Many of our clients are interested in the impact that AI might have on their business. The biggest question is “What Can I Do with AI” and “Where Do I Start?” These needn’t be abstract questions—in fact, in our business, they’re a natural outgrowth of the growth acceleration work we do. In many ways, the AI revolution is a descendant of the digital revolution that many of us participated in not that long ago. The lessons learned are plentiful, and while the path may not be crystal clear, the themes that can be leveraged to create success are.
Drive For A Clear Strategy:
Some organizations adopt AI without a clear strategy, leading to disjointed efforts and suboptimal results. A well-defined AI strategy is essential, answering the key questions of where can AI improve the business, what’s the path to success, and what should the financial, customer and people expectations be?
Keep a Cool Head Under Competitive And Board Pressure:
In the digital revolution, numerous boards and bosses were demanding to know “when are we going digital” because they saw that competitor xyz had gone and launched a digital asset of some kind. Lots of time and treasure can get wasted caving into that kind of pressure. It’s the same with AI. A balance between speed and strategy is essential—the bosses and board will appreciate it later.
Have A Reasonable, Specific and Clear Path to ROI: Back to strategy, evaluating the best business case and quantifying the potential impact of any AI initiative is critical. and The ROI for AI projects may not be immediate or easy to quantify. Setting realistic expectations and measuring success incrementally while having a very clear big picture theory of the case is worth the upfront investment. We always build a financial model as part of the foundational work we do, and it provides a great resource to constantly adjust assumptions and stay focused on the ultimate goal.
An Internal Team Needs To Closely Partner With Outside Consultants With the Goal Of Owning The Initiative Long-Term:
If we learn one thing the hard way every day, it’s that having a consistent client team with available bandwidth to help the consultants lead the effort is table stakes. AI is fundamentally a business transformation tool, and the consultants will ultimately move on. The only way transformational initiatives will stick is to have client ownership by a team that is bought in and battle hardened.
Change Management Starts At The Beginning:
Any AI initiative will no doubt require a change in workflow, processes, and talent. One of the key members of the team going in needs to be leading the strategy and implementation of the change. Just as digital created new jobs and new ways of doing things, so too does AI and getting ahead of the change is way better than retrofitting change on an organization after the big decisions have been made.
Data Quality and Availability—Easy To Talk About, Hard To Get Right:
AI systems require high-quality and diverse data for training and decision-making. Many organizations struggle with data that is incomplete, inaccurate, or not easily accessible. This challenge is one of the bigger deal-breakers or drags on any successful AI implementation.
Data Privacy, Security And Governance At The Front End:
Managing sensitive data in compliance with regulations (e.g., GDPR, HIPAA) can be complex. In addition, client privacy policies or philosophies may present obstacles to progress. In parallel to the item above, a realistic assessment of where you are as a client organization and a top-down commitment to evolving is a pillar of making progress on any AI initiative. Additionally, a robust and forward-thinking framework for data governance as AI and new use models generate new kinds of data is mandatory.
Buy Or Build Decisioning For Data And Tech:
There are many commercial applications out there with AI as a bedrock component or that have added AI functionality. Alternatively, new use cases, cost or cultural issues may lead to building AI/ML models for ownership internally. Either is OK based on the use case and a host of other factors, but evaluation of data and tech resources as part of the project will need to be planned in from the start.
Leadership, Training and Education:
Like any transformation, it’s a process and ultimately will involve many teams and fundamental reshaping of how some things are done—all for the better. Ensuring that employees understand the change and the role they can play is a responsibility of both the project team and ultimately company leadership.
There are no doubt dozens of additional considerations but as we think about AI as transformational as the internet, there are a set of lessons that we can draw on to create a successful outcome.
Overcoming these challenges requires careful planning, adequate resources, and a commitment to addressing ethical and regulatory concerns. AI adoption should align with the organization’s long-term goals and values to reap the full benefits while mitigating potential risks
