Artificial intelligence (AI) is transforming how economies grow, generate jobs, and conduct international trade. The McKinsey Global Institute estimates that AI could add around 16 percent, or $13 trillion, to global output by 2030.
Nothing will diminish my passion for AI, but at the end of the day it is just a tool, like many other tools, for accomplishing a job. While your customers might be excited by the prospect of the most bleeding edge technology, they ultimately will only care if it efficiently and effectively resolves a pain point they are experiencing.
You can create value even without having “big data,” which is often overhyped. Some businesses, such as web search, have a long tail of queries, and so search engines with more data do perform better. However, not all businesses have this amount of data, and it may be possible to build a valuable AI system with perhaps as few as 100-1000 data records (though more does not hurt). Do not choose projects just because you have a lot of data in industry X and believe the AI team will figure out how to turn this data into value. Projects like this tend to fail. It is important to develop a thesis upfront about how specifically an AI system will create value.
Data science today entails much more than building a prototype model and hoping it proves useful. Making the business case for additional data sources, prioritising against other projects and convincing stakeholders of the long term value provided are often equally if not more important than the actual technical work.
Change is hard. Convincing other people to change is even harder.
You spend the time cleaning and sharing data with your stakeholders, but they don’t use it in practice (despite you knowing it could help).
Broadly speaking, there are three main stakeholders. Each stakeholder has priorities which can, at times, be orthogonal to those of the others. As the data-science-delivery-leader, the first responsibility is to manage all these three stakeholders and ensure that they see the perspective of the other two while keeping the end-user of the solution at the centre of the picture. Only then can an optimal and practical solution emerge.
Stakeholder communication is certainly good for the organisation. Managers who are in constant contact with stakeholders are in a better position to assess organisational goals, to take advantage of unforeseen but mutually advantageous opportunities (e.g., cost reductions throughout the supply chain), and possibly to avert conflict before it reaches a critical stage (e.g., communication with dissatisfied employees or activists. But stakeholder communication is more than good for the organisation. It is a matter of moral obligation. Individual and groups who contribute to the organisation should be permitted some say in how that organisation is managed.
The fact that different people want different things from their relationships with organisations makes it impossible to know with certainty what stakeholders want. Stakeholder discussions often focus on allocating some measure of organisational value or outcome (e.g., who gets how much money from the firm). The question of how the organisation creates this value usually gets less attention, but it is certainly not less important. Not all stakeholders want a voice in organisational decision making, but those who do desire a voice should have it.
Anytime your organisation needs to initiate a change or transformation, especially a finance transformation, I cannot stress how important it is to break out your copy of Spencer Johnson’s “Who Moved My Cheese.” For a finance transformation to succeed, you must have buy-in from all the affected stakeholders. Stakeholders include individuals, groups, partners, sponsors, and resources that are impacted. Success demands more than excellent strategic and tactical plans; it requires an intimate understanding of the company’s culture, values, people, and behaviours that must be changed to deliver the desired result.
To many who aren’t data scientists, AI is still a black box and that scares them.
Many are reluctant to adopt AI because they don’t fully understand it. And the fear is real. In the highly regulated financial industry, institutions face serious legal and financial consequences if AI models are incorrect or misinterpreted.
Don’t expect people you’re pitching to to know what you’re talking about. You have to bring concepts to life – show them pictures, diagrams, or bring along a prototype – or create a video of real customers using it. If you can show something tangible stakeholders are more likely to sit up and listen.
Your stakeholders haven’t done the same level of research. Focus on the needs, priorities and concerns of your decision makers and by explicit in your claims when pitching. Stating that “it will improve collaboration” may sound obvious to you, because you know how your project will achieve that goal: but when it comes to securing support, it’s safe to assume a level of ignorance on behalf of your audience, and spell it out clearly.
Use your company’s core values as pillars to build your vision on.
If your values consist of sustainability, excellence and empowering communities, your core purpose should align with them to bring products that enrich society. Envision a future where your product better addresses people’s needs while preserving your values and aligning with your mission. Set your future aspirations at the limits that your company size, abilities and technological advances allow.
Take the time to identify the people, groups, sponsors, partners or resources that will be affected by the project or change. Include any roadblocks to a successful outcome. Stakeholder mapping and analysis is a great way to assess each stakeholder and the impact they will have on your project. Stakeholder analysis also has the goal of developing co-operation between the stakeholder and the project team, assuring successful outcomes for the project
Finally, tailor your message and your language. Look to those departmental objectives of your stakeholders. Does your CFO focus on ‘return on investment’, or ‘cost benefit analysis’ as a means of measuring success? Personalisation is a powerful, yet subtle tool to tap into the psyche of those you’re trying to influence.
Data holds the key: When used properly, your data can set your organisation up for success for years to come. After all, analytics is a powerful tool.