Emerging technologies like large language models (LLMs) are poised to transform how executives and business leaders operate. As a former CTO and current leader of a data management company, I have seen firsthand how innovations can boost productivity and decision-making. In this post, I will explore how chief operating officers, chief financial officers, chief marketing officers and other C-suite executives can specifically leverage LLMs like ChatGPT to enhance their roles.
As a COO, a big part of your job is overseeing company operations and ensuring different teams and systems run smoothly. There are many repetitive aspects of operations that can benefit from some automation powered by AI.
LLMs can help create automated workflows, schedules, templates, and alerts for various business functions. For example, you can have a LLM like ChatGPT:
- Generate weekly status reports automatically by pulling data from various databases and documents. This saves managers hours of manually compiling updates.
- Create calendar invites and agendas for recurring meetings. The LLM can even suggest relevant discussion topics based on past meetings.
- Automate the onboarding process for new hires by producing offer letters, scheduling training sessions, and sending welcome emails.
This level of automation enables you to focus on high-impact strategic planning rather than getting bogged down in day-to-day task management.
By establishing automated workflows like this, the LLM handles rote tasks while you work on innovating and optimizing operations.
LLMs also provide COOs and other executives with the latest research and insights needed to guide important decisions.
Whether you're deciding which new tools and systems to invest in across the company or which business processes to optimize first, you need access to thorough information before presenting options to leadership. Compiling this knowledge yourself is hugely time-consuming.
With a LLM assistant, you can have detailed briefings and recommendations on any subject prepared upon request. Simply ask for a summary on a topic, and the LLM will return a comprehensive overview along with contextual quantitative data, expert perspectives, case studies and other valuable analysis.
For example, before pitching a new ERP implementation, I might ask my LLM:
Provide a brief on ERP systems for mid-size manufacturing firms, comparing leading solutions like SAP, Oracle, Microsoft and Infor. Include ROI case studies, ideal pricing models, and ease of use scores. Focus specifically on IoT capabilities and supply chain integration features.
The LLM would generate a tailored report to inform my recommendation on which ERP to invest in next fiscal year based on our company's needs. This level of speedy yet thorough research support empowers executives to make better data-driven decisions.
To stay competitive, leaders require continuous insights into what peer companies and rivals are doing. Competitive intelligence should inform everything from your product roadmap to your go-to-market strategy.
However, comprehensively tracking competitors across news, articles, events, job postings, patents and more is an enormous undertaking. Humans simply lack the bandwidth.
Luckily, LLMs can take on competitive monitoring at scale. The right prompts can have your LLM assistant continuously scanning for and compiling the latest intel on:
- Industry Trends: Emerging technologies, new regulations, shifts in consumer behavior.
- Funding Rounds: Venture capital flowing to peers and startups entering your space.
- Product Launches: New features competitors are releasing.
- Leadership Updates: Executives joining or departing key players.
- IP Filings: Patents that could influence future R&D directions.
With an always-on LLM research assistant, you save huge analyst hours while unlocking an intelligence stream tailored to your market strategy and priorities.
Financial Planning & Analysis
As a leader steering company finances and operations, having quick access to past growth trends and future projections is critical for decision-making.
This analysis often requires pulling data from multiple systems and painstakingly building slide decks for leadership reviews. It's a drain on finance and ops teams.
With an LLM assistant, FP&A leaders can simply describe the insights they need, and complex models can be produced on demand.
For example, the CFO might ask:
Provide revenue growth projections for next year by region and product line given macroeconomic indicators and our sales velocity data from the past 3 years
The LLM could instantly deliver multi-tab financial models to power senior planning meetings. Leaders save hours of manual work while getting data-first recommendations.
Every business decision involves weighing risk and reward. As an executive determining long-term strategic direction, having rigorous risk assessments of potential investments is crucial.
Unfortunately, risk analysis tends to be a very manual process. Humans struggle to comprehensively map interconnected threats and probabilities.
This is where LLMs shine. Their pattern recognition capabilities plus access to huge training datasets enables LLMs to model complex risk scenarios on the fly.
