The COO’s Guide to AI Productivity Tools

Innovation is the currency that helps businesses grow. 

As the COO, you are not just the manager of a company’s day-to-day operations; you are also the visionary that bridges strategy with execution. 

One of the most transformative tools at your disposal today is Artificial Intelligence (AI). 

By most accounts, the economic impact of generative AI on global productivity will likely result in a multi trillion-dollar boost to the world economy. McKinsey’s latest research, estimates that generative AI has the potential to contribute between $2.6 trillion and $4.4 trillion annually across the 63 scenarios they examined. To put that in perspective, the UK's GDP for the entire year of 2021 stood at $3.1 trillion. 

According to the Nielsen Normal Group the use of generative AI in business boosted employee performance by 66% across the three scenarios studied. 

While the above reports provide ideas for which parts of a business can be augmented by AI, the specifics can vary based on company size, business model, and product. We spoke with operations leaders across industries to understand their real-world experiences with bringing AI into their businesses.  

This guide to AI productivity tools aims to demystify AI for the modern COO, while also diving into the most effective AI business tools and their applications within the startup ecosystem. 

What AI productivity tools can do for your business today (+ future capabilities)

AI tools can be used to automate tasks, improve decision-making, and develop new products and services. This rapidly evolving field offers tools that streamline operations and enable innovative strategies. 

Let's delve into the current and future capabilities of AI productivity tools in the business realm.

1. Information gathering

• Today: AI-driven platforms can find and collate information across disparate data sources, making the process of research and synthesis easier than ever. They can also monitor market trends, track consumer behavior online, and gauge sentiment. 
• Tomorrow: Advanced AI systems might provide businesses real-time global market shifts, nuanced consumer sentiment evaluations, and even predict emergent industry trends by correlating diverse data sources.

2. Generation

• Today: AI models can generate marketing content, financial reports, and even assist in product design. Open source LLMs empower businesses to streamline operations, improve customer engagement, and enhance decision-making through applications like text generation, code generation, chatbots, and sentiment analysis.
• Tomorrow: We can anticipate AI generating strategic business proposals, crafting comprehensive market strategies, or even simulating product launches to predict outcomes.

3. Analysis

• Today: Businesses use AI for sales forecasts, risk analysis, and customer behavior predictions. They're invaluable in sectors like finance for fraud detection and in marketing for understanding campaign efficacies.
• Tomorrow: According to HubSpot’s CMO, Kipp Bodnar, AI might offer multi-dimensional business insights by concurrently analyzing different data facets, such as sales data and global economic indicators. This combination could provide more holistic business strategies and risk assessments.

4. Decision-making

• Today: Automated tools, powered by AI can handle inventory, customer service queries, and supply chain optimizations. In the virtual space, AI-driven bots manage client interactions, and automated systems optimize digital marketing campaigns.
• Tomorrow: We could see AI-driven departments making autonomous business decisions based on real-time data, robots in retail engaging and assisting customers, or AI systems orchestrating complex, global logistics operations seamlessly.

How operations leaders are utilizing AI in their businesses 

Businesses are adopting AI productivity tools to improve efficiencies, gain productivity, and cut down on costs. 
A Forbes Advisor survey of 600 business owners using or planning to use AI found that the impact of AI was most felt in the areas of customer service, cybersecurity, fraud management, and digital assistants. 

Source

Enhancing customer-facing products and services

Pretty much every tech company has added or is exploring adding AI to their product either through third-party integrations or building a native AI solution. 

AI can also help improve response times for customer service interactions through automated support bots, sentiment analysis, auto-translation, and more. 

Some areas to consider related to improving customer support:

• Chatbots for common queries
• Suggested solutions and routing
• Drafting emails 
• Automated documentation
• Automated translation

Improving internal efficiency 

This is perhaps the most important area of focus for ops leaders. However, the specifics are largely business-dependent and require identifying time-consuming processes and tasks across departments and examining the ability to automate them while maintaining security and quality. 

