The adoption of artificial intelligence (AI) and large language models (LLMs) is accelerating across many industries. Early investigation and assessment have evolved into a scramble to adopt AI and put it to work solving problems, automating services and managing everything from customer service to cybersecurity. But, often, this scramble puts the cart before the horse. AI is an accelerator, but not a compass; a capability, but not a strategy; a force-multiplier, but not a decision-maker. In other words, it can certainly get you to where you are going faster. But are you going in the right direction?
For management, AI promises greater productivity at lower cost. For employees, benefits are less clear. Many top-down implementations focus on speed and headcount reduction, not strategy. Organizations often lack structure to maximize AI’s value, while employees remain hesitant, bearing responsibility for errors AI systems cannot own.
While there has been great emphasis on what AI can do, less thought has been given to what it can’t. Understanding its limitations and drawbacks is crucial to integrating it effectively. Giving your teams the right strengths to complement AI’s weaknesses will let it serve as the accelerator it has been promised to be.
Helping you get there, not deciding where to go
Cars were a transformative technology for travel when they were introduced. They could get passengers virtually anywhere faster and with less hassle compared to saddling a horse. GPS, similarly, turned road trips from arguments across a mangled, manhandled map to seamless journeys even to brand-new destinations. But neither innovation is of much help if you don’t know where to go. And neither a car nor GPS will give you back the time and gas you waste going to the wrong place.
The same is true about AI. Any LLM can give you suggestions about where to steer your organization or what the best way to tackle a given project might be. But building a strong AI strategy is a more complex process, requiring you to follow the POST model:
- Profiling: How to understand your context.
- Objectives: How to define your values and vision and set your goals.
- Strategy: How to figure out how to get there.
- Tactics: How to implement and iterate.
Properly used, any AI tool can provide useful information on externalities: opportunities, risks, etc. But it can’t define your values and vision or help you decide where and how your strengths and capabilities as an organization fit with your market objectives. Without clearly defining these, it’s very likely your organization will end up with the wrong market strategy. Throwing AI into the mix just takes you from implementing the wrong strategy to implementing the wrong strategy faster.
Technology alone cannot build trust
Even the most ambitious AI adoption strategy will not replace every human in an organization, for obvious reasons. That means there will still be people involved and teams that will be part of the business-critical decision-making process and execution strategy. Unfortunately, if you thought prompt engineering was difficult, getting buy-in and trust from diverse and multi-skilled teams can be even more challenging.
As Francis Frei and Anne Morriss explain in Harvard Business Review, the first and most important ingredient of effective leadership is trust. An especially well-prompted AI may provide some of the theoretical knowledge about how to build trust, but it can’t implement it for you. As Frei and Morriss explain, building trust rests on three pillars:
- Authenticity which involves being genuine rather than playing a role.
- Clearly articulated logic that teams can understand and follow.
- Empathy which ensures people feel heard and respected.
No matter how advanced AI becomes, it cannot build authentic trust or empathy. Humans will always remain the ones who bring these principles to life through their actions, relationships and daily interactions.
Accountability is human-only
Another important consideration is that AI models will never be perfect. And they are, unfortunately, immune to consequences.
Consider the relationship between AI and news information. So far, there’s reason to suspect AI may be struggling to get it right. BBC research suggests more than half of news queries by major AI tools have major errors of some kind. One can only presume what that ratio looks like on other issues, particularly more niche or technical questions, where datasets are smaller and even minor errors can be hugely significant.
This can, of course, be improved and will be (in theory). At an industry level, work continues to make better models. At the organization level, improving data management and breaking down information silos will improve the volume and quality of data available. But that is a slow, complex and expensive process.
The reality is that even the most sophisticated AI models lack accountability. LLMs don’t analyze meaning, only correlation. If an AI tool gets the correlation correct, based on its analysis of existing material, that will be its output, whether the words themselves are meaningful or not. AI cannot be relied upon for truth, insight, or alignment with your organization’s objectives — it simply reflects patterns in existing data.
Giving employees AI tools without training leaves them unprepared and unmotivated to validate results. Relying blindly on AI is a recipe for disaster. As a result, everyone in the organization, from the C-suite to entry-level employees, needs skills to complement AI’s strengths, compensate for its weaknesses and cover its gaps.
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About the Author
Raphael Renaux is a global management expert with more than 25 years of experience leading multicultural teams, driving organizational transformation and advancing leadership, team building, and performance excellence worldwide.
