How we plan for GenAI
Task-Based Analysis is a tool that can help pragmatically prepare your organization for the impact of generative AI with a strong focus on optimizing for performance.
A brief introduction to TBA
Meet task-based analysis
In 2024 many companies and organizations have found themselves in the midst of an AI gold rush — racing to either defend against or capitalize on generative AI. Service agencies like DK&A and our sister companies in The North Alliance are particularly exposed. Our business model—lending out top minds by the hour—has always been volatile, with low sales visibility beyond a few months. So, how can we—and companies like us—prepare for the impact of generative AI?
The Usable AI™ framework
We have spent the better part of the past year defining an approach that enables us and our clients to identify potential solutions. Our Usable AI™ framework helps teams answer two primary questions:
How does generative AI relate to our business? Where are the key risks and opportunities?
Given a concept for an AI-powered product or service—does it work? Does it solve actual customer challenges? Does it deliver value?
While the second question can be tackled with traditional methods like design thinking and prototyping (e.g. in the form of our AI Validation Sprint), the first requires a bespoke approach.
Task-based analysis
We’ve heavily invested in a repeatable, adaptable process to help companies understand how generative AI applies to them. One of the key tools in our tool belt is called “task-based analysis” (TBA).
TBA involves gathering and classifying the most important tasks executed in any given company, then matching them with generative AI capabilities. This bottom-up approach helps create an AI-specific vision of the company's future.
Step-by-step guide to TBA
Identify key roles: Select the 3–5 most critical roles in your business, considering both internal and external roles, including customers and suppliers. Use a voting strategy to narrow down and rank a larger selection if needed.
Define key tasks: Specify the day-to-day tasks of the selected roles using our “human toolbox*” of 10–15 key tasks. Customize each task to fit the role (e.g., “writing” becomes “writing copy for ads” or “writing code”). Write each task on a post-it note of the same color.
Rank tasks: Place tasks on the TBA canvas, ranking them by repetitiveness on the horizontal axis. Highly repetitive tasks go to the right, while customized tasks go to the left.
Assess value: Rank tasks by their value to the organization on the vertical axis. Define “value” upfront according to your organization's priorities.
Match with AI capabilities: Match each human task with its AI-enabled counterpart using our “genAI toolbox*.” Write AI tasks on post-it notes of a different color and adjust their position on the canvas if needed. Draw arrows to show the transition from human to AI-enabled tasks.
Analyze the AI-enabled role: Evaluate how the role changes with AI. Identify potential areas for automation and value enhancement.
(* For copies of the human and AI toolboxes send an email to ai@dka.io)
The GenAI lens
As time goes on, more tasks are likely to be enhanced or automated by genAI. Companies that implement strategies to move with this transition will equally be more likely to succeed. For example, companies providing expert consulting services might invest in creating domain-specific products to offer their services more efficiently or to new markets. So might a consultancy focusing on brand and identity work productize parts of their asset creation pipeline or a service design agency customer research processes.
Why task-based analysis?
The technology powering the genAI revolution is developing rapidly, making strategic planning and tactical execution challenging. Instead of focusing on long timelines and future AI developments, we focus on human tasks that generative AI can enhance or automate. This approach has been echoed by industry experts like Andrew Ng after attending the World Economic Forum in Davos:
My takeaways from attending WEF at Davos last week:
- There were lots of discussions on business implementation of AI.
My top two tips:
(i) Pretty much all knowledge workers can benefit from using GenAI now, but most will need training.
(ii) Task-based analysis of jobs is helping businesses identify opportunities.- Also lots of AI regulation conversations. I'm happy to report that the conversation is much more sensible than 6 months ago. For example, the unnecessary fears and discussion on AI extinction risk is fading away. But some big companies are still pushing for stifling, anti-competitive regulations, and the fight to protect open-source is still far from won.
- Attending climate sessions made me even more worried about the lack of action to change our planet's trajectory. Rather than 1.5 degrees Celsius of warming as the optimistic case and 2 degrees as the pessimistic case, I think 2 degrees is an optimistic case, and 4 degrees a more realistic pessimistic case. Decarbonization remains critical; and unfortunately, that we're talking about 1.5-2 degrees rather than 2-4 degrees means we're underinvesting in resilience, adaptation, and potentially game-changing technologies like geo-engineering.
Benefits of TBA
Given the tremendous hype around genAI it might at times seem difficult to keep a pragmatic point of view, focusing on actionable, achievable goals without losing sight of the bigger picture. TBA has helped us to identify potential risks and opportunities as they relate to us. We found that this approach checks a number of important boxes for us:
Continuous improvement: Focusing on tasks encourages a culture of continuous improvement. As we identify and automate tasks incrementally, we discover new opportunities for innovation, enhancing productivity and competitive advantage.
Incremental implementation: TBA enables companies to implement AI solutions step-by-step, managing risks effectively and learning from early implementations to make informed decisions about broader AI integration later on.
Optimized resource allocation: By identifying specific tasks for automation, companies can allocate resources more efficiently, investing in AI where it will have the most impact. This targeted allocation of resources maximizes asset utility.
Targeted automation: TBA precisely identifies tasks most suitable for automation. This specificity ensures that automation efforts are directly aligned with areas yielding the highest return on investment, improving efficiency where it matters most.
Risk mitigation: Starting with low-level tasks allows companies to test and refine AI technologies on a small scale before wider deployment. This approach helps identify potential issues early, reducing the risk of costly mistakes in larger-scale implementations.
Enhanced employee experience: Automating mundane and repetitive tasks can significantly improve job satisfaction. Employees are freed from tedious tasks to focus on more engaging and value-added activities, leading to higher motivation and productivity.