Some areas to consider: 

• Code generation
• Quality assurance for code 
• Copywriting and editing
• Chatbots for internal Q&A and IT/HR
• Drafting emails 
• Video editing
• Intelligent search 

Businesses must also take into account the limitations of the current versions of AI like hallucinations or its inability to understand context. This is especially dangerous when using tools like ChatGPT that do not provide references and citations in their responses. 

Something digital marketing agency, GR0, that experimented with ChatGPT can attest to. Earlier this year, they used the popular generative AI tool to improve writing efficiency for long- and short-form content creation with mixed results. 

Kevin Miller, co-founder & CEO of GR0, shared why they wanted to automate the process and the results it brought them:

“For our clients, we want to produce the most high-quality work possible to help them grow their domain authority and online traffic, so automation was a natural strategy to pursue that goal. 

We tried to improve workflow efficiency by up to 400% while experimenting with AI tools, asking writers to adapt their workflows and give feedback on how well ChatGPT helped improve their writing and deliverability. Although we did not hit those marks due to many natural obstacles and limitations of the ChatGPT software, we did increase writing efficiency by 200% through content templates and research assistance.”

Ops leaders also voiced concerns about the use of proprietary data to train large language models like ChatGPT. In general, the trend is to opt for AI assistants that can guarantee that a company’s conversational data will be secure and private. 

Research and information gathering

The Forbes Advisor survey found that 47% of companies use AI as a digital assistant. 

But the same research also found that there’s fear among one-third of the businesses that it may cause some jobs to become redundant because as AI gains new capabilities, we may start seeing it replace entire teams. 

And that’s where A/B testing and experimentation platform, Convert, has a different take. Their responsible-use AI policy found here has a simple takeaway — “replace tools, not people.”  

Convert’s Head of Ops, Marlon Jansz, elaborated, “The idea isn’t to replace human creativity, thought, and empathy with AI. It is to support the creative process so that the end result is better than before.” 

Some use cases to explore related to research and information gathering: 

• Customer and prospect fact-sheets 
• Idea validation 
• Literature reviews 
• Data mining 
• SEO research

Unlocking better insights faster with AI-powered analytics

For software development company Capicua, AI is transforming how they use and extract insights from their data. 

Carlos Traibel, Partner and COO at Capicua shared, “We're exploring AI-powered analytics to gain deeper insights from our data, allowing for more informed decision-making and personalized customer offerings. This includes predictive analytics for demand forecasting and improving recommendation engines for product suggestions.”

Some areas to explore related to AI analytics and data: 

• Automated data processing
• Natural language querying of large databases 
• Predictive analytics
• Democratization of insights

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Butterfly MX has already accelerated Support’s average handling time by enabling Dashworks AI-generated prompts while answering live customer calls.

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How to evaluate AI productivity tools (+ one tip to increase AI adoption)

Companies are now beginning to put formal processes in place to help them evaluate the glut of AI tools in the market. Here are the top three takeaways from our chat with Ops Leaders:

Create an evaluation guide

Abhishek Shah, Founder and CEO of Testlify (a talent assessment platform), has a step-by-step guide to find and implement the right AI productivity tools:

“To select the right AI tools, we follow these steps:

• Pinpoint areas where AI could offer the most significant benefits.
• Research available AI solutions extensively, considering scalability, reliability, and compatibility.
• Conduct pilot tests to evaluate performance and user-friendliness.
• Fine-tune AI tools selection and use cases based on user feedback” 

Tap into lateral thinkers

Another tactic COOs often take is to assign the process of finding AI productivity tools to lateral thinkers across the organizations. 

For example, Phong Tran, Director of Design, from influencer marketing agency, Obviously,  says, “The mandate is efficiency across the board. One of the reasons [the COO] is putting [the] designer (Phong) on (an evaluation process for AI tools) is for lateral thinking. We're looking at every role, every task they do, and considering if it can be automated or if there's a different way of thinking about it.”

Hire an AI coach

Rolling out these AI tools, of course, is a whole other ballgame. At Convert, AI is used across the board following their responsible use policy guidelines.

“We do have an AI Coach role that looks into how best to leverage AI for internal & external stakeholders so Convert can live its values better. Again, it is not about pumping out the most assets or doing something the fastest. We know where we have to go, AI can help us get there sooner.”

