Tag: Artificial Intelligence

Strategy First. AI Second.

Eighty-eight percent of AI pilots fail to reach production, according to IDC research. Most fail because organizations chase the tool instead of defining the outcome. They ask, “How do we use AI?” rather than “What problem are we solving?”

A little perspective

I’m old enough to remember when VisiCalc and SuperCalc came out. That was before Lotus 1-2-3, and way before Microsoft Excel. VisiCalc and SuperCalc were just ahead of my time, but I was a big user of Lotus 1-2-3 version 1. Back then, everyone focused on how to harness the power of spreadsheets to change the way they did business.

Teams built massive (for that time) databases inside spreadsheets to manage product lines, inventory, billing, and even entire accounting systems. If you didn’t know how to use a spreadsheet, you were last year’s news.

The same shift happened with word processing. Microsoft Word replaced WordPerfect and its maze of Ctrl and Alt key combinations. Then the World Wide Web arrived in the early 1990s and opened a new set of doors.

I could go on with databases, client-server, cloud computing, etc. Each technology wave creates new winners but also leaves some behind.

The lesson is simple each time. New tools expand possibilities. Strategy gives those tools a purpose.

The point today

AI is a modern toolkit that can read, reason (think?), write, summarize, classify, predict, and create. It shines when you give it a clear job. Your strategy defines that job. If your aim is faster cycle times, higher service quality, or new revenue, AI can be the lever that helps you reach those outcomes faster.

Three traps to avoid

Tool chasing. This looks like collecting models and platforms without a target outcome. Teams spin up ChatGPT accounts, experiment with image generators, and build proof-of-concepts that fail to connect to real business value. The result is pilot fatigue. Endless demonstrations with no measurable impact.

Shadow projects. Well-meaning teams launch skunkworks AI experiments without governance or oversight. They use unapproved tools, expose sensitive data, or build solutions that struggle to integrate with existing systems. What starts as innovation becomes a compliance nightmare that stalls broader adoption.

Fear-driven paralysis. Some organizations wait for perfect clarity about AI’s impact, regulations, or competitive implications before acting. This creates missed opportunities and learning delays while competitors gain experience and market advantage.

An AI enablement playbook

Name your outcomes. Pick three measurable goals tied to customers, cost, or growth. Examples: reduce loan processing time by 30 percent, cut customer service response time from 4 hours to 30 minutes, or increase content production by 50 percent without adding headcount.

Map the work. List the steps where people read, write, search, decide, or hand off. These are all in AI’s wheelhouse to help. Look for tasks involving document review, email responses, data analysis, report generation, or quality checks.

Run small experiments. Two to four weeks. One team. One KPI. Ship something tangible and useful. Test AI-powered invoice processing with the accounting team, or AI-assisted internal help desk with support staff.

Measure and compare. Track speed, quality, cost, and satisfaction before and after. Keep what moves the needle. If AI cuts proposal writing time by 60 percent but reduces win rates by 20 percent, you need to adjust the approach.

Harden and scale. Add access controls, audit trails, curated prompt libraries, and playbooks. Move from a cool demo to a dependable tool that works consistently across teams and use cases.

Address the human element. Most resistance comes from fear of displacement, rather than technology aversion. Show people how AI handles routine tasks so they can focus on relationship building, creative problem-solving, and strategic work. Provide concrete examples of career advancement opportunities that AI creates.

Upskill your team. Short trainings with real tasks. Provide templates and examples in their daily tools. Make AI fluency a job requirement for new hires and a development goal for existing staff.

Close the loop with customers. Ask what improved. Watch behavior and survey scores, with extra weight on what people actually do, versus what they say.

Governance that speeds you up. Good guardrails create confidence and help you scale.

Access and roles. Limit sensitive data exposure and log usage by role. Marketing might get broad access to content generation tools while finance operates under stricter controls. The concept of least privilege applies. 

Data handling. Define red, yellow, and green data. Keep red data (customer SSNs, proprietary algorithms, confidential contracts) away from general public-facing tools. Yellow data needs approval and monitoring. Green data can flow freely.

Prompt and output standards. Save proven prompts in shared libraries. Require human review for customer-facing outputs, financial projections, or legal documents. Create templates that teams can adapt rather than starting from scratch.

Audit and monitoring. Capture prompts, outputs, and sources for key use cases. Build processes to detect bias, errors, or inappropriate content before it reaches customers.

Vendor review. Check security, uptime, and exit paths before heavy adoption. Understand data residency, model training practices, and integration capabilities. Consider making Bring-Your-Own-Key (BYOK) encryption the minimum standard for allowing your organization’s data to pass through or be stored on any AI vendor’s environment.

Questions for leaders

Which customer moments would benefit most from faster response or clearer guidance? Think about your highest-value interactions and biggest pain points.

