STEP 1:
MAKE THE BUSINESS CASE
Leaders like you are already thinking about AI, even if it’s
unclear where or how to deploy it. You see its promise and potential – but
you’re also highly focused on driving business value. Two-thirds of SMBs report
that AI has already had a very big or modest impact on their businesses to
date, and just over 75% expect this level of impact in the next two years,
according to the SMB Group.1 AI-driven automation is their initial goal: reduce
manual tasks, analyze large datasets to detect patterns for decisions, and
enhance end user experience. You need an AI business case that articulates how
to drive business outcomes, not just science experiments. Prioritize with a
narrow focus to identify what will deliver the most high value impact to your
business. Describe the risk of doing nothing and the potential of business
disruption from new entrants or competitors. More importantly, document your
ideas and use cases for how AI could be the catalyst to transform your
business. Kick automation to the next gear for more flexibility, speed, scale,
and personalization. There are numerous AI Business Case Guide – a practical,
detailed guide that can serve as your road map, specifically designed for the
unique challenges and opportunities of AI.
Business Impact of AI in SMB
|
|
VERY BIG IMPACT |
MODEST IMPACT |
SMALL IMPACT |
NO IMPACT |
DON’T KNOW |
|
IMPACT TO DATE |
35% |
32% |
16% |
16% |
4% |
|
POTENTIAL IMPACT IN 2 YEARS |
38% |
37% |
13% |
9% |
3% |
STEP 2:
IDENTIFY AI OBJECTIVES
Many executives encourage their
teams to concentrate their efforts on answering a single “north star” question
– one that has the single greatest impact on everyday business decision making
and which ultimately spawns many more questions. 1-800-Flowers.
com, the online flower and gift retailer, defines their north star metric as
customer frequency: the number of times a customer buys from the company
annually. Company leaders know that the cost of acquiring a first-time customer
far outweighs the cost of subsequent purchases, so they maintain an intense
focus on repeat business that reverberates throughout the organization.
Customer frequency isn’t the only metric the company tracks, but it is the most
important one, affecting virtually all business decisions the leaders make.
Start with absolute clarity on what exactly you’re seeking to accomplish with
AI. What is the business problem that you need to solve to drive significant
business impact? What tools are needed to measure your success toward those
goals? What is the plan for communicating progress with key stakeholders, since
celebrating successes, even small ones, is vital? To increase their odds of
success, SMBs are starting small and building AI capabilities and capacity as
they go. They are using AI to streamline and automate analytics tasks: gaining
faster access to information in order to make informed, data-driven decisions.
QUESTIONS
which facilities should
manufacture given the shelf life of the drug and proximity to clients?
what is the lifetime value of
each of my customers?
which of my customers are at risk
to drop my product or service?
what is the best treatment
strategy for someone overcoming addiction?
what is ultimate production level
to have just enough to meet demand and avoid waste?
which loan or credit applications
are fraudulent?
DECISION
when should we perform maintenance on high value equipment
to prevent downtime?
STEP 3:
UPDATE TALENT STRATEGY
You’ve worked hard to recruit, retain, and grow great
talent. But today, your people may not be ready for AI. Now is the time to
update your talent strategy and execute change management initiatives. Create a
short list of curious, capable, pragmatic team members who could be prepared to
lead the way on AI. From there, AI-oriented change management tactics can help,
including providing extra support, training, and coaching. Many of the people
on your team today already have a solid baseline of skills required to be
successful in AI – by actively augmenting those skills with training focused on
their technical and analytical capabilities, it’s possible to quickly ramp up
your organization’s AI capacity. Be honest about your talent gaps. There are a
growing number of partners who can be pulled in for both short- or long-term
projects to help your AI strategy get off to its very best start – and to
continue delivering at a high level. Some, like software resellers, are purely
transactional. Others provide more in-depth capabilities such as staff
augmentation, data management and analysis, hosting, and much more.
STEP 4:
GET YOUR DATA IN ORDER
Data readiness is probably the least glamorous, most
overlooked, and most important element of any AI strategy. Like traditional
analytics, success with AI is data dependent. At many SMBs, data is often
improperly handled, or managed on an as-needed basis: There’s plenty of it, but
only a limited segment of the data gets used to inform business decision
making. If that sounds familiar, it’s time to advance your data strategy to
ease the transition into AI capabilities. Start by focusing on two aspects of
data: access and quality. Your data needs to be meaningful – not perfect – for
you to act on it with confidence. Since your data is constantly accumulating,
determining best practices for storage and access can be challenging. Partners
who offer data pipeline and data strategy services can help make sure your data
meets the standards for AI usage, working to ensure it’s available in the right
formats when needed. .
STEP 5:
START YOUR AI IMPLEMENTATION
Given how quickly AI technology is advancing and changing,
make sure to keep a pulse on the capabilities (and limits) of AI technologies
to determine where you want to start. There is no need to go “all in” on AI on
day one. You can be successful starting small and growing into a more
analytically mature business. For example, many SMBs are heavily reliant on
spreadsheets for decision making. One customer had reached their limit with
the 3.5 day, 34 step process required to generate a win-loss report. With
automation, the business implemented an “always on self-service portal’ with
interactive, web-based dashboards. The business impact: insights drove product
direction, optimized sales staffing, and launched new campaigns. The case
study details the two-phased project, as shown on the right. Evaluating your
existing data and analytics technology is a great way to assess your readiness
for AI. Where do you already have strengths that might bolster your AI
strategy? Consider that the majority of work required to enable AI will focus
on data – gathering it, prepping it, analyzing it, creating visualizations, and
understanding relationships, trends, and outliers in the data. These are all
areas where you can begin assessing your organization’s AI readiness today
SAS Case Study: Win-Loss Reporting
|
34 Steps-
3.5 days Quarterly BEFORE |
13 Steps –
0.5 Day Monthly AFTER – PHASE 1 |
Always On
& Interactive AFTER – PHASE 2 |
|
Insert, copy, 7 Vlook ups |
Insert, copy, 7 Vlook ups ! ! |
1 time set up of data sources |
|
21 Pivot tables -32 graphs |
Upload data to SAS |
21 interactive self-service dashboards |
|
Copy data to create charts |
Live, interactive web-based dashboards |
Live, interactive web-based self-service portal |
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