Impact Intelligence is the title of my latest book. It explains how
to improve awareness of the business impact of new initiatives. The
Classic Enterprise thinks of the expenditure on these initiatives as
discretionary spend. A software business might account for it
as R&D expenditure. Written with a framing of investment
governance, the book is aimed at the execs who approve investments.
They are the ones with the authority to introduce change. They also have
the greatest incentive to do so because they are answerable to
investors. But they are not the only ones. Tech CXOs have an incentive
to push for impact intelligence too.
Consider this. You are a CTO or other tech CXO such as a CIO or CDO
(Digital/Data). Your teams take on work prioritized by a Product organization or
by a team of business relationship managers (BRM). More than ever, you are being asked to report and
improve productivity of your teams. Sometimes, this is part of a budget
conversation. A COO or CFO might ask you, “Is increasing the budget the
only option? What are we doing to improve developer productivity?” More
recently, it has become part of the AI conversation. As in, “Are we using
AI to improve developer productivity?”. Or even, “How can we
leverage AI to lower the cost per story point?” That’s self-defeating
unit economics in overdrive! As in, it aims to optimize a metric
that has little to do with business impact. This could, and usually does, backfire.
While it is okay to ensure that everyone
pulls their weight, the current developer productivity mania feels a bit
much. And it misses the point. This has been stressed time
and again.
You might already know this. You know that developer productivity is in
the realm of output. It matters less than outcome and
impact. It’s of no use if AI improves productivity without making a
difference to business outcomes. And that’s a real risk for many companies
where the correlation between output and outcome is weak.
The question is, how do you convince your COO or CFO to fixate less on
productivity and more on overall business impact?
Even if there is no productivity pressure, a tech CXO could still use the guidance here
to improve the awareness of business impact of various efforts. Or if you are a product CXO, that’s even better.
It would be easier to implement the recommendations here if you are on board.
Impact Trumps Productivity
In factory production, productivity is measured as units produced per
hour. In construction, it might be measured as the cost per square foot.
In these domains, worker output is tangible, repeatable, and performance
is easy to benchmark. Knowledge work, on the other hand, deals in
ambiguity, creativity, and non-routine problem-solving. Productivity of
knowledge work is harder to quantify and often decoupled from direct
business outcomes. More hours or output (e.g., lines of code, sprint
velocity, documents written, meetings attended) do not necessarily lead
to greater business value. That’s unless you are a service provider and your
revenue is purely in terms of billable hours. As a technology leader,
you must highlight this. Otherwise, you could get trapped in a vicious
cycle. It goes like this.
As part of supporting the business, you continue to deliver new
digital products and capabilities. However, the commercial (business)
impact of all this delivery is often unclear. This is because
impact-feedback loops are absent. Faced with unclear impact, more ideas
are executed to move the needle somehow. Spray and pray! A
feature factory takes shape. The tech estate balloons.
Figure 1: Consequences of Unclear Business Impact
All that new stuff must be kept running. Maintenance (Run, KTLO)
costs mount. It limits the share of the budget available for new
development (Change, R&D, Innovation). When you ask your COO or CFO
for an increase in budget, they ask you to improve developer
productivity instead. Or they ask you to justify your demand in terms of
business impact. You struggle to provide this justification because of a
general deficit of impact intelligence within the organization.
If you’d like to stop getting badgered about developer productivity,
you must find a way to steer the conversation in a more constructive
direction. Reorient yourself. Pay more attention to the business impact
of your teams’ efforts. Help grow impact intelligence. Here’s an
introduction.
Impact Intelligence
Impact Intelligence is the constant awareness of the
business impact of initiatives: tech initiatives, R&D initiatives,
transformation initiatives, or business initiatives. It entails tracking
contribution to key business metrics, not just to low-level
metrics in proximity to an initiative. Figure 2 illustrates this with
the use of a visual that I call an impact network.
It brings out the
inter-linkages between factors that contribute to business impact,
directly or indirectly. It is a bit like a KPI tree, but it can
sometimes be more of a network than a tree. In addition, it follows some
conventions to make it more useful. Green, red, blue, and black arrows
depict desirable effects, undesirable effects, rollup relationships, and
the expected impact of functionality, respectively. Solid and dashed
arrows depict direct and inverse relationships. Except for the rollups (in blue), the links
don’t always represent deterministic relationships.
The impact network is a bit like a probabilistic causal model. A few more conventions
are laid out in the book.
The bottom row of features, initiatives etc.
is a temporary overlay on the impact network which, as noted earlier, is basically a KPI tree where every node
is a metric or something that can be quantified. I say temporary because the book of work keeps changing
while the KPI tree above remains relatively stable.
Figure 2: An Impact Network with the current Book of Work overlaid.
