AI for Enhancing Productivity - learning in the flow of work
- rs1499
- Mar 13
- 11 min read
Updated: Mar 23
This is the third of a three-part series focused on the use of AI in corporate environments.
This first piece focuses on AI for hiring, the second focused on AI for training and the third (this one!) focuses on AI for enhancing productivity. In some ways, this final piece on productivity pulls together some of the key themes observed in the prior two blogs.
We will introduce the key areas of innovation in each article, before presenting a market map inclusive of differentiation angles and opportunities for startups.
This article works through:
To jump ahead, click the links above!
Without further ado...
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The evolution of productivity in corporate environments
Productivity has long been a key driver of corporate success - how productivity gains have been powered has evolved markedly through technological advancements that have redefined how businesses operate. From mechanisation to the digitisation of the late 20th century, each innovation wave has reshaped the nature of work.
Yet, despite this progress, global productivity at large has faced significant challenges in recent years. The 2008 financial crisis caused a harsh reset on productivity growth across all sectors.
Our generation's industrial revolution is being driven by artificial intelligence (AI), with its promise to automate tasks, enhance human capabilities, and enable employees to focus on higher-value, strategic work. We frame this as 'learning in the flow of work' - employees having access to the knowledge, information and guidance they need in the moment they need it, powering improved decision-making, efficiency and business outcomes.
Part 1 - how is AI transforming productivity in corporates?
AI’s expansion into corporate environments
Hannah Seal, partner at Index Ventures, commented:
"AI has already delivered a massive efficiency boost to software engineering teams. In nearly every company I work with, engineers rely on tools like GitHub Copilot, Cursor, or similar AI-powered coding assistants to accelerate development."
But AI is not just transforming engineering.
Hannah continues:
"Now, we're seeing AI transform nearly every part of an organisation, from finance and law to HR and beyond. This shift is accelerating, and in many cases, it's already well underway."
According to Seal, AI now handles 80% of routine work, while employees focus on the remaining 20%- the high-value, strategic tasks that require human insight.
According to Mckinsey in their research on the use of generative AI specifically: "about 75 percent of the value that generative AI use cases could deliver falls across four areas: Customer operations, marketing and sales, software engineering, and R&D." They examined 63 use cases across 16 business functions, considering specific business challenges in ways that produce one or more measurable outcomes. Examples include generative AI’s ability to support interactions with customers, generate creative content for marketing and sales, and draft computer code based on natural-language prompts, among many other tasks.
They go on to cite specific sectors and industries likely to experience the most substantial financial impact: "banking, high tech, and life sciences are among the industries that could see the biggest impact as a percentage of their revenues from generative AI. Across the banking industry, for example, the technology could deliver value equal to an additional $200 billion to $340 billion annually if the use cases were fully implemented."
Along a similar vein, Jon Lexa, President at Sana highlights the impact of AI on enterprise knowledge management.
"AI-powered enterprise search is dramatically reducing the time employees spend looking for information. Take a salesperson, for example: our technology can automatically transcribe and summarise meetings, then take the salesperson’s next steps and seamlessly integrate them with Salesforce. This eliminates the need for the salesperson to manually interact with multiple platforms, saving significant time."
Sana has seen efficiency gains of up to 90% in structured workflows like CRM automation.
Meanwhile, as stated elsewhere in this articles series, AI is transforming onboarding and training processes, which have traditionally been time-consuming and inconsistent. Raf Guper, co-founder at Ujji AI, notes:
"Slow time to value is one of the biggest pains leaders struggle with, especially as teams expand rapidly. It takes 6 to 12 months on average for new joiners to achieve full productivity. By using AI to structure customised onboarding playbooks and transform internal documentation into bite-sized training experiences, companies can significantly shorten this timeline."
According to Raf, one client, AyaData, saw its training NPS soar to 9.5 after implementing AI-driven onboarding.
Moving from 'clickers' to strategic thinkers
More broadly, AI is redefining how knowledge work is structured. Instead of merely digitising processes, it enables real-time knowledge retrieval, automated documentation, and improved decision-making. Claudio Erba, founder of Docebo, argues that AI should move employees away from a ‘clicker’ mentality, where work is defined by routine actions, toward a more strategic and problem-solving approach.
"Humans will not be operators. They must stop simply clicking and thinking that their job is clicking. They need to think like strategists, oriented around specific objectives in their organisation. In the next three years, I predict huge AI adoption across all sectors, but this will require re-skilling employees into roles that engage with AI as prompt engineers rather than passive operators."