Tip to increase AI adoption among employees: Since the idea behind using AI is to improve processes, not replace people, anxiety about using AI is generally low. But everyone at Convert is encouraged (and empowered) to reach out to the AI Coach, and incorporate AI in ways that are meaningful to their Jobs to be Done (JTBD).

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Taking these into account, we propose the following process (including learnings from the SMEs), and here are the axes to evaluate products on.

• Scalability - Will this tool grow with your business needs?
• Accuracy - Is the tool prone to hallucinations and inaccuracies? Make sure that your AI assistant includes references.  

In HeyGen, the sales reps leveraged Dashwork’s AI prompts to process almost 40% of common questions instantly, thanks to references included in Slack bot’s answers.

• Vendor Support - Can you get the help you need to implement tailored use-cases from the AI partner?
• Security - Is your data secure? (On-prem tools over cloud deployment can help keep your data safe, but they can be extremely expensive to implement. We recommend using AI tools that do not store your data on their servers.)

Pro Tip: Don’t buy before you try! We recommend choosing AI tools that offer a free trial or a freemium plan. A free trial lets you fully understand if and how an AI tool can impact your business.

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AI productivity tools you should consider: What experts and the data says

Based on our confab with ops leaders and additional research, we’ve identified some of the best tools to consider in each of the categories above: 

1. Customer support

• Tool: Intercom - AI-powered chatbot system to automate customer support queries. 

Best use case: Live chat

• Tool: Zendesk's Answer Bot - Uses AI to provide instant answers to customer questions.

Best use case: Self-service

2. Internal Processes

• Tool: Dashworks: An intelligent assistant that can surface insights, summarize documents and Slack conversations, generate content, and create code. 

Best use case: Company’s knowledge assistant

• Tool: Descript - Streamlined video editing through transcription and text script manipulation

Best use case: Efficiently edit talking head videos by transcribing content and manipulating the text script

• Tool: HubSpot: Uses AI to score leads, segment contacts, and personalize content for more effective marketing campaigns.

Best use case: Marketing - repurposing content | Sales - predictive sales forecasts

• Tool: Drift: An AI chatbot specifically tailored for sales and lead generation on websites.

Best use case: Patented Conversational AI understands and responds to humans.

3. Research and information gathering

• Tool: CAFFE - Used for creating startup prototypes, academic research projects, and large-scale industrial applications in multimedia, speech, and vision.

Best use case: Research experiments and industry deployment

• Tool: Aomni - Utilizes AI agent technology for automated research.

Best use case: Provides a comprehensive summary of the research process.

4. Analytics and data

• Tool: Tableau - Visual analytics platform for code-free data exploration, analysis, and modeling, with machine learning capabilities.

Best use case: Effortless data exploration, analysis, and modeling without coding, along with integrated machine learning features

• Tool: Tellius - Business intelligence platform powered by AI. 

Best use case: Facilitates data understanding, visualization, and actionable insights through an intelligent search function and user queries.

The Future of Productivity with AI: The COO's Playbook

AI's potential to enhance productivity is multi-dimensional. It promises not just efficiency but precision, customization, and adaptability. 

Virtual assistants will manage our schedules, AI-driven analytics will offer real-time insights and collaborative robots will work alongside humans in warehouses, factories, and offices. 

The mundane will be automated, freeing human capital to focus on creativity, strategy, and innovation.

So it worked out for Tremendous as they filled the knowledge gap between product decision-makers and operational workers using Dashworks as a go-to for everyday enterprise searches.

But this AI-driven productivity surge isn't without its challenges. Data privacy concerns, the ethical use of AI, and potential job displacements are issues that COOs will need to navigate. 

Preparation is key. COOs should invest in continuous learning programs, ensuring that their workforce is not just AI-literate but also adaptable to its evolving nuances. 

Embracing a culture of change, fostering interdisciplinary collaboration, and promoting ethical AI use will be the cornerstone of a successful transition.

Want to bring AI to your team without compromising privacy or security? Get the answers you need in seconds with Dashworks - your company’s knowledge assistant.