Strata52: AI Strategy Consultants

The Four Core Components for Successful AI Adoption in Medium-Sized Businesses

Core components of successful AI adoption

If you run a medium-sized business and you’re looking for ways to integrate AI into your operations, then you’re typically looking to receive several benefits from this AI transformation. These benefits can usually be summarized by having an AI-copiloted staff, low-risk AI adoption, high visibility into the ROI of your AI initiatives, the ability to let go of initiatives that aren’t working, and empowered employees who can find, evaluate, and onboard AI tools as they come to market.

Over time, this AI transformation will look like a collection of tools. They are deployed inside every department of your business, such as sales, marketing, operations, supply chain, and finance. For many small to medium-sized businesses, this final state represents a future vision of what their AI stack will look like. However, reaching this point can be overwhelming.

To become a successful early adopter, businesses should focus on these four core components:

  1. AI Strategist SME
  2. Ethical AI Policies
  3. AI Road Mapping and Implementation
  4. AI Performance Dashboard

Let’s dig into each of these core components.

1. AI Strategist SME

An AI Strategist SME is an industry expert. With a deep understanding of the current and future state of artificial intelligence. Many businesses are starting to onboard AI strategists or hire them as their Chief AI Officer. These individuals are brought in to own the AI stack and the company’s AI transformation, with three core responsibilities:

  1. Architect the company’s AI strategy
  2. Oversee the company’s AI roadmap implementation
  3. Monitor and report on the AI stack

At Strata52, an AI transformation agency, we specialize in recruiting industry experts and putting them through a rigorous certification course to turn them into AI strategists. This involves several hours of coursework, a final exam, and a written and verbal thesis process. We prioritize finding individuals who are already industry experts in a particular field, such as an oil and gas expert who is also an AI enthusiast. AI engineers, data scientists, and machine learning engineers possess plenty of technical expertise. But, they might not fully understand your industry’s nuances, acronyms, and competitive landscape, which typically require experience and time spent within the industr

2. Ethical AI Policies

Ethical AI policies are the AI guidelines and guardrails defined by leadership within your business. These policies outline what tools are allowed and not allowed to be used. Examples of these policies might include a data and privacy policy, a cybersecurity policy, a support policy, a cost and billing policy, and an accuracy and bias policy.

Developing these ethical AI policies is unique to every business engagement. However, the individuals who write the policies remain the same: the AI strategist and leadership, such as the president, CEO, COO, CTO, CIO, or any C-level executives or individuals in a leadership position who shape the company’s vision.

Putting together AI policies is not a process to take lightly. Once you establish the policies, you need to roll them out to the entire organization and assign department heads the responsibility of enforcing them. This way, they have the freedom to seek out, evaluate, and adopt new AI tools without running into bottlenecks when seeking approval for specific tools that could change their entire department.

3. AI Road Mapping and Implementation

AI road mapping is the transparent list of off-the-shelf and custom AI tools. Broken down by rollout date and the department where they’ll be implemented. To create an AI roadmap, you need to identify the opportunities for AI implementation within the business, and determine whether to solve those opportunities with off-the-shelf tools or custom AI. Prioritize the low-hanging fruit to get quick wins in AI adoption throughout the organization.

Creating an AI roadmap can be a lengthy process, typically taking about four weeks. Several individuals from the organization partner with every department head and the leadership of the businesses they work with. The responsibility of putting together this AI roadmap lies on the shoulders of the AI strategist, who leads the roadmap creation, implementation, and management of the entire AI stack.

4. AI Performance Dashboard

An AI performance dashboard is a dynamic dashboard that gives you visibility into the performance of your AI stack. As businesses onboard dozens of off-the-shelf tools and create dozens of custom AI tools. They’ll likely have expenditures in the form of subscription fees and development costs at the end of the year. Ensuring these expenses provide the expected return requires measuring and monitoring the performance of each AI tool deployed across the entire business.

The AI performance dashboard categorizes two different types of metrics:

  1. Business KPIs: These are relatively understandable metrics like change in headcount, change in revenue, return on investment, etc. These metrics can be gathered through API access or updated monthly.
  2. Employee Sentiment Metrics: These metrics shed light on adoption rate, time savings, headcount savings, and more. Insight into employee sentiment metrics is gained through monthly surveys distributed to all employees, asking questions like:
    • Are you using this tool? (Yes/No)
    • How much time does this tool save you?
    • If this tool disappeared tomorrow, would your job be very difficult to do?

By answering these questions, employees provide qualitative feedback that allows businesses to assess the performance of the AI stack on a department-by-department basis and zoom out to look at the organization’s AI stack as a whole.

If you don’t already have a dashboarding solution like Power BI or Tableau, or a data analyst aggregating this data to display it in a nice dashboard for rapid visibility. You can use a free tool called MyAIStack. MyAIStack is a cloud-based dashboard where you can create an account. Load your entire AI stack, and get instant visibility into the value your AI tools are bringing to your business.

In summary, the four core components to becoming a successful early adopter of AI in your business are:

  1. AI Strategist Subject Matter Expert: The person who will manage your AI stack from idea through implementation and AI stack management.
  2. Ethical AI Policies: These empower your staff to seek out and onboard their own set of AI tools to streamline their departments.
  3. AI Road Mapping and Implementation: Your strategic plan and rollout of that plan.
  4. AI Performance Dashboard: Allows you to make data-based decisions inside your business with complete visibility over the AI stack.

Before adopting another AI tool inside your business, make sure you have these four building blocks in place.

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