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Services

 

Interpret offers three services: Workshops, Sprints & Upskilling

 

Workshops

Workshops are short (often 1 day or less) and used by clients to orientate themselves to the potentials of adopting AI to become market leaders. Think of them as a tour of the possible.

 

Sprints

Sprints are the main unit of work for adopting AI. They can last anywhere from 1-10 weeks and are geared towards producing tangible outcomes in a relatively short space of time with high energy and focus.

Design Sprints are for clients who need help with better understanding or identifying their problem, especially in relation to application of AI. (See below).

Adoption Sprints are units of work trying to solve a well-defined problem by application of AI. However, there are several flavors depending upon execution constraints (often related to supply of talent).

Upskilling Programs

No enterprise can tackle AI without a dedication to upskilling part or possibly all of its workforce.

We specialize in the design of custom upskilling programs that are outcome focussed rather than knowledge-acquisition focussed.

If you're thinking of placing some of your employees on a selection of online courses -- don't. Nine times out of ten these approaches fails because of the same complaint: workers don't know how to apply the new skills to their day job.

We use design-thinking to analyze why, how, where and when to upskill using an approach that gets workers hands-on solving real problems alongside the idealized ones found in online courses.


Start here: Workshops

These help you get oriented to the world of design-driven AI and discover the potential for AI transformation of your enterprise.

Orientation Workshops

Objective: Tour of AI and Design - what lies at the intersection of AI and Design.
Duration: Typically 1 day
Audience: CxOs, execs, VPs, key stakeholders, decision makers
Method: Classroom venue (onsite or external).
Prerequisites: None.
Preparation: None.
Summary:

  1. A tour of the latest methods in AI and related fields, explained in layman’s terms and including demos.

  2. An outline of the latest techniques in design-thinking and design-driven innovation.

  3. A tour of the latest methods in innovation execution (e.g. labs, agile talent, scale-up acceleration etc.)

  4. Examples of design-driven AI, plus related modern technologies (e.g. blockchain).

Outcome: Client has a grasp of AI's potential and its relevance to growth problems and the future of business generally.

 

Discovery Workshops

Objective: Discovery of how your organization might use AI to achieve growth and/or market leadership.
Duration: Typically 1-2 days
Method: Classroom venue (onsite or external)
Prerequisites: Client should be conversant with some of AI's potential and ideally have a set of problems/applications in mind.
Preparation: Exchange of key business goals/parameters under NDA.
Summary:

  1. Review of methods in AI applicable to the client’s problem space.

  2. Review of applicable design-thinking strategies.

  3. Joint exploration of suitable method in innovation execution (e.g. labs, agile talent, outsourcing, scale-up acceleration etc.)

  4. Examples of possible applications of AI to the client’s problem space.

Outcome: Client has a good idea of why and how to proceed with application of AI to their specific business challenges. This includes a sufficient understanding of the prerequisities for embarking upon an AI adoption pathway, such as data readiness, data governance, tools, people skills etc.


AI Adoption Sprints

Sprints are the main unit of work for moving quickly along the AI Adoption Pipeline. They last anywhere between 1-10 weeks and are geared towards producing tangible outcomes in a relatively short space of time with high energy and focus.

We offer two types of sprints: design and adoption.

    Data-Driven Design Interpretation Sprint

    Objective: Find out what the problem really is, or might be, in the light of AI possibilities and realistic client constraints, including data readiness.
     

    Duration: Typically 3-5-day intensive session with key stakeholders, and then up to 10 weeks of related research and delivery with frequent "futurespective" drops and interpretations for joint review with the client.
     

    Method: We use our own variant of design-driven innovation via design thinking contextualized to the possibilities of AI and the client's current state of AI readiness, which includes data-readiness. Data-readiness assessments can be carried out separately and often constitute a "data readiness sprint."
     

    Prerequisites: Client should be conversant with some details of AI's potential (via Orientation Workshop or otherwise). Client should have a particular problem in mind.
     

    Preparation: Exchange of key business goals/parameters under NDA.
     

