A decade ago, Apple looked unbeatable. It’s launch of the iPhone completely revolutionized mobile phones and made good on Steve Jobs’ vision of creating a hub of devices that connected people to technology. What’s more, he did it with just a fraction of the research budget of other tech giants.

Yet now Apple seems stagnant. It’s still very profitable, but it’s been ten years since it launched a truly hit product, despite the fact that it has steadily increased its R&D budget. Today, it invests over $10 billion on new technologies, mostly focused on things few people notice, like chips and sensors.

The problem is that while Apple had the perfect strategy and culture to integrate mature technologies into “insanely great” products that dominated the market, it’s ill equipped to tackle other challenges. The truth is that there is no one “true” path to innovation. Every strategy has its time and place. What’s great for one type of problem may fail at others.

The Design Flaw In Design Thinking

One of the most popular innovation strategies, and the closest to Apple’s approach, is design thinking championed by David Kelley, founder of the design firm IDEO and Stanford’s d.school. The process is sometimes summarized as: define, research, ideate, prototype, choose, implement, and learn.

What makes design thinking so effective is its relentless focus on the needs of the end user. Instead of starting with a set of features, it begins by asking what the final experience should be and then works to define a solution. Designers develop products through a series of prototypes and continuously improve and refine them through testing.

So, for example, instead of developing a mobile phone by asking, “what should the keypad look like? A design thinking engineer would start by asking “What does the user want to do with the phone?” In a similar way, a design thinker wouldn’t start designing a doctor’s office by asking where the waiting room should go, but by asking, “what is the purpose of the waiting room?”

As Apple has demonstrated, design thinking can be tremendously helpful when you’re working with mature technologies that are well understood. Unfortunately, they’re not much help when you’re venturing into the unknown to, say, find a new cure for cancer or develop a new approach to artificial intelligence, which may be why Apple has gotten bogged down lately.

The Disruption Dilemma

When Harvard Professor Clayton Christensen introduced the concept of disruptive innovation in his book, The Innovator’s Dilemma, it was a revelation. In his study of why good firms fail, he found that what is normally considered best practice — listening to customers, investing in continuous improvement and focusing on the bottom line — can be lethal in some situations.

In a nutshell, what he discovered is that when the basis of competition changes, because of technological shifts or other changes in the marketplace, companies can find themselves getting better and better at things people want less and less. When that happens, innovating your products won’t help, you have to innovate your business model.

More recently, Steve Blank developed lean startup methods and Alex Osterwalder created tools like the business model canvas and value proposition canvas. These are all essential assets for anyone who finds themselves in the situation Christensen described.

But if you already have a successful product you want to make better, none of that is going to be of much use. Lean startup methods are great for identifying new problems for existing solutions, as in the case of Uber and AirBnB, but they’re not much help if you need to find a new solution to a fundamental problem or improve an existing product to compete in the marketplace.

When To Close Down Open Innovation

One of the best innovation stories I ever heard came to me from a senior executive at a leading tech firm. Apparently, his company won a million dollar contract to design a sensor that could detect pollutants at very small concentrations underwater. It was a very complex problem, so the firm set up a team of crack chip designers and they started putting their heads together.

About 45 minutes into their first working session, the marine biologist assigned to their team walked in with a bag of clams and set them on the table. Seeing the confused looks of the chip designers, he explained that clams can detect pollutants at just a few parts-per-million and when that happens, they open their shells.

As it turned out, they didn’t really need a complex chip to detect pollutants, just a simple one that could alert the system to clams opening. “They saved $999,000 and ate the clams for dinner,” the executive told me. That, in essence, is the value of open innovation. When you have a really tough problem, it often helps to expand skill domains beyond specialists in a single field.

However, most of the time we are need experts in specialized fields to improve performance in areas that are fairly well understood. Many innovation “experts” derisively call this “incremental innovation,” but that’s how most of the value from innovation is produced. The steady improvements of Moore’s Law, to take just one example, have driven the digital revolution.

So, in most cases, what we really need is experts who know their field well. If, for example, those same chip designers needed to improve the performance of a conventional chip by 20%, a marine biologist dumping a bag of clams on their table would have been nothing more than a distraction.

Using The Entire Tool Box

Go to any innovation conference and you will undoubtedly see a wide variety of innovation experts championing their favored strategy and each will have stories that will amaze you. Design thinking, disruptive innovation, lean startup methods and open innovation have all become buzzwords because they have produced real results.

Yet none of them is a cure-all. Each performs well with some classes of problems, but not so well in others. That’s why in my new book, Mapping Innovation, I advocate using the whole innovation toolbox.  The trick is to match the right type of problem with the right type of solution.

The truth is many organizations get stuck because they end up locking themselves into a single strategy. They find something that works and say, “this is how we innovate” and end up trying to apply essentially the same solution no matter what the problem is. Eventually, that ends badly.

That’s why we so often see organizations once billed as great innovators fall behind. They essentially become square-peg companies in a round-hole world and lose relevance. Every strategy fails eventually, because you have to match solutions to problems, not the other way around.