In business, it's easy to get distracted by the latest buzzword or shiny object. Lately, AI and Machine Learning have been seen as silver bullets, but blindly chasing trends can do more harm than good. Many companies seem to feel the pressure to jump on the bandwagon, convinced it's part of the secret to remaining competitive. Yet, few of these same companies realize any innovation, instead each coming with their brand of chatbot. Just because AI, or anything else, is popular doesn't mean it's the best focus for the business.

Pushing a team to pursue AI or any other trendy, shiny object without a clear reason, plan, or alignment with business objectives can backfire, wasting precious resources, introducing new complexity, wasting or preventing the build-up of momentum, and lowering team morale. Doesn't sound great, does it? We must focus on meaningful outcomes instead of chasing shiny objects and ensure we make sound strategic decisions.

Chasing for value or chasing because shiny?

Imagine you’re at a company where the leadership has suddenly decided that AI is the next big thing everyone should focus on. But here’s the catch: no one can clearly explain why AI is necessary, the comparison and trade-offs versus other solutions, how it aligns with the company’s strategy, or even what specific problems it would solve. Worse, some solutions would be of more value to your customers, could bring you more value, or your team doesn’t have the skills or expertise to make AI work effectively.

This is a classic example of chasing something because it's the new shiny object of the moment—it’s trendy, and without a solid plan or purpose.

What’s wrong with this approach?

Let’s break down why this–or any– approach can be problematic:

1. AI is not a strategy; it’s a tool:
   AI is a powerful tool that can help businesses solve specific problems, but it’s not a strategy. Using AI just because it’s popular won’t magically improve your business. It would be best to have a clear purpose for why and how it fits into your goals. What clear business problem does this solve? What clear customer problem does this solve? Why is it the best choice of all options?
2. Lack of strategic fit:
   Strategic fit: "the degree to which an organization is matching its resources and capabilities with the opportunities in the external environment."–Wikipedia
   If your business model and customer needs don’t align with what AI can do, then pursuing it is a distraction. If your team isn’t skilled in AI, it will take time, money, and effort to build that capability. Is that important right now? That’s time you could spend on projects that actually align with your strengths.
3. Low team morale:
   If your team feels like they’re being forced to work on something they don’t believe in—or worse, don’t understand—they can become disengaged. This can lower productivity and lead to higher turnover.
4. Ethical fit and risk:
   Depending on the type of AI, if it were trained on other people's content and they aren't receiving compensation for subsequent work of that AI, many people find that to be quite problematic. Would that align with your business practices? What would your customers think if it were put into the spotlight?
5. Near-sightedness
   Investing in AI, whether through partnership, integration, hiring, or otherwise, can incur significant investment. Is that investment going to have long-term gains, or have you thought that through? How does this become a strategic differentiator with long-term implications?

Where to focus

Let's take a step back. What's a good approach?

1. Focus on outcomes, not solutions

• Before deciding to use AI (or chase any other shiny object), business leaders should ask themselves: What outcome are we trying to achieve? Are we trying to improve customer satisfaction? Reduce costs? Increase user engagement? Define the goal first, then explore different ways to achieve it.
• For example: If your goal is to reduce customer support requests, explore solutions like self-service resources and improving user experience—AI may not even be necessary. Plus, exploring options in this way may increase existing team strengths, and compound gains. Improving the experience to reduce customer support volume can also increase customer engagement, value, and trust.

2. Leverage existing strengths

• Before diving into AI, focus on optimizing the areas where your business already excels. What separates your business from the competition? Do you have a strong brand, loyal customers, unique technology, workers strong in a particular skill, or something else?
• Minor improvements in core strengths, or utilizing them to take advantage of key opportunities, often yield more significant results than chasing a shiny new trend.

3. Put the customer first

• Instead of focusing on shiny things, focus on understanding what your customers are trying to achieve. What are their deeply held needs, problems to solve, or reason to hire you and your product to help them?
• This approach is common in the Jobs to Be Done (JTBD) framework, Product Experience (PX) and Product Management (PM) work. Identify real problems customers face so you can find the best solutions—whether that involves AI or not.

