Big ideas require bold actions, planning, patience, and plenty of support. The more ambitious the task, the greater the resource commitment and need for collaboration with others. A business case needs to be made for the investment regardless of its size, which can become difficult when cross-department collaboration is necessary to secure enough funding. Nowhere is this more evident than the cost centers of corporations.
For example, a legal team may have a series of tasks which have been identified as an ideal candidate for some form of automation. The team would likely identify several benefits that could be achieved through this process, demonstrating the relevant improvements to time management, and freeing up of human capital for more productive tasks. But some AI applications do not link neatly to projected returns, making ROI calculations less straightforward. Others can be more complex and unpredictable, making it difficult to apply traditional ROI approaches. Then there is the challenge of cross departmental collaboration. Identifying departments with which cross-department investment is difficult to achieve until it is obvious collaboration is required. Convincing stakeholders means building relationships and demonstrating how your big idea will make their lives easier. If they do not believe you (or like you), they may not agree to help fund these efforts.
Thankfully there has been a good deal of thought put into this challenge by a variety of consulting firms and independent research initiatives. Accenture, McKinsey, and Boston Consulting Group have all followed the growth and impact of automated solutions across a wide variety of industries, leading to insights that demonstrate common trends between those who are successful, and the organizations that fall short. Often the solutions presented sound simple enough, but upon further inspection these initiatives require courage, hard work, and a commitment to stand tall and lead across the enterprise. Understanding what makes a successful ecosystem for innovation should become part of professional development in every law firm and organization with dedicated legal and information governance functions. Just as AI requires education to gather support and gain trust, executive teams must respect and openly support a process which brings stakeholders together under common goals that seek to catapult big ideas forward through effective change management. Where it is difficult to make such a business case, organizations can risk either losing competitive advantage by delayed investment or wasted opportunity through sinking money into the wrong AI initiatives.
In a survey of more than 2,500 senior executives and other managers from 29 industries and 97 countries, BCG Gamma and MIT Sloan Management Review conducted a survey (link above) to better understand the challenges associated with successfully implementing AI. The results provided a pattern that has been seen elsewhere in data provided by Stanford’s annual AI Index and several other examinations of AI’s industry impact. For example, 9 out of 10 senior executives see AI as a business opportunity, however, only half that number also consider AI a potential competitive risk. Currently, around 7 out of 10 report little or no gain from their AI efforts, yet 6 out of 10 whose companies made significant AI investment have witnessed positive impact. In essence the survey revealed one key insights; those who commit are more likely to succeed than the rest.
This may sound obvious, but commitment often does not come easily. BCG’s survey revealed top-down support is required to grow individual stakeholder investment. There must be a collective understanding that it is ok to invest the time to get it right. In fact, the survey revealed six key moves that separated successful AI initiatives from their lesser counterparts. The first was integrating AI into the business strategy. Organizations invest a significant amount of effort ensuring their have a sound and defensible strategy that will see them succeed. Artificial Intelligence cannot operate in isolation from this strategy. Finding the ways that AI can be introduced alongside or in support of this strategy is the first positive ROI argument that any department can make. What are your business objectives and how can AI help you meet them? When building relationships with other departments in the pursuit of seeking collaboration, this question is a great place to start. Zero-in on the initiatives with the highest potential impact and grow your business case accordingly.
Success firms also sought to prioritize revenue growth over cost reduction. This might seem like a brick-wall to legal and compliance teams who literally work in the cost-center of a company, often trained to look for cost reduction by nature of the department. However, this is not necessarily the case. The intelligent use of data can turn it from a liability into an asset that improved data governance initiatives that seek to break down silos and gather intelligence across the organization to improve service and allow from product innovation. Much of an organizations data is collected and pooled without further inspection, but this is a waste in resources. There is no limit to the value that can be achieved, including everything from tracking employee sentiment and satisfaction, to mapping common customer complaints and supply chain issues. The catchphrase ‘data is the new oil’ is frankly tired and boring. Principally because it leads nowhere beyond a few nods of approval. Next time someone says this in a meeting, stop and ask them what they really mean. You would be surprised by the answers. Instead, drive the conversation toward proactively treating your data as a capital asset and communicate accordingly. It is important to pursue AI value through revenue and growth, embedding AI more deeply into the business. Cost cutting and efficiency will follow as a direct result of more intelligently using the data on hand.
AI winners have also demonstrated the willingness to take bigger risks and achieve greater impact. BCG’s survey found that companies that invest in large, high-risk initiatives are significantly more likely to see value that those focused on low-risk projects (50% versus 23%). Ok – stop reading and take deep breathe. I appreciate that many of my readers are lawyers with a firm understanding of risk and how to avoid it. However, this “the floor is lava” approach to innovation is about as helpful as pet rock. So here is how you overcome it; communicate the risk as part of a calculated business strategy that links to the core business strategy of the organization. The critical first step is designed to gain the support of c-suite (and ideally the board). Once you have the support of c-suite, you need to identify relevant stakeholders in other departments, and align to ensure cooperation and secure additional funding when needed.
Being fearful of risk is akin to fearing failure. This is a toxic mindset we all need to train away. If you never fail, you never learn. The key is to create an environment where it is ok to fail. The best athletes in the world do not look at failure as a negative thing. In fact, those who fear failure have consistently proven to fall short, operating without the will and determination which ultimately allows big ideas to succeed. It is also important to be realistic and maintain perspective. Sometimes, we cannot help but be caught up in our feels and our idea of failure consumes our willingness to push through. Just remember, Neil Armstrong was catapulted to the moon on a pocket calculator. Try creating an argument for that high-risk initiative as if you were going to convince Neil’s wife that this was a great idea. Then go back to your AI project and reconsider whether the risk is too great to pursue. If Neil can do it, so can you. #america
Successful AI initiatives also seek to align development with usage. BCG identified that solutions should be designed collaboratively with business and process owners. From the survey’s they uncovered some interesting statistics, revealing that of the effort required, 10% was about algorithms, 20% was about technology, and the majority revolved around business application. What this tells us is that success need not be an overly complicated endeavor. Rather, it is important to understand exactly what you are trying to automate and why. It may often be thrown around in marketing circles, but Simon Sinek’s Start with Why remains as relevant for Apple computers as it does for cross-departmental AI initiatives. By beginning with Why, you can reverse engineer your AI ideas to ensure you are approaching the task from the right perspective or perspectives.
Companies also succeed when they treat AI as a business transformation initiative. The key here is the need for collaboration across departments alongside strong support from c-suite and the board. AI initiatives have proven to fail when pursued in isolation. There may be a successful proof of concept performed here or there, but there is no organization wide impact unless AI is understood and communicated as a project that is inclusive and exciting. This is not the time to lock the egg-heads away in a closet and expect results. Instead, embrace and grow knowledge and appreciation for what AI can achieve, then fan those flames to make the technology a company wide effort that will grow the organization into a leader for years to come. Among companies generating value from AI, 88% tightly link their efforts to companywide digital transformation.
Finally, there is the importance of investing in AI talent, governance, and change management. Stakeholders across the enterprise require investment in human capital to feel truly supported in these efforts. At the c-suite level there should be a chief digital officer (or dare I say, Chief AI Officer), who leads this growth and creates new roles designed alongside the broader company strategy and key initiatives of each department. Investing in recruiting, reskilling, and training increases the odds of seeing results from AI – as does embracing agile and robust platforms and governance. The net effect of these behaviors, and their underlying commitments, are to address difficulties generating value with AI, manage unavoidable competitive and implementation risks from AI, and effectively exploit AI-related opportunities.
Sink or swim, eventually we will all need to jump in the water.