This shift is also changing how businesses measure productivity. Traditional productivity metrics, such as output per hour worked, are being augmented by AI-driven insights into efficiency, innovation cycles, and collaboration.
Indeed, Martin Mason, co-founder at TalentMapper states:
"We consider our primary way of measuring ROI as reduced time to hire and improved talent identification. By enabling internal promotions and better succession planning, we help companies save on recruitment costs and ensure retention of key talent. This, in turn, enhances overall productivity."
Martin's insights reflect a broader trend of AI-driven workforce planning, where skills are dynamically mapped to business needs.
The rise of AI-driven learning and decision-making
AI is also transforming how organisations approach learning and development (L&D). Michelle Connon-Roodt, Global People Consulting at EY notes that employees are increasingly comfortable with AI-powered learning tools:
"Most employees are receptive to working with AI companions and coaches. They like the idea of experiencing learning in the flow of work. There is also a perception that employees may feel able to be more honest with AI than with human coaches or their managers. AI feels like an ally that’s working on your side, so it’s easier to be honest and not fear judgment."
However, she also cautions that AI struggles with context, nuance, and balanced reasoning, making human oversight crucial.
AI is also closing the gap between L&D and business operations.
Lavinia Mehedintu from Offbeat notes:
"Because L&D is taking place closer and closer to frontline operations via just-in-time learning, its impact is seen more directly in business development and customer success."
AI is improving this attribution by integrating learning data with broader business metrics.
The human-AI balance: challenges and ethical considerations
Despite these productivity gains, AI adoption comes with challenges. Roxana Dobrescu, Chief People Officer at commercetools, warns that while AI optimises workflows, it cannot fully replace human intuition, creativity, or cultural understanding:
"AI is a powerful enabler in hiring and productivity, but it’s not a magic wand. Where it truly shines is in efficiency—automating repetitive tasks, sourcing candidates at scale, and analysing large data sets to surface insights. However, hiring, team dynamics, and cultural fit still require human judgment. AI should be a co-pilot, not the final decision-maker."
Similarly, Michelle Connon-Roodt from EY emphasises the importance of trust and transparency.
"There is an enormous need for people to be able to trust the privacy of the AI systems they are using. Most choices around AI adoption are currently made for quick wins, but the next phase will see HR and People teams moving from the sidelines to centre stage in driving AI integration."
Moving beyond the areas of corporates most impacted by AI, there are five key ways that AI is transforming productivity in corporate environments:
Automating routine tasks and reducing manual work
AI is eliminating repetitive, time-consuming tasks, freeing employees to focus on higher-value work. Companies are leveraging AI for: workflow automation; email and document automation; finance and HR automation.
Enhancing decision-making and strategic insights
AI is supercharging corporate decision-making by analysing vast amounts of data and providing real-time insights. Instead of relying solely on human intuition, businesses now: leverage predictive analytics to anticipate market trends and optimise business strategies; use AI-driven business intelligence to extract insights from large datasets; enable AI-powered financial modelling to assess investment risks and opportunities.
Revolutionising workplace collaboration and knowledge sharing
AI is breaking down information silos and improving collaboration across departments by: AI-powered enterprise search that retrieves knowledge in real time; intelligent document management that categorises, organises, and summarises reports; smart communication assistants that help employees manage emails, chat responses, and meeting follow-ups.
Personalising learning and employee development
AI is transforming corporate training & employee development by: creating adaptive learning experiences tailored to individual employees; providing AI coaching and mentoring to upskill employees faster; Identifying workforce skill gaps and recommending personalised training paths.
Enhancing creativity, content generation and ideation
AI is augmenting human creativity by generating text, images, code, and ideas, helping companies: scale content creation across marketing, sales, and product teams; automate brainstorming and ideation processes; generate presentations, reports, and client proposals with minimal effort.
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Part 2 - market map and analysis of opportunities
To better understand how different AI solutions fit into this evolving landscape, we use a market map structured along two key axes:
The X-Axis
Workflow automation vs. knowledge work augmentation
Workflow automation: AI reduces manual effort in routine, repetitive tasks (e.g., AI-driven task automation, meeting summarisation, automated data entry).
Knowledge work augmentation: AI enhances decision-making, creativity, and strategic work (e.g., AI copilots for knowledge workers, generative AI for content creation).