4. Start small and iterate at the ground level

• If you’re curious about AI but have no proof it should become a major strategic focus or lever for your business, start with small experiments. Give your teams room to try things, train them, do a small experimental sprint, test a pilot or proof of concept to see if AI can truly add value.
• Enable and empower your people to try big, new, innovative approaches, exploring whatever will solve the customer's needs best– needs that your team are often closest to.

Strategic frameworks that help drive or hone in on results

OKRs (Objectives and Key Results) or Rocks
Focus on setting clear, measurable goals. Let your teams decide how to achieve those goals rather than prescribing solutions like AI from the top down.

McKinsey’s Three Horizons framework
Balance your focus between short-term, mid-term, and long-term goals. If AI is a long-term play, treat it as such and focus on your core strengths in the short term.

SWOT analysis
Assess your company’s Strengths, Weaknesses, Opportunities, and Threats. This will help you determine if investing in AI is a good idea, or if your efforts are better spent elsewhere.

PESTEL analysis
Make sure you have a good idea of the Political, Economic, Social, Technological, Environmental, and Legal landscape before making big bets can be crucial. As your team grows to include experts like Product Marketers, Competitive Intelligence, Researchers, and Analysts, they will help your team hone in on this type of information and feed your strategy.

Important shifts in leadership

Simply providing you with new tools and frameworks isn't going to fix the problem of chasing shiny objects. An actual change in thinking is required to stay focused and form an impactful, long-reaching strategy that can be acted upon with conviction. I recommend a few key areas of training– leadership styles that I have found beneficial in the past:

Situational leadership
Adapt your leadership style based on your team’s needs. Instead of telling them what to do, focus on empowering and supporting them to find the best solutions. Know when to switch between directing, delegating, coaching, and supporting leadership styles.

This is the type of thing people can just read about. I have been on several teams that thought they had excellent situational leadership, yet when we did actual training in this area, they struggled and failed the (thankfully for them, non-graded) tests. However, the tests, inquiry-based learning approach of the training, and roleplay seemed to help those folks unlock new skills and empathy that benefitted them, the company, and their teams.

Servant leadership
Focus on enabling and empowering your team rather than controlling every detail. Trust your team to develop creative solutions that align with your business goals.

I think this works best in tandem with situational leadership. It can really help bring others along, help them help themselves, and steward a new batch of leaders.

Transformational leadership
Inspire your team with a clear vision and long-term goals rather than jumping on trends. Help them see the bigger picture and how their work contributes to it.

A good transformational leader is a source of inspiration and influence that motivates teams toward lasting change. Similar to servant leaders, transformational leaders love to enable and draw the best out of their people through collaboration, not dictation.

Key Takeaways

1. Don’t chase trends: Just because everyone talks about AI or some other shiny new object doesn’t mean it’s the right move for your business.
2. Focus on outcomes: First, define what you’re trying to achieve, then explore the best ways to achieve it.
3. Work from the lens of those you serve: Look at what your team needs to be effective and what meaningful problems your customers have to solve.
4. Empower your team: Give your teams the autonomy to find long-term solutions that work rather than prescribing short-term solutions that might not fit.
5. Enable your team: So, you're interested in X shiny new thing? Start small, train teams, and remove barriers to them trying new things. Build them up to be the best problem solvers.

There's an underlying theme here, too. Good leaders know when they need to stay out of the weeds. They're wise enough to recognize the difference between which problems their teams should be closest to and which problems they should be closest to. As businesses scale, a leader's proximity to customer-level problems and understanding the solutions that may address those problems start to be hindered. It takes a lot of emotional, intrapersonal, and interpersonal intelligence to notice that happening and not just let go but enable and empower those who need to take the reigns.

Technology like AI can be compelling—but only if applied in the proper context and with a clear purpose. Before jumping on the next shiny object trend, take the time to understand your goals, your strengths, and the real problems your customers face. That’s the best way to stay ahead of the competition and build a product that truly delivers value.