do we have access to their models so we understand how the AI works?
• Do we understand how the insights are being generated and how our specific constraints and objectives are being factored in?
5. Invite input and expertise from a wide variety of stakeholders.
Great ideas don’t just happen at the executive level. Invite ideas, feedback and expertise from a wide variety of individuals in your business — and even some outside. You might be surprised what new perspectives and insights they bring to the decision making process and the decision at hand. Ask questions like:
• Have you noticed anything notable since the last time we addressed this?
• Have you heard anything you found concerning or exciting that might influence this decision?
• What are some things you think we may have overlooked?
6. Explore and model a wide range of scenarios before making your decision.
The best way to explore your options is to lay them all out in front of you. Cutting-edge technologies, like Decision Science platforms, can model the different decisions you’re considering, providing clear direction on the best way forward. When preparing to model scenarios, ask questions like:
• What are our long-term goals, and how do we measure these scenarios against them?
• What are our constraints, and are they incorporated?
• What trade-offs, if any, do we need to consider?
• What results are we specifically looking to see, or to avoid?
7. Monitor results and feed this data back into the decision making process.
This part is crucial to making this process repeatable and scalable, as well as one that improves over time. When you’ve implemented your decision, closely monitor and collect data on the results and ensure it’s easily and readily accessible for the next time you need to make that decision. Ask questions like:
• Are the results what we expected to see based on our modeled scenarios?
• How did our competitors and customers react to the decision? Was it what we expected?
• How do the outcomes of this decision line up with our long-term objectives?
• What new sources of data or stakeholder expertise should we include the next time we make this decision?
8. Improve the cycle over time with new information, data and experience.
The next step is committing to working on the decision making process consistently and thoughtfully. No process is more tightly aligned with a company’s long-term success, sustainability and scalability than its decision making process. Executive teams and decision makers who commit to taking a holistic and thorough approach to the decision making process, and supplement it with human expertise and robust data, will reap the rewards over time, while those who don’t will fall behind.