The X-Axis
Function-specific vs. cross-organisation tools
Function-specific: AI tools tailored for specific departments or professional functions (e.g., AI-powered CRM for sales, AI-driven project management for engineers).
Cross-organisation: AI tools applicable across different roles and industries (e.g., AI email assistants, AI-powered search and document organisation).
By plotting AI solutions along these axes, we can categorise workplace productivity tools into four distinct quadrants.
Quadrant-by-quadrant breakdown
1. Workflow automation & function-specific AI (Bottom-Left)
"Task executors for specialised roles"
These AI tools automate repetitive, operational tasks within specific business functions. They aim to reduce manual effort, improve efficiency, and eliminate bottlenecks in areas such as finance, HR, legal, and customer service.
2. Workflow automation & cross organisation AI (Top-left)
"Universal efficiency boosters"
This quadrant covers broad-use AI tools that automate repetitive tasks across different functions and industries. These solutions help employees optimise their time, minimise distractions, and complete everyday tasks more efficiently.
3. Knowledge work augmentation & function-specific AI (Bottom-right)
"Intelligent advisors for specialised roles"
These AI tools support professionals in making more informed, high-value decisions rather than just automating their tasks. They act as AI copilots, helping users synthesise data, generate ideas, and optimise their workflows within specific functions.
4. Knowledge work augmentation & cross-organisation AI (Top-Right)
"AI-powered knowledge companions"
These AI tools are designed to enhance creativity, problem-solving, and strategic thinking across multiple industries. They help knowledge workers by providing insights, recommendations, and content generation to amplify human decision-making.
If you have comments, feedback or would like to be added, please get in touch with rs@brighteyevc.com! 🚧
Avenues of differentiation
AI-driven productivity solutions differentiate themselves based on multiple factors, including technological approach, target users, business function, data handling, and value delivery. Below are the most important areas of differentiation:
1. Depth vs. Breadth of AI Functionality
Specialised AI → Companies focus on a specific use case or industry vertical (e.g., AI for legal contract review → Luminance, Lawgeex).
Broad AI Platforms → Companies offer AI for multiple productivity functions, often integrating across workflows (e.g., Microsoft Copilot, ServiceNow AI).
Hybrid AI Solutions → Companies offer both specialised and general AI capabilities within an ecosystem (e.g., Salesforce Einstein provides AI for CRM, analytics, and automation).
2. Automation vs. Augmentation
Automation-Focused AI → AI replaces human effort in repetitive workflows (e.g., UiPath for RPA, Paradox AI for hiring).
Augmentation-Focused AI → AI enhances decision-making without replacing human work (e.g., Notion AI for writing, Tableau AI for analytics).
Mixed Models → Some companies provide both automation & augmentation based on user needs (e.g., Sana enhances learning & automates knowledge management).
3. User Experience & Interface
AI-First Interfaces → AI acts as the primary interaction method (e.g., x.ai for scheduling, Moveworks for IT automation).
Embedded AI in Existing Tools → AI enhances familiar software (e.g., Adobe Sensei inside Photoshop, Jasper AI inside Google Docs).
No-Code AI vs. Developer-Focused AI → No-code tools empower non-technical users (e.g., Notion AI, Canva AI); Developer-oriented AI requires technical expertise (e.g., GitHub Copilot).
4. Data Handling & Customisation
Pre-Trained AI Models → AI uses pre-built models trained on generic data (e.g., Grammarly, ChatGPT).
Enterprise-Specific AI → AI models train on company-specific data for custom workflows (e.g., Sana AI for enterprise knowledge).
On-Premise vs. Cloud AI → Cloud-based AI allows seamless updates and scalability (Google Bard, Jasper AI); On-premise AI ensures data security for highly regulated industries (IBM Watson for finance & healthcare).
5. Integration & Ecosystem
Standalone AI Tools → These work independently and require users to switch platforms.
Embedded AI in Large Ecosystems → AI integrates into existing enterprise software suites (Salesforce Einstein, Microsoft Copilot, Google AI).
API-First AI Models → Companies provide AI as a service for integration into other platforms (OpenAI’s GPT API, Anthropic Claude API).
6. AI Training & Adaptability
Static AI Models → AI operates using fixed models and requires manual updates (traditional chatbots, rule-based AI).
Adaptive AI Models → AI continuously learns from user input, feedback, and data interactions (Salesforce Einstein, Moveworks AI).
Retrieval-Augmented Generation (RAG) → AI retrieves contextual data dynamically to generate better responses (Notion AI, Sana AI).
