Sophisticated AI Strategy: The Key to Unlocking AI Success

The first of a series on business-first AI, this article covers general AI use from an industry agnostic perspective. Future installments will focus on the AI technologies with industry-specific applications.

Artificial Intelligence (AI) technology has leapt from the pages of science fiction novels and on to the front page of newspapers. Its emergence as a real-world tool has transformed markets, the workplace, and the human experience.

Still, it’s important to acknowledge that simply adopting and onboarding AI is not enough to benefit from the technology’s many potential advantages. The fact is, no AI will seamlessly integrate into a complex business environment and generate impeccable results out of the box. The businesses promising results to that degree aren’t selling products, they’re selling hype.

To realize the value of AI, businesses need to start with a smart strategy that applies the technology where it can have a positive business impact while controlling for known risks.

The Formula for Success

Setting the stage for AI success is a lot less complicated than the technology itself. Preparing for AI success requires that you create a strategy which suits your business objectives and emphasizes:

Curating Inputs

AI doesn’t reason or think in the human sense. It processes information through complex probability engines. When the AI interprets the user’s prompts and data inputs correctly, it can produce useful results. But, if the user’s prompt is unclear or misinterpreted, or if the data provided is inaccurate, the AI’s output won’t be dependable. Effectively curating inputs is a multiphase process requiring that you (i) optimize your data for AI applications and (ii) train your employees to prompt the AI tools effectively.

Governance

All new technologies carry risk. AI risks stem primarily from inaccurate or unclear inputs, AI hallucinations or the potential for security risks. Everyday users unknowingly upload confidential information to AI systems or receive AI generated content that infringes on protected IP. AI governance is broad and complex but is designed to streamline and manage resources, projects and AI tools to effectively align them with your business objectives. Proven AI Governance and risk management models help you successfully deploy AI resources in your environment, while minimizing risk and focusing on business outcomes.

Strict Direction 

Not every task is suitable for AI. Just ask the attorneys who have been censured by judges for submitting AI derived documents citing cases that never happened. AI is subject to hallucinations (made up facts) and organizations must be alert to the potential for incorrect findings or results. Your organization needs to align on how to define acceptable AI use, qualify outputs, and measure the value of results. To get the most value from AI, consider applying it in the following business-first use cases and adopting a slow and steady approach to expanding AI capabilities.

Staff Training

While many staff are very excited to use AI, training them on proper usage of these tools is critical. Proper training will accelerate your staff’s ability to find useful examples, and improve s and outputs. Staff often get extremely frustrated with new technology and put it aside as a result, thereby foregoing the benefits. Establishing a solid training and engagement program will yield results, reduce frustration and improve the adoption of this critical technology.

Proven Use Cases

At its best, AI enhances the capabilities of the humans using it. At its worst, it’s expensive, generates unreliable outputs, and weakens the abilities of over-reliant users.

That’s why the most critical element of any AI strategy is identifying how to apply the technology. Now that AI is at work in organizations of all kinds, we have real-world examples of the tasks it’s well equipped to handle.

Every business is unique, but many find room for AI enhancements in the following areas:

Front Office: Marketing Content & Blog Development, Proposal Generation, Pricing / Sensitivity Analysis

Use Case in Focus: Pricing analysis powered by AI can generate multiple models predicting customer impact, profitability, and break-even points associated with pricing changes.

Operations: Staff Scheduling, Production Optimization, Customer Development

Use Case in Focus: Customer segmentation analyses can help businesses identify high value customers, as well as those that may be costly to serve, and recommend ways to develop deeper business relationships and more cost effective ways to serve clients.

Back Office: Job Description Development, Resume Screening

Use Case in Focus: Recruiting efforts can benefit from AI resume screening which can analyze incoming applications at tremendous speed, allowing recruiters to quickly identify top candidates and reach them faster.

Applications like these are traditionally associated with costly specialized service providers. AI promises to democratize business applications like these by bringing them in-house and radically revising the expense associated with them – of course, the success of such an endeavor depends entirely on how effectively businesses incorporate AI solutions.

Controlling Risks

AI technology is still in its infancy, so it’s natural that your business approach develops in tandem. I like to encourage my clients to take baby steps in their AI journey and get comfortable with a three-stage “crawl, walk, run” process.

Starting small helps businesses control for unintended risks, assess the efficacy of systems supported by new AI tools, tweak programs as necessary, and expand the scope of these programs as they get more comfortable.

For example, let’s look at one more proven business-first use case from the previous section. AI can be extremely useful creating work schedules for businesses with large teams of employees. For scheduling assistance, provide AI with data on your workforce and the tasks required, the qualifications necessary to complete those tasks, and each employee’s target hours. AI can then generate primary and backup schedules based on these parameters, offering managers a useful starting point.

That’s one example of a project ideal for the “crawl” phase of AI adoption to provide a meaningful efficiency advantage while remaining low-risk. It’s a great starting point because it saves manual effort while getting users comfortable interfacing with an AI tool.

Controlling risks with AI means starting small, getting comfortable and developing the confidence and skillset necessary to take on more ambitious projects. It also means validating outputs and the results of your AI use.

Conclusion

AI represents a significant leap in tech but the adage holds true: technology is only as useful as the person (or organization) behind it. Successfully incorporating AI requires a clear path toward adoption, organizational alignment, well-structured data models and inquiries, the right use cases, and a slow and steady approach to implementation.

Stay tuned for future articles taking a closer look at the specialized AI tools reshaping major industries. If you have any questions about your AI strategy, contact CBIZ today.