So, once again, welcome back to our course on Strategic Innovation.
So, in this video,
we're going to start the second set of lessons in this module.
As I mentioned, the second group of lessons covers
recent research on the execution side of innovation.
Vijay Govindarajan and Chris Trimble
of Dartmouth are responsible for this interesting work.
And their central finding as I mentioned again is
that while there's a need to have a dedicated team and it's going to
look a lot like a heavyweight team for challenging innovation projects success
equally involves forming a partnership between
that dedicated team and the mainstream organization.
But they don't get to that partnership
and dedicated team right away and we shouldn't either,
this was in-depth research that represented 10 years of work.
They looked in detail at numerous innovation initiatives out in the field.
A number of observations and insights emerged and
you'll see a number of connections to what we've talked about the course already.
So, let's just jump in and start with the title.
The title is revealing to the other side of innovation
and they're referring to the execution side as opposed to the idea generation side.
And they're starting observation is that in most models of innovation,
execution is under emphasized.
They see firms in advice devoting a great deal of
attention to generating ideas and testing them,
but less to execution.
The process of moving from that idea once you have approval for it to impact,
and managers recognize this.
Managers recognize that on the one hand,
their strategy must encompass innovation.
Okay, that message over the past 20 years has been pounded into firms, right?
But the management tools to push innovation forward are limited.
One CEO is quoted in the book is saying that he
knows a lot about how to encourage idea development in his organization,
but the execution side is limited by management capability,
and he felt that there weren't a lot of tools he could draw on.
So, when we think about our model for successful innovation,
we want to emphasize it's not innovation equals ideas,
rather it's going to be innovation equals ideas plus execution.
And all of this supports our courses focus on managing innovation, right?
Where execution is a course at center stage.
Now, if execution is a challenge,
at the same time, it's important to recognize that challenge is not always overwhelming.
Innovation cuts a wide swath that encompasses
many different efforts and many different kinds of efforts.
And there are some common models of innovation that managers hold,
which involve building innovation into
established organizations and where they can point to some notable successes.
It is the first thing that the authors talk about.
They identify two common innovation models that have had some success.
One is innovation equals ideas plus motivation.
Second, innovation equals ideas plus process.
So take the first,
the idea is that to execute on your ideas,
the key is motivation, getting people charged up and
spending their efforts on innovation.
So, you provide incentives,
celebrate innovation successes, build a culture of innovation.
So for example, Nucor, a steel company,
a mini-mill company is a great, great success story.
Nucor was very clever in building, production teams,
cross training them to develop ideas
and then providing incentives and managerial encouragement
for those teams to continuously improve the steel output in their mills ton by ton,
and they built their enormous success on the back of
this model of innovation equals ideas plus motivation.
The second model where innovation equals ideas plus process,
the authors often found that in companies that confront
repeated innovation challenges like developing successive generations of a product.
Now these companies will build detailed processes,
detailed guides for the execution of these efforts and can see a lot of success.
For instance, they talk about Deere and
its successive generations of their flagship tractor,
award winning tractors developed through this model.
So, even though these models have a lot of successes they can point to,
research found that they're limited.
And the core reason and the core way that we can explain their limits
is evident when we look at how organizations are built and develop over time.
And the kind of organization that we're talking about here is
what I've referred to in previous talks as the mainstream organization,
the central body of the firm.
As that evolves and has success we talked about in
the ambidexterity discussion that
there's pressure towards making things repeatable, right?
To go ahead and break it down say,
if we go through these steps, we'll succeed.
We can specialize that way people can become expert in each step of a repeatable process.
We can become efficient, because we can figure out
exactly how many resources we need for it.
And related to that idea of repeatability is the idea that things become predictable.
Over time, we can look to the past and
understand that the present is not going to be that different,
and so we can start to use results based management.
We can hold managers accountable for results,
because we have a pretty good idea what those results might be.
We can forecast them from the past.
Now, this process of developing
an organization that is built on repeatability and predictability,
Govindarajan and Trimble talk about
this part of the organization as the performance engine
and that's the same thing is
the mainstream organization in terms of what I've talked about before.
But I like that term because it suggests power,
and this performance engine can create growth,
it can create new products like we saw with Nucor and with Deere.
It can innovate, right?
It just can't handle every innovation.
And it tends to stumble,
when we undertake a major initiative,
which instead of being repeatable, is non-routine.
Where instead of being predictable, it's unpredictable.
That is, we do something where the past and our design,
that is built on the past.
Is not an effective guide and in fact would likely head us in the right direction.
So for instance, if you're Aetna and you're used to
ensuring group plans and you want to get into individual health insurance,
the past is not going to be a good guide.
If you're BMW and you're trying to build
a hybrid car and you're used to building standard brakes,
but you now want to build a regenerative braking system,
the past is not going to be a good guide.
And so, the models that we've talked about,
innovation equals ideas plus motivation,
or ideas plus process are going to turn out not to work out so well.
And you should know as I'm talking through this,
that it evokes ideas of alignment,
the diamond models that we used before,
it's another way of saying the same thing.