Which workflows have the most repetitive reading or writing? These offer the quickest wins and clearest ROI calculations.

Which decisions would improve with better summaries or predictions? AI excels at processing large amounts of information and identifying patterns humans might miss.

Do we have the data infrastructure to support AI initiatives? Clean, accessible data is essential for most AI applications to work effectively. Solid data governance and curation are critical.

What risks must we manage as usage grows, and who owns that plan? Assign clear accountability for AI governance before problems emerge.

What will we stop doing once AI handles the routine? Define how you’ll reallocate human effort toward higher-value activities.

Who will champion AI adoption when the inevitable setbacks occur? Identify executives who understand both the potential and the challenges.

What to measure

Cycle time. Minutes or days saved per transaction.

Throughput. Work items per person per day.

Quality. Rework rate, error rate, compliance findings.

Experience. Customer effort score, employee satisfaction, NPS.

Unit cost. Cost per ticket, per claim, per application.

AI is the enabler

Strategy sets direction. AI supplies leverage. Give your people clear goals, safe guardrails, and permission to experiment and fail along the way.

Then let the tools do what tools do best. They multiply effort. They shorten the distance between intent and execution. They help you serve today’s customers better and reach customers you couldn’t reach in the past.

The question isn’t whether AI will transform your industry.

The question is whether you’ll lead that transformation or react to it.

Which will you choose?

Photo by Jen Theodore on Unsplash – I love this old school compass, showing the way as it always has. The same way a solid strategy and set of goals should lead our thinking about leveraging the latest AI tools.

Providing Room to Fail

Organizational culture, not technology, is the hardest part of innovation

How many of your projects are truly innovative? If you have any, what’s your success rate? Would you consider your success rate to be all-star caliber?

This baseball analogy is almost a cliché, but it holds up. A professional hitter with a .300 average is considered excellent (all-star?). That means they fail seven times out of ten.

Now imagine applying this to innovation. What if only 30% of your projects succeed? At first glance, that sounds like a losing record. But if the successful projects provide 10x productivity increases, transform your customer’s experience, or massively boost profitability…30% success would yield incredible results for your organization.

This is the kind of opportunity in front of us today with AI. Tools are maturing quickly. The potential is staggering. Every company, large or small, is beginning to experiment.

Some will tiptoe. Others will dive headfirst. All will face a mix of breakthroughs and busts.

There will be tools that don’t deliver on promises, pilots that fizzle, and teams that struggle with adoption. But there will also be amazing homeruns. Projects that reshape the business and redefine what’s possible.

Many leaders today are focusing on which AI tools to purchase and how to train their teams. That’s the easy part.

The harder part is creating space for both the hits and the strikeouts. If people feel they must succeed every time, they probably won’t swing at all. They’ll play it safe and stick with what they know.

Innovation will grind to a halt.

Providing room to fail doesn’t mean celebrating mistakes. It means making sure your team knows that experiments, even the ones that fall short, are part of making progress. Leaders who demand perfection get compliance. Leaders who make room for failure get innovation.

As you lead your organization into AI and beyond, remember that your job isn’t to guarantee every swing is a hit.

Your job is building a culture where people are willing to keep taking swings.

Photo by Chris Chow on Unsplash

The Known vs. The Obvious: Embracing AI in the Workplace

For years, we’ve heard that Artificial Intelligence (AI) will revolutionize industries. The idea is so prevalent that it’s easy to stop actively thinking about it. We acknowledge AI in headlines, in passing business conversations, and in abstract discussions about the future. Yet, much like a fish is unaware of the water surrounding it, we’ve been immersed in AI without fully recognizing its impact.

That impact is now undeniable. The question is: will we embrace it—or ignore it at our peril?

AI as the Invisible Force

AI is no longer a futuristic concept, or a background presence. It’s embedded in the tools we use every day, from the smartphones in our pockets to the chatbots handling our customer inquiries. It powers business decisions, optimizes operations, and influences nearly every industry.

Yet, because AI is so familiar, we often overlook it. The term itself has become a cliché—almost old news.  Something we assume we understand. But do we? How much do we really know about its capabilities, its limitations, or its potential disruptions?

Many still view AI as a distant idea, relevant only in the future or in industries far removed from their own. This perspective is outdated.

The Shift from “Known” to “Obvious”

AI is a driving force that can shape how we work, compete, and innovate. Organizations that continue treating AI as an abstract concept risk being blindsided by its rapid evolution.

This shift—from AI being “known” to becoming “obvious”—is critical. The moment we stop seeing AI as some far-off development and recognize it as an immediate force, we can take meaningful action.

Make no mistake: AI will transform your organization, whether you engage with it or not. The only choice is whether you’re leading that change or struggling to catch up.