    Summary:

    1. Review of methods in AI and applicable technologies to the client’s specific problem space.

    2. Review of applicable design-thinking strategies.

    3. Joint exploration of suitable method in innovation execution (e.g. labs, agile talent, scale-up acceleration etc.)

    4. Examples of possible applications of AI to the client’s problem space.

    Outcome: Client has a workable idea of why and how to proceed with AI to tackle a specific growth or business transformation challenges.


    AI Adoption Sprints (3 execution types)

     

    Objective: To traverse any part of the AI Adoption Pipeline as identified in a prior design sprint or via consultation with the client. Note that several sprints might be necessary depending upon the client's current situation, especially with regards to their data maturity and available resources.


    Duration: Typically a 3-5-day intensive futurespective kick-off with key stakeholders and then up to 10 weeks of agile delivery with weekly retrospectives. This sprint cycle is repeated every 10 weeks as needed for handover to the client or to a 3rd party solution provider.
     

    Prerequisites: Adoption sprints can only begin once a design and product goal has been clearly established and interpreted via Interpret's AI Adoption framework. To ensure success, key stakeholders must support the sprint without reservation.
     

    Preparation: A fully defined futurespective of the project including a plausible design interpretation of AI's potential to solve the business challenge. If the client has not engaged with a full Design Sprint but has a clear idea of the problem, then typically we propose step one of the Design Sprint process first (3-5 days).

    Method of execution: We recognize that available AI talent is a major bottleneck as is confusion with AI vendors and tool selection. We therefore work to identify the best method of execution from the following three options:

    1. AI adoption via judicious use of curated partners: start-ups, scale-ups, established vendors.
    2. Adoption via reliance upon in-house agile innovation team (i.e. "labs")
    3. Adoption via use of "agile talent platforms" - i.e. highly qualified remote workers
    4. Did we say three? We meant four, which is all of the above!

    Our in-house talent includes AI engineers, designers and data scientists. But the role of Interpret is to act as navigator of the pipeline rather than hands-on do-ers unless this is specifically called for. Our goal is to make your enterprise as self-sufficient in AI as possible as soon as possible. We are about enterprise transformation, not point solutions.


    Execution Methods for Adoption Sprints

    For more details of the specific execution methods, see below.

    Partner Method

    Summary: We set about facilitating AI adoption mostly via the use of suitably curated partners. We take much of the pain out of selecting a suitable partner in the rapidly emerging world of AI solutions.

    1. Identification of suitable start-up or scale-up partners with a pre-qualified AI solution.

    2. Short-listing of applicable partners.

    3. Grooming of short-listed partners.

    4. Partner selection

    5. Partner orientation to design goals.

    6. Co-authoring of execution plan

    Outcome: Client has a clear AI execution plan in place with a viable partner. Minimal Viable Product goals clearly understood and executable by partnership.

    Subsequent sprints are execution sprints via an agreed framework of deliverables per the execution plan.


    In-House Labs Method

    A lab means a dedicated agile "AI innovation" team set aside to work on innovation projects separate from the corporation's main roadmap. Note that this method can also be used in conjunction with method 1 in a number of ways. This is often to ensure that the client gains new internal skills and modern tooling during the outsource process. It can also be used to create “adversarial” strategies whereby internal teams compete (with each other or external vendors) to find the best solution. within constraints set out in the design phase.
     

    Summary:

    1. Labs orientation and methodology design/review with key client stakeholders.

    2. Resourcing, training and culture strategy.

    3. Tools selection.

    4. Execution plan for ramp-up.

    5. Ramp-up to first execution sprint readiness.

    Outcome: Client has a clear plan in place with a realistic labs execution pathway with skeletal labs team in place. Minimal Viable Product goals clearly understood and executable by lab


    Agile Talent Method

    This method is usually an augmentation of methods 1 and 2. It implies the use of agile talent platforms (highly skilled on-demand remote workers) per the Boundaryless Innovation principles pioneered by ID. It is sometimes the case that a start-up (method 1) has its own shortfall of talent that can be plugged by agile talent augmentation. This offers various advantages to the client, such as increased knowledge transfer and better readiness for future hand-over and scaling of the start-up’s solution.
     

    Summary:

    1. Review and predict key talent issues for  methods 1 and 2.

    2. Review of applicable agile talent strategies and platforms.

    3. Agile talent execution plan.

    Outcome: Client has a good idea of why and how to use agile talent to augment the growth plan.