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Tech Bottleneck: 4 Hidden Enterprise AI Challenges

by | Mar 3, 2020

If you’re on the outside looking in, artificial intelligence (AI) and machine learning (ML) might look like a miracle of modern business. From St. Louis to Singapore, enterprises are embracing these emerging technologies at a rapid pace. World-class operations like Alibaba and Facebook aren’t shy about deploying AI, and industry peers are quickly following suit.

That said, the adoption of AI among enterprises isn’t without roadblocks. And if you’re reading this, there’s a good chance you’re painfully aware of that fact. Whether you’ve just started shopping or you’re overcoming objections from tech-resistant teammates, AI deployment can be daunting.

We spend a great deal of time discussing potential challenges with our enterprise clients here at Capacity. As such, this article seemed like a great way to proactively address some of the most common concerns.

If curious about enterprise AI but not completely convinced, hopefully these ideas will encourage further discussions. Check out some of the most common causes for concern below, and shoot us a message if we can address others for you.

1. Selecting an AI vendor.

As more enterprise AI vendors come out of the woodwork promising the best features, concluding what’s best for your org can feel like a real headache. After all, features are only worth something if they produce a meaningful ROI for your team.

No matter which AI vendor you choose, we recommend a bottom-up approach. Set aside time to speak with teammates and department leaders about pain points. You might be surprised by what you learn.

Is your customer experience team overwhelmed? Perhaps it’s high time you invested in a guided conversations tool. Are projects getting delayed because different departments aren’t communicating effectively? That might mean an applications integration solution is forthcoming.

In the short term, you gain a better understanding of which AI systems will be most beneficial. Long term, your company avoids unnecessary subscription fees on services that don’t perfectly satisfy your needs. The sooner you gather feedback on which tools are most needed, the faster you can move forward.

Actionable next steps:

  • Discuss workflow bottlenecks and inefficiencies with department managers.
  • Compile the most common concerns into an Excel or Google spreadsheet. 
  • Compare the tools offered by various AI vendors against the needs of your enterprise. 

2. Resistance to change.

Change can be scary, especially in a busy work environment packed with deadlines and KPIs. Adjusting to new software tools often feels like more of a curse than a blessing, and teams are often justifiably skeptical. Overcoming this resistance to new tech doesn’t happen overnight, but with the right plan, it can be done successfully.

Whether you’re rolling out an AI-driven chatbot or completely overhauling your knowledge base, explaining the end result beforehand is the best strategy. Despite limited functionality, teammates often feel a sense of attachment to the software being replaced, especially when transitioning away from legacy systems.

Starting with the feedback-based meetings mentioned above, keeping everyone as informed as possible lessens any growing pains of AI adoption. The more your teammates feel included in the process, the better.

After all, enterprise AI-powered tools aren’t worth much if employees don’t ultimately adopt them.

Actionable next steps:

  • Request a demo from your chosen AI vendor.
  • Invite as many users as possible to take advantage of the pilot program.
  • Follow-up by asking for feedback on the chosen solutions.

3. Cross-application functionality.

For enterprises in every industry, cross-app functionality is a massive source of tension when adopting an AI solution. According to a recent report, companies with 1,000+ employees average a staggering 19,848 person-to-app connections. If the AI system of choice cannot be integrated with each application, it won’t be maximally effective.

After all, enterprise AI tools are about working smarter.

Though many AI systems tout app integration, not all solutions are equally accommodating. We recommend shopping vendors that can provide an established list of integrations for popular applications. The SaaS companies that can do that should also be able to provide insight on developer options for future integrations.

Between preexisting app integrations and a user-friendly developer platform, your team can rest assured that future software programs won’t be left out in the dark. After all, there’s no excuse for siloed information in the age of AI.

Actionable next steps:

  • Investigate the apps your AI vendor has already integrated.
  • Find out if they offer a developer platform.
  • Identify which user interfaces are preferred by your team, and verify your AI solution’s compatibility.

4. Unstructured knowledge.

Even a highly successful enterprise can struggle with an organizational structure around its knowledge base. Depending on your org, that may not come as a surprise. Thanks to the proliferation of big data, assigning responsibility and granting access to crucial information can devolve into a spider web of confusion and missed messages.

In terms of AI adoption, the challenge arises when it comes time to establish this structure—often for the first time. To avoid bottlenecks, look for an AI solution that makes knowledge management intuitive.

Natural language processing (NLP), user permission structures, and robotic process automation (RPA) are the key attributes of an AI solution that will facilitate an effective knowledge framework. If you’re leading the charge for new SaaS tools, be sure to shop systems that can deliver those.

Actionable next steps:

  • Verify that your AI vendor has implemented user permission structure functionality.
  • Collaborate with IT personnel to uncover data management challenges.
  • Identify the key stakeholders for each division of data and tacit knowledge.

Enterprise AI expectations.

Ultimately, the challenges of AI adoption will vary from business to business and industry to industry. If only one or two of the challenges listed here hit home, rest assured, you’re not alone. As Capacity is adopted among multinationals, new challenges emerge all the time.

Which, to be blatantly salesy, is empowering our team with the experience required to implement enterprise AI for any org.