The Cost of Waiting

A passive approach to AI is no longer viable. Waiting for the “right time” to adopt AI means falling behind competitors who are already leveraging its power. Yes, AI is complex, and yes, there are risks. But the greater risk lies in hesitation.

I’m old enough to remember the early days of the internet (I’m that old).  Most businesses dismissed it as a fad. Others chased the new idea with reckless abandonment and wasted tons of time and money.  But a relative few (at the time) experimented, learned, made incremental changes, and ultimately thrived in their use of the new “internet-powered” approach. Not to mention all the new multi-billion (trillion) dollar businesses that were made possible by the internet. 

AI is following a similar trajectory. Many are ignoring, even shunning, AI as something other people will figure out.  They don’t want to be the one pushing these new ideas within their organization.  It’s easier to stay in the background and wait for someone else to take the leap.

But others are already leaning in (to coin a phrase), experimenting, and learning.  They are incrementally (and sometimes dramatically) shaping a new future…and remaining relevant in the process. 

Learn the Basics

AI adoption doesn’t require immediate mastery. It starts with small, intentional steps.

You don’t need to be an AI expert, but understanding its core functions and business applications is essential.

Start by exploring industry-specific AI tools already in use.  How did I make this list?  You guessed it, I asked ChatGPT to give me a list of industry-specific AI tools in use today.  Will each one be a winner?  Not sure, but it’s a great list to use as a starting point:

–Retailers use Amazon Personalize and Google Recommendations AI for AI-driven product suggestions, improving customer engagement and sales.

–Marketers leverage HubSpot AI for automated email campaigns, Persado for AI-powered ad copywriting, and Seventh Sense for optimizing email send times.

–Financial analysts turn to Bloomberg Terminal AI for market insights, Kavout for AI-driven stock analysis, and Zest AI for smarter credit risk assessments.

–Healthcare professionals rely on IBM Watson Health for AI-assisted diagnostics and Olive AI for automating administrative hospital tasks.

–Manufacturers use Siemens MindSphere for AI-powered predictive maintenance and Falkonry for real-time industrial data monitoring.

–Customer service teams integrate Forethought AI for automated ticket triaging and Zendesk AI for intelligent chatbot interactions.

–HR and recruitment teams utilize HireVue AI for AI-driven candidate screening and Pymetrics for bias-free talent assessment.

Experiment with Broad-based AI Tools

Don’t wait for the perfect strategy.  Start small. Generalized AI tools can improve various aspects of your business (again, I asked ChatGPT for this list):

–Conversational AI & Research: Tools like ChatGPT, Claude.ai, or Anthropic’s AI help generate content, answer complex questions, summarize reports, and assist in brainstorming sessions.

–Automation: Platforms such as Zapier AI, UiPath, and Notion AI automate workflows, streamline repetitive tasks, and generate notes and summaries.

–Data Analysis: Solutions like Tableau AI, ChatGPT’s Code Interpreter (Advanced Data Analysis), and IBM Watson process and visualize data for better decision-making.

–Customer Engagement: AI-driven tools such as Drift AI, Intercom AI, and Crystal Knows enhance customer service, lead generation, and sales profiling.

These are just a few of the many AI-powered tools available today. The landscape is constantly evolving.  Exploring AI solutions that fit your specific needs is the key to personal and professional growth.

Cultivate a Growth Mindset

Learning AI is a journey, not a destination. It’s okay to make mistakes.  It’s actually necessary. Feeling uncomfortable is a sign of growth. The more you experiment, fail, and adjust, the more effectively you’ll integrate AI into your work. AI isn’t about instant perfection.  It’s about continuous learning.

Lead from the Front

If you’re in a leadership role, set the tone. Your team will look to you for guidance. Show them that AI adoption isn’t just an IT initiative.  It’s a mindset shift.

Encourage experimentation, provide resources, and support a culture of AI-driven innovation. Companies that will thrive with AI aren’t the ones waiting for a complete plan.  They’re the ones embracing AI through hands-on learning and iterative improvement while incorporating these new discoveries into their future plans.

The Future is Now

AI is not a distant disruptor—it’s an active force shaping today’s workplace. Organizations that recognize this and take action will thrive. Those that don’t will be left behind.

It’s time to stop treating AI as a theoretical innovation and start engaging with it as a business reality.

The future isn’t waiting, and neither should you.

Photo credit: The graphic was generated by DALL-E.  I asked it to generate an image of an office on the ground floor that captures the essence of the blog post I had just written. 

In its first few attempts, it tossed in robots sitting amongst the office workers.  I like to think of myself as a forward thinker, but I’m not quite ready to accept that reality…even though I’m sure it’s rapidly approaching.  I asked DALL-E to eliminate the robots (for now).