Executives can easily get risk heat maps for major decisions like:
- New market expansion
- Mergers & acquisitions
- Novel technology adoption
- Supply chain shifts
The LLM can outline financial, operations, regulatory risks inherent to each path. This augments human intuition with data, arming leaders with better risk intelligence.
The Future of Executive Assistants
While LLMs may not replace trusted human advisors, they absolutely make C-suite leaders far more productive and empowered.
By automating rote tasks, generating insights, and assessing risks, LLM assistants amplify executives' capacity 10x. Leaders can rely on instant expertise while focusing energy on higher-level strategy and judgment calls.
And this is just the beginning. As LLMs like ChatGPT become more advanced and conversational, they will work even more seamlessly alongside executives as trusted partners.
The future office of the CFO, CMO, CIO, and COO will certainly have another C in it - Chief AI Officer. LLMs promise to fundamentally evolve business leadership.
Are you in charge of major business decisions? How can an LLM amplifier help you with strategy and operations? I welcome your thoughts and questions. Drop us an email.
How can LLMs improve decision-making for business leaders?
LLMs excel at rapidly compiling data, research, and recommendations to inform strategic decisions. Rather than executives manually gathering intel from various sources, they can provide an LLM with a query outlining the decision context and key factors to consider. The LLM can then leverage both its vast training data and follow-up internet research to generate detailed briefings comparing options. This enables leaders to make more data-driven choices much faster.
For example, a COO might be deciding whether to migrate existing enterprise software to the cloud or undertake a lengthy on-premise ERP upgrade. They can ask an LLM to summarize the costs, risks, and benefits of each path over a 5-year timeline, specifically outlining things like:
- Implementation times
- Vendor capabilities
- Security considerations
- Staff training requirements
The LLM delivers an in-depth but concise briefing, arming the COO to make an informed strategic choice.
What types of executive documents and reporting can LLMs automate?
There is significant potential to use LLMs in automatically generating various collateral that executives rely on strategically manage their business units. This includes things like:
- Dashboards: An LLM can compile weekly or monthly tracking of KPIs like revenue, user conversions, churn rate into digestible visual charts.
- Status Reports: LLMs can autopopulate detailed overviews of program milestones, budget variances, risk metrics and more on recurring schedules.
- Presentations: For standing meetings and reviews, slide decks can be dynamically produced reflecting the latest data outputs.
- Audit Requests: Documentation like policy attestations, inventory activity reports and transaction details can be instantly assembled by an LLM for auditors.
Automating reporting reduces grunt work while giving executives continuity of insights. Humans focus on summarization and exceptions vs. manual creation.
What are some best practices for implementing an executive LLM assistant?
Successfully leveraging an LLM like ChatGPT to enhance executive productivity hinges on some core practices:
- Understand decision contexts - Take time mapping out what strategic choices and analytical needs underpin the executive role. This helps shape LLM queries for maximum relevance.
- Structure requests clearly - Well-framed prompts lead to better LLM output quality. Outline the exact factors, datasets, and considerations to cover.
- Train with domain documents - Feed company reports, plans, process flows etc. to the LLM so it better grasps business context and standards.
- Stage incrementally - Start with lower-risk automations and analysis to validate quality. Scale usage as confidence grows.
- Monitor for bias - Audit early LLM output for biases inherited from broad training data, given societal gaps that persist in language models today.
What risks come with relying on AI assistants for executives decision-making?
While promising, empowering business leaders with LLMs does introduce some real hazards, including:
- Output bias: LLMs can surface harmful biases from their training data. Leaders should critically evaluate results.
- Over-reliance: Humans must retain accountability vs. blindly accepting LLM recommendations.
- Security hazards: Attackers could target executive LLMs, as data is highly sensitive. Protections are critical.
- Loss of human checks & balances: AI assistants erode traditional governance of CEO decision-making by boards and advisors.
Organizations should proactively develop guidelines addressing ethical application of LLMs at the leadership level, given societal implications.
How could over-reliance on an LLM impact strategic thinking?
If executives lean too heavily on AI assistants like LLMs for insights, they risk diminishing critical faculties like strategic intuition, future planning, and deductive reasoning developed through experience making complex multifactor decisions. If all analysis is delegated, human leaders lose practice sharpening high-level cognitive capabilities that separate human intelligence.
