AI’s greatest value is fueling a culture of performance. When a transformative technology’s primary value is efficiency, automation and knowledge delivery, the performance of every operating function should elevate accordingly. Yet the reality is that the massive ROI promised to corporations has not materialized at scale.
Between $3 trillion and $4 trillion will be spent on AI infrastructure by the end of the decade, according to Nvidia CEO Jensen Huang. When the technology hyperscalers commit that kind of capital, they want CEOs to embrace their technology vision, fast:
“Ignore AI and risk becoming irrelevant… Adopt it, and adopt it fast,” — Eric Schmidt, former Google CEO
“The pace of progress in artificial intelligence… is growing at a pace close to exponential.” — Elon Musk
“If you don’t have an AI strategy, you’re going to die in the world that’s coming.” — Devin Wenig, Business Insider
This mix of urgency, passion and adamancy resulted in massive corporate investment in AI—without a lot of focus and with very little understanding of what the actual impact would be. Big bets with no strategy have never proven to create meaningful shareholder value.
Economic historians point to a revealing lesson from the early days of electrification. When factories replaced steam engines with electric motors, the results lacked the impact most had hoped for. A key reason was that, in their haste to embrace innovation, they maintained the legacy floor plans optimized for mechanical power. It took decades for companies to realize meaningful gains. The lesson: Transformative technology is rarely optimal for legacy workflows and infrastructure. “All in on AI” makes for wonderful headlines and eases the concerns of fearful board members—but is it actually the best way to start enterprise transformation and business evolution?
This is where microdosing comes in.
Popularized by Dr. James Fadiman’s 2011 book, The Psychedelic Explorer’s Guide, microdosing LSD and hallucinogenic mushrooms took firm hold in the Silicon Valley “biohacking” community. Engineers, executives and creatives embraced microdosing not to “tune in, drop out,” but to enhance creativity, code better and expand their mental abilities. They sought to realize the benefits of activating new capabilities while minimizing the risks of full-scale use. Seen this way, microdosing may offer a more strategic path forward for AI.
Almost every global enterprise operates in complex tech ecosystems, or “stacks,” often dominated by one of the major software companies. Salesforce, Microsoft, Oracle, SAP and ServiceNow have vast footprints and often coexist with cloud infrastructure providers such as Amazon Web Services, Google Cloud, Microsoft Azure and Alibaba. Each is investing billions in AI platforms, agents, security tools and data management. They are urging CEOs to make big bets on their vision of the AI-dominated future. But there’s an all-too-often overlooked elephant in the room: Very few companies have executives and staff with the experience necessary to manage technology that is disruptive, transformative and, quite frankly, dangerous.
Large organizations do not need to serve as the proof of concept or test case for technology vendors; instead, “AI micro-solutioning”—the AI version of microdosing—presents a much more manageable and attractive option. Now, I imagine that many of you are thinking, “I invested in pilots and proof of concepts already,” but that is not the same thing as micro-solutioning. We are not creating AI orphans that end up abandoned after the test period and ROI analysis; rather, micro-solutioning solves real-world problems and creates impact that scales over time.
With that backdrop, here are five microdoses to start with that every company can implement in manageable time frames.
First Dose | Sales Team Intelligence
One of the opportunities presented by AI is the codification and systemization of a revenue supply chain that integrates product, marketing and sales. Sales teams are the natural starting point for that long-term transformation. Knowing what customers really want, getting feedback fast and reacting to it accelerates revenue acquisition. Who has the best access to that information? Sales teams. Sales intelligence gathering is one of the easiest AI solutions to implement. Between meeting tools from Google, Zoom, Fireflies and others, and sales listening tools from Gong, Chorus.ai and Clari, organizations can capture robust, real-time customer intelligence from every interaction. This requires very little disruption to the existing tech ecosystem and provides valuable insights for sales, marketing and product teams. Starting with the sales team also creates a strong cultural catalyst for the rest of the organization.