Typically, the introduction of new features or capabilities moves the
needle on product or service metrics directly. Their impact on
higher-level metrics is indirect and less certain. Direct or first-order
impact, called proximate impact, is easier to notice and claim
credit for. Indirect (higher order), or downstream impact,
occurs further down the line and it may be influenced by multiple
factors. The examples to follow illustrate this.
The rest of this article features smaller, context-specific subsets
of the overall impact network for a business.
Example #1: A Customer Support Chatbot
What’s the contribution of an AI customer support chatbot to limiting
call volume (while maintaining customer satisfaction) in your contact
center?
Figure 3: Downstream Impact of an AI Chatbot
It is not enough anymore to assume success based on mere solution
delivery. Or even the number of satisfactory chatbot sessions which
Figure 3 calls virtual assistant capture. That’s proximate
impact. It’s what the Lean Startup mantra of
build-measure-learn aims for typically. However, downstream
impact in the form of call savings is what really matters in this
case. In general, proximate impact might not be a reliable leading
indicator of downstream impact.
A chatbot might be a small initiative in the larger scheme, but small
initiatives are a good place to exercise your impact intelligence
muscle.
Example #2: Regulatory Compliance AI assistant
Consider a common workflow in regulatory compliance. A compliance
analyst is assigned a case. They study the case, its relevant
regulations and any recent changes to them. They then apply their expertise and
arrive at a recommendation. A final decision is made after subjecting
the recommendation to a number of reviews and approvals depending on the
importance or severity of the case. The Time to Decision might
be of the order of hours, days or even weeks depending on the case and
its industry sector. Slow decisions could adversely affect the business.
If it turns out that the analysts are the bottleneck, then perhaps it
might help to develop an AI assistant (“Regu Nerd”) to interpret and
apply the ever-changing regulations. Figure 4 shows the impact network
for the initiative.
Figure 4: Impact Network for an AI Interpreter of Regulations
Its proximate impact may be reported in terms of the uptake of the
assistant (e.g., prompts per analyst per week), but it is more
meaningful to assess the time saved by analysts while processing a case.
Any real business impact would arise from an improvement in Time to
Decision. That’s downstream impact, and it would only come about if
the assistant were effective and if the Time to initial
recommendation were indeed the bottleneck in the first place.
Example #3: Email Marketing SaaS
Consider a SaaS business that offers an email marketing solution.
Their revenue depends on new subscriptions and renewals. Renewal depends
on how useful the solution is to their customers, among other factors
like price competitiveness. Figure 5 shows the
relevant section of their impact network.
Figure 5: Impact Network for an Email Marketing SaaS
The clearest sign of customer success is how much additional revenue
a customer could make through the leads generated via the use of this
solution. Therefore, the product team keeps adding functionality to
improve engagement with emails. For instance, they might decide to
personalize the timing of email dispatch as per the recipient’s
historical behavior. The implementation uses
behavioral heuristics from open/click logs to identify peak engagement
windows per contact. This information is fed to their campaign
scheduler. What do you think is the measure of success of this feature?
If you limit it to Email Open Rate or Click Through Rate you
could verify with an A/B test. But that would be proximate impact only.
Leverage Points
Drawing up an impact network is a common first step. It serves as a
commonly understood visual, somewhat like the ubiquitous language of
domain driven design.
To improve impact intelligence, leaders must address the flaws in their
organization’s idea-to-impact cycle ( Figure 6).
Although it is displayed here as a sequence, iteration makes it a
cycle.
Any of the segments of this cycle might be weak but the first (idea
selection) and the last (impact measurement & iteration) are
particularly relevant for impact intelligence. A lack of rigor here
leads to the vicious cycle of spray-and-pray ( Figure 1). The segments in the middle are more in the realm
of execution or delivery. They contribute more to impact than to impact
intelligence.
Figure 6: Leverage Points in the Idea to Impact Cycle
In systems thinking, leverage points are strategic intervention
points within a system where a small shift in one element can produce
significant changes in the overall system behavior. Figure 6 highlights the two leverage points for impact
intelligence: idea selection and impact measurement. However, these two
segments typically fall under the remit of business leaders, business
relationship managers, or CPOs (Product). On the other hand, you—a tech
CXO—are the one under productivity pressure resulting from poor
business impact. How might you introduce rigor here?
In theory, you could try talking to the leaders responsible for idea
selection and impact measurement. But if they were willing and able,
they’d have likely spotted and addressed the problem themselves. The
typical Classic Enterprise is not free of politics. Having this
conversation in such a place might only result in polite reassurances
and nudges not to worry about it as a tech CXO.
This situation is common in places that have grown Product and
Engineering as separate functions with their own CXOs or senior vice
presidents. Smaller or younger companies have the opportunity to avoid
growing into this dysfunction. But you might be in a company that is
well past this orgdesign decision.