Over-outsourcing executive thinking also tends to narrow perspectives to just quantitative recommendations from LLMs. The systematization of decision-making through LLM guidance can cause a loss of outside-the-box solutions and diminish executive creativity. Leaders end up trapped in echo chambers of the AI assistant's logic, rather than bringing their sparks of ingenious insight.
Preserving human innovation and wisdom even in augmented intelligence environments will require leaders to actively develop their own strategic thinking through regular practice - not just consumption of LLM briefing books.
How can we prevent biases in LLM outputs from negatively impacting leadership decisions?
While LLMs like ChatGPT show promise, most also demonstrate observable biases from their training data that could skew executive decisions if left unchecked. Leaders relying on LLMs for key analytical support should:
- Actively monitor for biased language, stereotyping, exclusion in generated briefings.
- Ensure the data the LLM trains on represents diversity across gender, ethnicity, age, ability.
- Formally audit early LLM work products for fairness defects before scaling adoption.
- Ask specific questions to test if prejudicial beliefs are embedded, e.g., “How would recommendations change if leadership was predominantly female?”
- Report skewed or unfair outputs to the LLM vendor, so its models can be refined.
It is also critical leaders retain accountability rather than blindly trusting insights. LLMs provide decision support, but human executives must have override authority to catch issues missed by algorithms.
What impact could LLM executive assistants have on company culture?
As with any transformative technology touching daily work, bringing LLMs into executive roles will absolutely impact organizational culture in some intended and unintended ways, including:
- Productivity obsession: Leaders may push harder for 24×7 work cycles now that LLMs enable round the clock support. This could enable overwork.
- Data-driven doctrine: With LLMs delivering analytics-backed recommendations, decisions based on gut feel or experience may lose influence, changing norms.
- Transparency challenges: If executive strategy is heavily dictated by black box LLMs, there may be cultural clashes with staff demanding more visibility.
- Loss of loyalty: The role of loyal human advisors shielding leaders could be displaced by artificial assistants focused solely on tasks over relationships.
Ultimately, businesses must continually assess and manage cultural perceptions as AI augmentation alters collective beliefs on how executive roles should operate.
What new skills will the executive assistant role require in an age of AI augmentation?
While LLMs like ChatGPT handle rote information compilation and basic document production, human assistants to executives will need to adapt their capabilities to focus more on emotionally intelligent support aligned to AI collaboration, including:
- Relationship building – Because LLMs lack empathy and nuanced personality, human assistants play an irreplaceable role understanding executives needs and motivations to ensure technology alignment.
- Critical thinking – Assistants must review LLM outputs with skepticism rather than taking them as absolute to catch errors and question assumptions baked into AI logic.
- Creative coordination – Planning meetings, off sites, sensitive conversations will require uniquely human tuning even if scheduling, data prep and background docs are automated.
- Technical translation – Helping leaders articulate clear, effective prompts and contextualizing LLM limitations calls for both technical and interpersonal tact.
The most effective executive assistants will embrace opportunities to elevate soft skills and high-order support unmatched by even advanced AI.
How could regulators intervene if LLM executive adoption presents societal concerns?
As large language models become entrenched in business leadership and decision-making, policymakers attuned to ethical risks have a few options to provide guardrails, including:
- Mandating bias testing - Require proof of bias detection as a prerequisite to legally selling LLM services to executives.
- Incentivizing diversity - Provide tax credits or subsidies for significantly expanding minority representation in LLM training datasets.
- Enforcing transparency - Compel LLM providers to share model logic, explain outputs, and warn on potential harms stemming from overreliance.
- Restricting applications - Limit most advanced LLM assistants only to narrow executive use cases unlikely to directly impact protected groups through exclusions or skewed decisions.
Finding the right balance between innovation and oversight will call for ongoing public-private collaboration as LLM capabilities grow more transformational.
Is a solution and ROI-driven CTO, consultant, and system integrator with experience in deploying data integrations, Data Hubs, Master Data Management, Data Quality, and Data Warehousing solutions. He has a passion for solving complex data problems. His career experience showcases his drive to deliver software and timely solutions for business needs.