AI-for-SMB
Is There A Place for AI in Small to Medium Businesses?
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Humberto Farias
Posted on: February 13, 2019
AI
Tags: ai business predictive analytics smb
Tags: ai business predictive analytics smb

Many small to medium business owners view artificial intelligence as something only huge corporations need.

In reality, it can help position them to compete with those corporations on a whole new level.

It seems like everyone in the business world is launching artificial intelligence programs.

That’s partly because nearly everyone is. 61% of businesses have already begun using some form of artificial intelligence, many of those focusing on predictive analytics and machine learning.

71% report they plan to expand their use of predictive analytics and other AI applications over the next year.

For most companies the decision to adopt AI is an easy one.

For small to medium businesses (SMBs), though, there are tough questions to answer.

Even successful SMBs don’t have the same depth of financial resources as a multinational corporation.

They need to invest cautiously, and artificial intelligence can sound like a science fiction daydream.

That’s unfortunate, because artificial intelligence is fast becoming the kind of tool that can help small to medium businesses keep up with their larger competitors.

Read on to explore the things keeping SMBs from investing in artificial intelligence. then find out how to get past them and what technologies are best suited for small to medium businesses.

Practical Artificial Intelligence

“Artificial Intelligence” brings to mind futuristic robots and complex movie plots, but the reality is much simpler.

The term refers to teaching machines to “think” and interpret information like humans do. Humans have very flexible minds.

They can handle a variety of rapidly-changing topics and navigate difficult conditions that confuse computers (although computers have a greater ability to process repetitive data quickly and accurately).

Modern artificial intelligence has come a long way.

It can’t quite mimic human thought yet, but there have been some exciting advances using AI techniques like machine learning and deep learning that show potential for nuanced processing.

The technology is proving its value as an enterprise tool, too.

There are a few common applications that some people don’t realize are based on artificial intelligence:

  • Predictive analytics, especially embedded features in enterprise software
  • Chatbots on websites or social media pages
  • Intelligent assistants in office and productivity software
  • Recommendation engines used for suggesting Netflix titles and upselling in ecommerce

What Holds SMBs Back

Even as larger companies move to wider integrations of artificial technologies, small to medium businesses are slow to adopt.

Their hesitation is understandable – after all, a failed technology project could threaten the future of their company – but it also holds them back.

The truth is, many of their concerns aren’t as serious as they think.

The issues have practical workarounds or can otherwise be mitigated through proper planning.

Here’s why the leading reasons SMBs aren’t adopting artificial intelligence don’t have to be unmovable roadblocks to progress and how they can be overcome.

AI is too expensive

Industry news reports tend to cover high-end artificial intelligence ventures done by major international corporations, with price tags in the millions (or occasionally billions).

That kind of investment is an intimidating prospect for an SMB who just needs a better way to utilize their data.

The thing is, those programs usually involve the most difficult and expensive forms of AI.

Experimental programming, complex interactions, sensitive health information, government-regulated data, huge amounts of simultaneous users, and other complicating factors raise the costs above the average for enterprise AI projects.

SMBs don’t need the same amount of scale or infrastructure. Their modest needs can be met at a much more reasonable price point.

There is no “usual” price for AI. The costs associated with artificial intelligence are based on many factors, including safety and regulatory protocols and the complexity of necessary interactions.

To build an estimate, developers will ask questions such as:

  • Does the program need access to sensitive information?
  • Is it designed to address a specific set of circumstances or is it more a broad-spectrum tool?
  • What level of interaction with humans is desired?
  • What’s the scale involved?
  • Will the AI need to perform complex actions?

Even when a full artificial intelligence program is out of reach, there are ways to integrate AI on a limited budget.

For one thing, AI is included in many enterprise software packages. Most companies already have access to some AI tools, even if they don’t realize it.

Targeting tools in email marketing software and personal assistants on smartphones are both driven by artificial intelligence.

More in-depth AI toolsets are often available with a reasonably-priced software upgrade to enterprise level from free or lower-tier accounts.

It’s work checking with vendors to see what’s within reach.

The rise of reusable code and powerful development frameworks has put small-scale custom solutions within reach, as well.

Developers have platform options for creating analytics dashboards and chatbots that makes the costs approachable for SMBs.

AI isn’t ready for enterprise because the projects fail too often

Project failure is a daunting prospect for SMBs, who usually have a longer list of desired business improvements than they have capital to spend.

They need to prioritize projects because they can’t do everything they’d like.

Investing in AI means putting another project on hold, something they aren’t willing to do when it seems like all they hear about is failed artificial intelligence projects.