7. Industry-Specific vs. Cross-Industry AI
AI for Industry-Specific Use Cases → Tailored for finance, legal, HR, sales, customer support, etc. (Kensho AI for finance, Lawgeex for legal AI).
Cross-Industry Productivity AI → AI designed for broad workplace use (Otter.ai for transcription, Notion AI for writing, OpenAI ChatGPT).
8. Monetisation & Pricing Models
Subscription-Based SaaS AI →Users pay monthly for AI access (Notion AI, Fireflies.ai, Grammarly).
Pay-Per-Use AI APIs → Companies charge based on usage (OpenAI API, Anthropic Claude API).
Enterprise AI Licensing → Large businesses pay for custom AI deployment (IBM Watson, SAP Leonardo AI).
Where is the white space and where are the opportunities?
Below are 6 opportunities where AI-driven startups could differentiate and capture unmet demand.
1. Cross-Functional AI Assistants (bridging multiple functions in a company)
Most AI tools today are either department-specific (e.g., AI for Sales, HR, Finance) or broad productivity tools (e.g., AI note-takers, email assistants). The white space is in AI copilots that seamlessly operate across multiple business functions.
AI copilots for specific departments exist.
AI meeting tools exist.
But AI rarely bridges multiple departments within a company to synthesise data across finance, HR, sales, and operations.
Potential model: An "Enterprise Copilot" that connects HR, sales, finance, and operations insights into a single AI-powered dashboard.
2. Adaptive AI for Highly Regulated Industries (e.g., Finance, Healthcare, Legal)
AI is widely used for automation, but highly regulated industries (e.g., finance, healthcare, legal, government) lack AI solutions that meet compliance and security requirements.
AI for legal contract review (Lawgeex, Luminance) exists, but few tools support decision-making in legal strategy or compliance.
AI in finance (BloombergGPT, Kensho) provides market insights but doesn’t assist with regulatory compliance or audit management.
AI for healthcare exists (IBM Watson Health) but lacks decision-making tools to help doctors interpret medical data.
Potential model: An AI "Regulatory Intelligence Copilot" that helps banks, legal teams, and healthcare providers automatically assess compliance risks.
3. AI for Workforce Planning & Internal Talent Mobility
Most AI tools focus on external hiring, performance tracking, or automation- but few help businesses proactively manage internal workforce skills, talent movement, and succession planning.
AI for hiring (Paradox, Beamery AI) is strong.
AI for employee training (Docebo, Sana AI) is emerging.
But AI-driven workforce planning & internal mobility tools are still limited.
Potential model: An AI-powered "Internal Talent Copilot" that predicts employee career paths, recommends training, and aligns workforce planning with business strategy.
4. AI for Workplace Well-Being & Employee Productivity Analytics
AI for task execution is strong, but AI that monitors and optimises workplace well-being and work-life balance is underdeveloped.
AI for email summarisation, scheduling, and automation exists (Motion, x.ai).
But AI that proactively prevents burnout, workload overload, and disengagement is missing.
Existing productivity tracking software is employee-surveillance-heavy, not well-being-focused.
Potential model: An AI "Work-Life Balance Copilot" that tracks workload, prevents burnout, and recommends energy-optimised work schedules.
5. AI for Collaborative Decision-Making & Strategy
AI helps individuals with tasks (writing, coding, summarising), but few AI tools help teams make complex strategic decisions together.
AI for content creation (Jasper, ChatGPT) is strong.
AI for BI & analytics (Tableau AI, ThoughtSpot) exists but is mostly data-focused, not decision-focused.
AI lacks tools for collaborative brainstorming, team alignment, and group decision-making.
Potential model: An AI "Team Decision-Making Assistant" that analyses data, synthesises input, and suggests consensus-based recommendations.
6. AI for Blue-Collar & Non-Digital Workers
AI productivity tools overwhelmingly focus on knowledge workers (e.g., office jobs, finance, software, legal). There is a major gap in AI for frontline workers, skilled trades, and non-digital industries.
AI for office productivity (Notion AI, Microsoft Copilot) is robust.
AI for blue-collar industries (construction, logistics, healthcare, retail) is underdeveloped.
Many desk-less workers lack AI tools that help them optimise tasks, workflows, and efficiency.
Potential model: An "AI Copilot for Frontline Workers" that helps warehouse workers, technicians, and retail employees with task automation, safety monitoring, and workflow efficiency.
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