And in fact, let's connect this also to the S curve model,
that we had covered in earlier units.
Now, if you remember the S curve,
the idea is that over time
the technical performance of a technology of a product improves.
We used the example you remember of airplanes, right?
And early on there's a lot of product experimentation,
a lot of exploration of the market and the customer.
As things develop, and the market matures,
and the technology matures,
we move towards process improvements,
segmentation of the market and differentiation,
lowering costs and efficiency.
So, Tushman and Anderson call the more mature side,
the era of incremental change.
Where reliability, and predictability,
and efficiency are the rule.
And past data usefully forecast the future.
Whereas, the early stage they call the era of ferment,
where uncertainty is dominant.
And there's many assumptions built
into everyone's ideas about how things are going to work.
Okay. So, now we can come back and relate these ideas talk a little bit
more about the innovation model that
you have in your head and what the performance engine can and can't do.
So, if we think about that innovation equals idea plus motivation model,
where the kind of continuous improvement metaphor
that we saw in new core represents success.
The limit is that,
because we have pressures for efficiency,
because we are judged on our results as managers,
there's only limited resources that any individual or
any group is going to be able to apply to innovation in this approach.
So, when you get to something that's beyond
the scope of a continuous improvement initiative,
you start to have trouble with resources.
People start to get pulled to the their mainstream task,
the performance engine tasks and it's got to do well for the company to succeed.
And then on the idea plus process model,
the issue is repeatability.
Right? If we have a process that we can build our innovation around,
like generations of a product,
we're in good shape.
But, when we can't look to the past to guide what we're doing,
when we can't forecast the future,
then working within the performance engine,
and working within a process that's
built around the performance engine, isn't going to work.
So, where organizations go often,
is to say all right well what we need to do for this more dramatic idea,
this more dramatic initiative,
is we need a leader.
We need a great leader and that leader is going to help change the organization,
so that it can do this new thing.
And the trouble here,
is that this approach is a bad bet,
because the mainstream organization that
performance engine is built to fight dramatic innovation.
Remember, it's built to be repeatable,
it's built to be predictable.
It has processes, it has culture,
it has standards built around that.
And so what happens, is that the leader ends up being pushed
towards break all the rules, kind of approach.
And then they're ending up fighting the mainstream organization.
This is big and powerful,
and so this is not a good bet.
What Govindarajan and Trimble suggest is that,
we're in a model where yes the leader is critically important,
because there is a lot of change involved here.
But we have the idea, and the leader,
and a team, and a plan.
Okay? And to go forward and think about how we implement that model right.
We can look back at this same chart and figure out what we need to do,
when we confront a complex innovation initiative that's
non-routine, and unpredictable right?
So the first thing is,
well we're going to need a separate organization.
Common refrain.
We're going to need to bring our people out of the mainstream organization,
so they can do things differently.
But one reason I'm excited about this research is that
the separate organization recommendation that we've seen in many places,
can be a bit general, even vague.
And here, we take this core idea and develop it in-depth.
And so, in particular they suggest look,
the fact it's non-routine,
means that this kind
of innovation initiative is going to need a custom organizational model.
We're going to design it, specifically for the parameters of this initiative.
We're going to have its own processes,
its own culture, and so on.
It also has to account for the fact that it's an unpredictable situation.
So, we not only need a custom organization,
we need a custom plan,
a plan where the goals and measures on
accountability are built around the needs of this initiative.
So, that's where we're going to be building.
At the same time,
they also observe that building
a separate organization is an expensive and challenging thing.
This is a heavy duty kind of solution.
And so, you want to use it only to the extent that you need to.
And often, some resources can and should stay within the mainstream organization,
be part of the innovation initiative,
in the parts where it's within their capabilities,
and contribute to the innovation initiative from there.
So, this is what the innovation equals ideas,
plus leader, plus team,
plus plan looks like in graphic form.
You see that we have the company
drawn and the performance engine is the large part of it.
The diamond is dedicated team.
Looks a lot like a stand alone team.
But the initiative is integrally a partnership
between the dedicated team and what they call the shared staff.
The shared staff are the people who are part of the innovation initiative,
but they continually based within the performance engine or mainstream organization.
They're part time typically.
And the key is to take these two parts,
develop a partnership between them.
So, in that sense it looks a little bit like the heavyweight team approach.
Okay? But critically the dedicated team has a variable role.
It can take on most of the project or only specific aspects of it,
depending on what's new,
what's predictable, what's not.
So, to the extent that the dedicated team starts to take on just about everything,
it starts to look a lot like
the semi-autonomous units that we've seen in the ambidexterity work.
Okay. So, you see how this model is going to lead
us to some insights that I think integrate what we've seen before,
and apply to where we seen before, and then further inform it.
And we end up with three things really to explain further.
The first, we say the team is a custom organization.
What does that mean?
How do you build that organization?
We also say the team is in a partnership.
How do you manage that partnership?
And then we say we have a plan; goals, metrics, accountability.
That is going to be designed around the uncertainty heavy,
assumption rich world of challenging innovation initiatives.
What does that look like? And answering these questions,
is where we're headed in the next two lessons.