It’s easy to become discouraged by high-profile AI failures or assume tools are overhyped, because some projects do fail and some tools are overhyped.

Artificial intelligence is at a point in the Hype Cycle where its applications are being rigorously tested, and some won’t make it through to becoming everyday technologies.

However, project failure is more often an organizational issue instead of a technological one.

Projects fail for a variety of reasons, most commonly:

  • A weak discovery process results in a weak final product.
  • Internal adoption rates are too low to realize the project’s potential.
  • Misaligned business goals lead to the company creating a product that no longer fits within their workflows.
  • The company experiences an outsourcing failure or developer issues.

Avoiding these issues is somewhere small to medium businesses may have an edge over larger corporations. Why?

  • Pushing internal adoption on a small team is more effective because the company leadership can personally talk to everyone (or at least every team leader) to convince them of a project’s value.
  • There is less opportunity for confusion over business needs and goals.
  • The development process has fewer moving parts, so it’s easier to make needs clear during discovery.

What SMBs need to watch out for is the tendency to default to the lowest bidder, especially when outsourcing overseas.

If they focus on quality as much as price, they’re more likely to get a quality return on their investment.

Choosing Agile development methods is another way to ensure a positive outcome.

Developers who use Agile and conduct a thorough discovery are actually seeing a rise in project success rates, and have been for a couple of years.

AI isn’t practical for a small to medium business; it only works for massive corporations.

Many SMB owners see AI as something that can’t help their business.

They assume they don’t have enough data to process or that the impact of AI won’t be noticeable at a smaller scale.

A lot of those same owners would be surprised to realize how much data they already have – data which is going untapped.

Putting that data to work might result in smaller gains, but proportionately those gains matter more.

One interesting thing about AI is that is has opposite benefits for SMBs and larger companies.

It helps giant companies operate with the personalization of an SMB while allowing SMBs to function with the efficiency of a massive corporation.

That is, it gives small to medium businesses the edge they need to “punch outside their weight class” when it comes to competing for market share.

While there are some AI applications that won’t help smaller-scale businesses, there are many more that will.

A small bookshop with five employees wouldn’t get value from predictive scheduling software, but they could see an impressive return on predictive ordering and email marketing programs.

AI doesn’t apply to this industry

There’s a perception that artificial intelligence is only for high-tech fields like software development or banking.

That couldn’t be farther from the truth. AI can be applied anywhere where data is generated – that is, everywhere – to improve efficiency, guide decision making, and maximize the impact of marketing and sales campaigns.

Some examples:

  • A cleaning company uses AI to intelligently manage their leads and upsell current clients.
  • A stroller rental company builds an AI-powered solution to manage their inventory and give customers more options for customizing deliveries.
  • A vacation rental agency uses price optimization to get the best possible pricing on rentals for owners.
  • A landscaping company decides where to expand based on data gathered from predictive analytics tools.

These are all small but important decisions, and they’re made easier using insights gathered by artificial intelligence.

AI is too hard to learn

SMBs tend to have long-time employees in leadership positions with lower turnover in mid-level roles.

They often hesitate to push something that seems high-tech or confusing due to established relationships with employees.

These fears are large unfounded. Building enterprise AI tools is complicated.

Using them is less so, especially with custom tools created specifically for non-technicians.

Most enterprise AI software is designed to be user-friendly at an operator level, so the on-boarding process would likely be much less complicated than SMBs might expect.

Where there are problems, there are well-established training solutions.

The most popular AI tools have online classes at a variety of price points, from free YouTube tutorials to subscription-based professional development platforms.

Developers generally offer training and support packages for their software at reasonable rates.

With so many options even the most technophobic staffer can find a way to get on board with new tools, especially once they realize how much easier AI makes their job.

Staying In The Game With AI

Larger companies are already investing in artificial intelligence.

As they do, they’re gaining a lot of advantages traditionally enjoyed by SMBs, like personalized service and shorter response times to changing local market conditions.

Small to medium businesses have a choice. They can make the AI investment that will help them stay competitive or risk losing their customer base to larger, better-informed companies.

At the end of the day, that isn’t much of a choice at all.

Artificial intelligence doesn’t have to be a headache. Concepta can help you build an intelligent business intelligence solution that fits your needs- and your budget. Schedule your complimentary appointment today!

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Humberto Farias

Humberto Farias is the co-founder at Concepta. He is a seasoned technology professional with over 18 years of experience in the area of web-based applications and software development and now leads a team of developers in the US and Brazil. With experience working on enterprise systems and applications, he has worked for Fortune 500 companies including Walt Disney World and GE.