Five common AI mistakes—and how to avoid them

Forewarned is forearmed. The awesome business potential of AI is yours for the tapping, if you can sidestep these common adoption challenges.

For executives tasked with making the big bets that translate to shareholder gains, the challenge of monetizing Artificial Intelligence (AI) can feel overwhelming. Faced with a profusion of exciting use cases, leaders need to pick one surefooted path through the forest of options, while also managing the adoption of enterprise platforms for boards and shareholders who want to see short-term results.

But help is at hand. While these are still the early days of the AI Era, it isn’t dawn anymore. There are lessons to be learned from the pioneers and early adopters who have already put transformative technologies such as machine learning, conversion AI and generative AI to work in their organizations. Based on our own experience advising top-tier communications, media, and technology clients worldwide, here are the five most common AI adoption mistakes we’ve seen companies make, and our best advice on how not to make them yourself.

Common mistake #1: Getting carried away.

AI encourages our imaginations to run wild—and that’s a good thing. But creative license cannot come at the expense of solid, old-fashioned business decision-making. We worked with one company—already beset by weighty challenges, from resource limitations to a complex, long-tail operation—who prior to our arrival had devoted valuable time and attention to considering a proposal for, of all things, an AI-generated avatar.

How to course correct: Set targets that can deliver quick results. A different company we partnered with, a prominent telco, sought breakthrough growth in digital revenue. To maximize engagement and conversion rates, we guided the client to adopt a suite of AI tools to personalize customer experience in real time, while providing real-time shopping assist based on purchase propensity. Careful execution of this plan resulted in the telco exceeding its top- and bottom-line targets.

Common mistake #2: Failure to integrate.

Corporate capabilities don’t exist in a vacuum, and AI is no exception. We’ve seen too many companies be proactive in their AI investments, only to then treat these new tools and platforms as “standalone” systems from which they expect value to flow as if by magic. Generally speaking, companies that fail to integrate have been disappointed.

How to course correct: The key to integrated AI adoption is to build capabilities that will one day be at the heart of a company’s future operations, but which also yield immediate results. Maintaining this dual focus is no small feat. One approach we’ve found helpful is to create a future-state “capability map” for the enterprise. The capability map inventories all of a business’s tools, competencies, and abilities that will be impacted by the AI initiative, and addresses what’s needed to reach the desired future state.

The result is a holistic vision for AI, and a step-by-step plan for its implementation. We partnered with one client on a capability-mapping exercise that began by outlining the company’s operational structure and business processes, then reimagined its omnichannel customer-care operations. With the capability map as its guide, the company positioned itself for a unified, integrated approach to AI, and we used the map to build out AI capabilities across all functions: reducing departmental siloes, and democratizing the company’s enterprise-wide knowledge management system.

Common mistake #3: An overly “tech-centric” approach.

AI initiatives too often begin and end with the purchase of AI platforms, without charting an AI-powered course toward a defined set of business objectives. Deciding on, and articulating, a vision of your company’s future operations once AI has been fully deployed is essential for success.

How to course correct: Here, a different sort of map is called for: a “transformation roadmap” of your company’s journey toward those defined business goals. Unlike a capability map, which inventories strengths and highlights gaps across the structure of an organization, a transformation map is a comprehensive plan for moving forward in time. It includes a step-by-step forecast of how the AI initiatives will impact the company overall, with concrete implementation plans for every impacted stakeholder group.

When it comes to customer service, for example, look for tools and technologies that will leverage AI capabilities to build a more interactive experience. We partnered with a telco client that sought to reduce its annual customer-service costs by millions of dollars. To meet that goal, we developed a comprehensive plan to train generative AI-powered virtual assistants to handle the company’s customer-service requests. The client had initially planned to replace its contact center technology but hadn’t considered the complexity of challenges that would follow due to high customer volume. We encouraged them to take a step back, to map out their key objectives, identify pertinent tools and technologies, and consider impacted stakeholders. Then our team helped develop a plan to not only sustain those changes but also manage the ripple effects for business units far beyond the customer call center.

One key point: Executives should be heavily involved in the process of restructuring business operations with AI as the backbone. We regularly see this responsibility left to middle management—rarely with good results. It’s essential to ensure that leaders at all levels are aligned across all horizontal and vertical teams as you implement AI solutions.

Common mistake #4: Making governance an afterthought.

Many companies go all in on AI experimentation with no framework in place for oversight and governance. Too often, they find themselves scrambling to put governance in place after the fact, in the wake of projects gone awry or an AI-related data breach or liability issue.

How to course correct: Before investing in AI tools and platforms, establish an AI Council of dedicated leaders representing business and technology stakeholders within your organization. Next, establish a plan for funding AI initiatives, and a structure for reviewing and prioritizing those initiatives. Smart AI governance involves putting in place clear policies and procedures for core functions such as ethics, privacy, and risk management. Done right, smart governance can also play a useful role in evaluating and prioritizing future AI investments moving forward.

Over time, the goal should be to build an AI Center of Excellence (CoE), where contributors provide insights, tips, and training to the Council, based on their experience working with AI. Using this information, Council members can then standardize best practices for company-wide adoption of AI, providing advice on strategic planning, decision-making, and execution that leaders can cascade to their counterparts.

Common mistake #5: Not planning for scale.

We often hear clients say, “Let’s just do the pilot quickly. If it works, we'll figure out the scale-up.”

How to course correct: If your intention in creating AI use cases is simply to prove that you can leverage AI for straightforward natural-language tasks—conversations, processing requests, formulating responses, etc.—then it’s not a worthwhile investment. That concept has already been tested and proven.

But if your company is looking for transformative uses, then it’s critical to build in plans to scale right from the start. This exercise also provides focus: By requiring pilots to include a proper end-state solution architecture and dataset strategy before launch, companies make the most of limited project funding, and ensure that teams focus on strategic projects.

In addition, incorporating change management in the pilot phase is key for stakeholder engagement. It creates structure, raises awareness and reduces the anxiety associated with its use for all stakeholders. If your workforce and partners don’t adopt the new AI solutions, your investment cannot be fully realized.

Having a thorough AI transformation plan—including consideration of AI governance, practical AI capabilities embedded into core operations, as well as a long-term plan for scalable solutions that take into account the impact all stakeholders—will set you apart from other contenders trying to navigate the still-Wild West of Artificial Intelligence.

With these strategies in place, C-suite executives such as the ones we’ve partnered with in communications, media, and technology, are in a stronger position to generate revenue, reduce costs, and gain competitive advantage in the market sooner rather than later.

But we’re confident that these methodologies and best practices apply across all industries, and to every leader seeking big wins in the age of AI.