Second Dose | Rethinking—Even Reimagining—External Service Providers
Learning and experimenting with key partners is a great place to start. For publicly traded and PE-owned companies that spend heavily on external auditors and outside law firms, AI presents an immediate opportunity for cost savings. AI works best when it has reliable data to reference, and legal contracts have the structure, rules and workflows that enable robust functionality. Further, external auditors can create conversational voice and visual dashboards (think briefings versus reports) that allow executives to interact verbally, with visuals appearing on demand. Internal teams can analyze specific clauses in contracts and potential changes without direct human interaction. Key decision makers will still need to vet critical decisions, but these functions are ripe for rapid and meaningful elevation. Investments in these external services should deliver significant cost reductions over time.
Third Dose | Customer Experience Enhancements
Nothing annoys customers more than feeling underappreciated and unknown. Many organizations—including financial services firms, mobile carriers, insurers, broadband providers and healthcare organizations—have years of data that customers would like remembered and taken into account in their interactions. AI is the ultimate tool for elevating customer experience in environments like this. Every customer-facing team member and system can be equipped with context. Most of us have experienced the power of a Spotify personalized playlist, a Netflix movie recommendation or a location-based restaurant suggestion from American Express. When customers feel seen and heard, their impression of your brand is greatly enhanced. This is a capability that ecosystem partners such as Salesforce, ServiceNow and Oracle can activate at scale in a short period of time, with minimal risk.
Fourth Dose | Let’s Get Visual
One of the most exciting breakthroughs in AI has been its ability to analyze medical images alongside physicians. Research indicates this collaboration shows strong promise in improving diagnostic accuracy—enhancing detection while reducing unnecessary follow-ups and procedures. Now apply visual analysis to your business. Insurance companies can have auto accident claims initiated and vetted in real time from accident scenes via mobile applications. HR departments at companies encouraging return-to-work policies can immediately gauge attendance compliance and employee interaction, and repair organizations can get feedback on complex scenarios by sharing real-time visuals. Visual data consumption is so essential that Meta, Google, Samsung and Snap are all releasing solutions for the consumer space.
Fifth Dose | Can Everyone Be a Decent Writer? Just About.
It is essential to understand that the most powerful acronym in AI, LLM, stands for Large Language Model. Language is a core strength of AI, making it highly useful across the enterprise. First, no employee has any excuse to write a bad email ever again. All major email tools now include AI-based editors that can clean up the narrative mess from a 2:00 am coffee-infused stream of consciousness. Second, any creative brief, conceptual explanation, strategy thesis or business case can be augmented, researched and elevated by leading LLMs, including Google Gemini, ChatGPT, Claude and Microsoft Copilot. Lastly, internal teams producing social media posts, press releases, website copy or advertising language can leverage a range of low-cost, easy-to-use AI tools. It may not turn everyone into a great storyteller, but language-based communication should improve across the board.
The core philosophy of microdosing is using small, consistent doses to achieve long-term benefits without disrupting normal function. This is the core of AI micro-solutioning as well. Small, iterative improvements to core business functions create impact without disrupting the entire ecosystem of existing systems, workflows and data models. Organizations can continually introduce innovation and automation, creating long-term value. As AI continues to get smarter, more interactive and more adaptive, these solutions should evolve naturally as larger technology ecosystems implement agentic AI at scale.
Meanwhile, these systems require basic diligence and governance to limit risk. Consider the following:
- Customer data and privacy laws still apply and must be managed judiciously.
- Executives and managers need to create cultures of innovation that encourage teams to embrace technology and AI-human collaboration.
- When working with partners and service providers, clearly defined Service Level Agreements (SLAs) will help distribute responsibility appropriately.
- Long-term interoperability with ecosystem partners will need to be roadmapped to avoid paying for duplicate functionality from competing vendors.
Microdosing reframed the massive risks of LSD and hallucinogen abuse and created a methodology for introducing their use into the mainstream. Micro-solutioning has the same potential for enterprise organizations. It allows companies to introduce transformative technology at a manageable scale, empower teams to amplify their effectiveness and better prepare for the transformative impact AI will have on the global economy.
