The AI Strategy Playbook for Mid-Market CIO’s

Where AI Delivers Value (and Where it Doesn’t)

Currently, artificial intelligence is the most talked-about technology initiative in the business and personal sector. Every day, businesses and executives are hearing and learning more about productivity improvements, operations automation, elevated customer experiences, uncovering new revenue opportunities, and much more…all because of AI.

For most mid-market organizations, the challenge isn’t understanding what AI is.  It’s understanding where AI can genuinely create business value and where it’s just noise.  The organizations currently enjoying success aren’t doing so because it’s the latest technology trend. They are implementing AI with a clear business strategy that is purposeful and disciplined.

AI Is No Longer an Innovation Concept

In the last 12 months, AI has moved from being considered an “experiment” to now being viewed as a true business capability.

Search the following question: What are the top four questions business executives are asking about using AI as a business strategy?  The results clearly show the answers are no longer about technical questions.  Answers abound around efficiency, governance, security, opportunity, and much more.

The role of the CIO has shifted significantly from evaluating AI tools to being a necessary participant in developing the strategy that determines AI’s role in the business’ long-term strategy.

The Biggest AI Mistake: Having the Mindset that AI is a Technology Tool

Many organizations begin the AI adoption process by purchasing the shiniest AI software or the latest generative platform as a “plug and play” tool.  Successful AI adoption doesn’t begin with technology; it should begin with addressing business challenges.  And, those challenges vary from industry to industry, company to company.

Organizations need to take a hard look inward and answer questions such as:

• What processes consume the most time for our employees/customers?
• What repetitive tasks do our employees struggle with?
• What company decisions could be improved with better, more accurate data?
• Which of our customer experiences would be most enhanced?
• Are there revenue opportunities we could use by implementing an AI strategy?

Only after identifying these opportunities should technology become part of the discussion.  AI should not become the strategy; it should support the strategy.

Where AI Delivers the Greatest Business Value

For most mid-market organizations, the biggest opportunities aren’t flashy. They are practical, measurable, and improve the way our employees work and the way our customers experience our service through:

• Operational Efficiency by improving repetitive administrative tasks, summarizing meetings, assisting with documentation, improving ticket routing, and reducing manual workloads.  All of which offer more time for employees to solve problems and serve customers.
• Better Decision Making by analyzing large volumes of operational, financial and customer data considerably faster than traditional methods. Equally, AI can assist with gaining insights into emerging trends, discovering operational pinch points, and better forecasting.
• Better Customer Experience through AI-assisted chat, faster response times, personalized communications, and order management.  AI should be viewed as enhancing customer experience without replacing the human relationships customers value.
• Improving Cyber Resilience to detect abnormal behavior, identify threats earlier, reduce alert fatigue, and accelerate incident response.  Just as with the customer experience, AI should not be viewed as a replacement for cybersecurity professionals.  It should allow them to respond faster and more effectively.

Where AI Doesn’t Deliver Value

Not every business problem requires an AI solution. In fact, many AI initiatives fail because of internal broken processes or trying to solve problems that technology can’t fix.

• Poor Data Quality is the #1 reason AI can be unreliable.  We’ve all heard the saying “Garbage In. Garbage Out.”  Before investing heavily in AI, organizations should first strengthen their data governance policies.
• Lack of Governance policies forces businesses to face risks of data privacy, regulatory compliance, intellectual property interests, and inconsistent decision-making.  Governance requires as much attention as cybersecurity or financial systems, and executive oversight is paramount to a successful governance program.
• Automating Broken Business Processes simply makes inefficiency happen more quickly. Organizations should optimize workflows before introducing AI.
• Chasing the Latest Trend does not create measurable value. Organizations that adopt AI because their competitors often find themselves trying to justify the investment.

AI has the potential to reshape every aspect of business, but only when it’s guided by a clear strategy, strong governance, and measurable results.  Rather than trying to implement AI throughout an organization, many take a phased approach. 

To take the next step in your AI readiness, Secure Data Technologies has developed “A 4-Phase Practical AI Roadmap for Mid-Market Organizations” that you can download.