You have your foolproof AI roadmap in place. You have high-impact use cases. You have the data,  the team, the platform, and the partner in place to turn your plans into tangible results. You’ve checked all of the boxes to get your project up and running — right? Almost. 

A 2019 McKinsey report on AI impact and scale noted that one of the biggest challenges business leaders encounter when implementing AI solutions and software is getting timely buy-in from their IT teams. Predominantly, IT leaders and teams are concerned with ensuring software and tool efficiencies, maintaining security, and making smart investments in scalable technologies, to name a few.

Conversely, business leaders paving the path for AI-powered innovation want to work fast and nimble, constantly iterating to discern what AI projects are generating the most wins. This process might be viewed as equal parts strategic growth and experimentation, which, in many instances, may not cozily fit within frameworks and processes developed by IT teams.

The good news? Core concerns of business leaders and IT teams don’t need to be at odds. We compiled a list of five frequently asked questions that you’re bound to confront on your AI ascent. If you’re ready to address these with confidence – you’re on the right path.

1. How agnostic is the tech stack?

Why this is important: If you’re investing in an AI platform, you want to make sure it’s the best possible choice for your specific goals. With that said, it should also fuse seamlessly with other tools that teams view as fundamental to their daily work. For example, if your sales and marketing team is highly reliant on a CRM such as Salesforce and one of your key AI initiatives is content personalization in campaigns, ensuring there will be a harmony between your provider’s tech stack and Salesforce is key.

What points you should be able to address: Is it easily integrated at both the cloud and infrastructure levels? Is it cost-efficient and free of interruptions and downtime? Can it connect with my key pre-existing systems? What APIs are used to connect to data sources?

2. Is the AI software customizable?

Why this is important: Off-the-shelf products definitely have their advantages: they are typically quick to onboard, less expensive, and if scaling AI throughout the entire business stack isn’t a priority, they can get the job done. However, these solutions are typically less sophisticated and may not guarantee the same level of algorithmic accuracy of more robust platforms. Finding comprehensive, state-of-the-art software that can grow with your team, and your business, is the right route to take if innovation matters to you. Customizability, when done quickly in a platform environment, can yield significant competitive gains.

What points you should be able to address: Are competitors using similar technologies? How will this make us more competitive and help us reach our goals? Furthermore, how will we demonstrate clear ROI in the software investment?

3. What are the data security and privacy guarantees?

Why this is important: With data breaches becoming a mainstream topic of conversation, ensuring a secure software environment is a core topic of concern for IT teams. In the realm of AI, this is also one of the biggest hesitation points for leaders when planning or executing their AI strategy. But there are guardrails, systems, certifications and frameworks in place that should protect your business, your data, and your customer.

What points you should be able to address: What security certifications does the company have? Can you describe their approach to data privacy? What type of privacy and regulation framework do they have in place?

4. Who are the people developing the platform or customized solutions? What experience do they have?

Why this is important: Delivering and integrating an AI solution and software in its full lifecycle is a complex process. When you’re working with experts, they should be able to evaluate risk factors and create detailed mitigation plans. You’ll also want to do your homework to ensure that seasoned application architects, data engineers and SMEs with experience in your industry are on your partner or provider’s roster.

What points you should be able to address: What wins or case studies does the company have? What’s the track record of success for technical experts on their team? When and how can we connect in-person with this team?

 5. Who will be on the other end of the line when we need something?

Why this is important: You can create an ironclad plan, and you can invest in the best software and best talent the market has to offer. But questions arise, things happen, and knowing you have a trusted person on the other end of the line when needed is a crucial trust-building element that IT leaders look for.

What points you should be able to address: What’s the protocol when you need to speak to a person? What are the service hours? Does the company have extended service level agreements if needed? Who is our main point of contact for each key topic?

Key takeaways:

  • While IT teams may have separate goals, concerns and broader initiatives than business leaders driving AI innovation, they play a key role in ensuring AI platforms and/or projects launch;
  • Arriving to key questions prepared to answer and speak to FAQs can help speed up the process, and illustrate the level of your conscientiousness for key topics in IT;
  • Your AI platform provider should be able to speak to all of these questions, and provide supporting documentation for you to share with your IT team.

This list is an adaptation from our upcoming ebook, The AI Ascent: Engineering your pathway to innovation, out later this month. You can request your copy in advance, right here.