Elevating with AI Part 1 - AI for Business Efficiencies

Artificial intelligence (AI) continues transforming marketing, sales, and operational efficiencies in today’s rapidly evolving business landscape. To keep up, companies must wrap their heads around its adoption, challenges, strategic applications, and where AI-driven solutions make sense.

The big question is, where to start?

Try right here. OuterBox has been following AI closely and was an early adopter of its capabilities within business operations, including establishing an AI department, which created its own chatbot. While a brag, it is a humble brag. We love helping businesses improve their bottom line, and that includes adding AI to your day-to-day.

Let’s start with some AI 101 and then dive into examples of how you can increase business efficiency. But wait, there’s more! We have a Part 2 exploring AI applications in your marketing, sales, and data.

The State of AI Adoption

Whether it is automation or as a writing aid, businesses (and likely your competition) are working with AI in some capacity. One study showed that 65% of companies integrated generative AI into at least one function by mid-2024 (McKinsey & Company).

The early adopters are no surprise. Tech firms and financial services have been at the forefront. Meanwhile, regulated industries have understandably been slower. Industries like healthcare, government, and smaller enterprises encounter resource constraints and compliance challenges. Meanwhile, 67% of small businesses have embraced AI but are skittish due to concerns about content quality and legal risks.

The money is heading toward AI. 80% of decision-makers have experimented with capabilities (Hatchworks, 2023). 72% of organizations plan to increase AI budgets due to positive ROI from early pilot programs (Synthesia, 2024). However, we know more money doesn’t always mean more success.

AI barriers such as integration with legacy systems, data security concerns, and personnel skills can slow adoption and impact results. Another report said 65% of business leaders said they “cannot justify implementing” generative AI yet.

Businesses are also concerned about content accuracy and maintaining brand trust. AI is also getting some flack as a job stealer. AI could partially automate two-thirds of jobs, but many of these jobs will be complemented by AI, not substituted by it (Goldman Sachs, 2023).

A good start to AI evaluates these challenges upfront. It also includes this key mindset—AI is just a tool.

Getting Started with AI

Director of Business Development Josh Blankenship likes to use this analogy: You use the same tools from the hardware store to build an armoire but the outcomes between builders can differ significantly—from ornate works of art to a wobbly mess. Think of AI the same way. The tool isn’t what creates success; it’s the user.
If your business looks to implement AI, you’ll need a structured approach to ensure consistent outcomes (i.e., no wobbly mess). Here are a few things to consider when implementing AI in your organization:

1. Establish an AI Policy

58% of executives lack a formal AI policy (Weaver, 2023). So first, define clear AI policies addressing data safety, ethical considerations, human oversight, and transparency. Get it in writing, distribute it across all teams, and update it as your needs and use evolve. You’ll really want to define data safety and what you use these platforms for. 

Questions to consider when creating a policy:

• • What data can be shared with AI providers? 
  • What integrations could bring data privacy concerns and how can we navigate this?
  • What criteria and processes should be in place for evaluating new AI services?
  • How will AI-generated content be reviewed for accuracy?
  • What are the risk management strategies?

2. Pilot AI Initiatives

Scaling is key to doing things right. Start with focused AI pilots in areas like IT support, marketing, or sales, and allow time for testing capabilities before scaling up. You likely have AI champions already in your organization to leverage for testing and training. Find those folks and utilize them.

3. Train Employees

AI literacy is worth the investment. Set up AI training programs and encourage teams to explore AI use cases during internal exercises.

Breaking Down AI Models

There are a lot of models available, so we created a helpful chart that breaks down each one by noting strengths, unique features, and target audiences.

AI model breakdown

AI for Business Efficiency & Data Analysis

AI streamlines operations and decision-making by automating data analysis and reporting, enhancing CRM automation, and improving customer service responses through AI chatbots. In tracking these uses, productivity gains are up to 30-40%(Capgemini, 2024). Additionally, AI provides faster decision loops with daily insights (OpenAI, 2023).

You need the right prompts to achieve AI-driven analytics, data-backed decisions, and greater agility in responding to market trends. This goes back to the idea of AI as a tool and the user as the key.

A good prompt:

1. Is super specific
2. Uses action statements
3. Asks to analyze patterns
4. Asks the platform for correlations
5. Asks questions with a role in mind such as “As a marketing or sales manager, what actions would you take based on the data?”

A prompt to analyze data for sales purposes.

You can create lead analysis, KPI analysis, coaching and strategy outlines, data enrichment suggestions, and Google Sheets or Excel aids. We bet you can think of more. Remember that these tools are built for follow-up questions, so be sure to ask those questions and ensure you get the information or output you need.

Key Takeaways for Businesses

Establishing guidelines and guardrails via an AI policy ensures ethical and practical AI use across your organization. Once that’s in place, your business can use AI to enhance decision-making processes and operations where it makes sense.

The main takeaway is to treat AI as a tool to assist your team, keeping in mind the power of your prompts and maintaining human oversight.

In the second part of this series, we look at implementing AI-driven solutions in marketing, sales, and data analysis to explore their potential.